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This section provides an in-depth exploration of the central nervous system, encompassing both the brain and the spinal cord, which collectively oversee the body's voluntary functions, including cognition and action. It delves into the structural intricacies of the brain, elucidating its various regions and their respective functions. Additionally, the discussion emphasizes the concept of localization and interconnections, underscoring how different brain regions govern distinct bodily functions. Furthermore, the section highlights the significance of brain research, detailing methodologies such as MRI, X-Ray, and Cat Scan employed to advance our understanding of neural processes.

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This section delves into the fascinating processes of the brain during learning, starting with the initial stages of information processing. It explains how sensory registers momentarily hold incoming stimuli, highlighting the brain's selective attention mechanism, which filters out irrelevant information to focus on the task at hand. Additionally, the discussion delves into memory networks, illustrating how repeated exposure to information strengthens memory pathways over time. Hebbs' theory further elucidates this process, emphasizing the role of synaptic connections and network formation in shaping learning and memory. Together, these insights offer a comprehensive understanding of how the brain processes and retains information, informing teaching practices to optimize student learning experiences.

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Brain development is a complex interplay of various factors, ranging from genetic predispositions to environmental influences. Genetics provide the blueprint for brain development, dictating its size, structure, and neural connectivity. However, environmental stimulation is equally crucial, as the brain requires external input to foster growth and organization. Adequate nutrition is essential for optimal brain development, as deficiencies can have profound impacts. Additionally, steroids play a role in sexual development and stress responses, with excessive stress potentially leading to neuronal damage. The presence of teratogens poses another risk, as these foreign substances can induce abnormalities in embryonic or fetal development. Understanding the phases of development and sensitive periods is key, as these periods shape cognitive and emotional growth. Language development also plays a significant role, while the influence of technology introduces new considerations for brain development in the modern age. Overall, recognizing these multifaceted influences is essential for promoting healthy brain development from infancy through adulthood.

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Motivation and emotion play intricate roles in human psychology, intertwining to influence behavior, cognition, and subjective experiences. Described as non-cognitive emotions, they transcend mere cognitive processes, shaping individuals' actions and perceptions. Motivation, defined as the instigation and sustenance of goal-directed activities, drives individuals towards specific objectives, manifesting in observable changes in behavior—whether in rate, intensity, or duration. These motivational states are not isolated phenomena but intricate neural connections encompassing emotions, cognitions, and behaviors, illustrating the complexity of human motivation. Emotions, in turn, are transient and intense internal experiences, encompassing feelings and internal events that significantly impact human psychology and behavior. Together, motivation and emotion form a dynamic interplay that underpins human motivation, cognition, and affective experiences.

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Educational researchers increasingly recognize the significance of understanding brain processes in elucidating the complexities of learning and development. This acknowledgment is accompanied by a critical examination of prevalent brain myths, debunking misconceptions such as the notion that the majority of learning occurs before the age of 3 or the existence of critical periods for learning. Additionally, misconceptions regarding brain capacity, gender differences in brain function, and the notion of being "right-brained" or "left-brained" are challenged. Amidst these discussions, various educational issues come to the forefront, prompting educators to explore brain-based educational practices that align with neuroscience findings. These practices encompass diverse approaches such as problem-based learning, simulations, and role-playing, which engage learners in experiential and hands-on activities. Furthermore, active discussions, the use of graphics, and fostering a positive classroom climate are integral components of brain-based educational practices aimed at optimizing learning outcomes and promoting holistic development.

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1. Mindfulness practices: Engaging in mindfulness meditation and yoga can improve mind-body awareness, reduce stress, and enhance overall well-being by fostering connections between the brain and body.

2. Physical exercise: Regular physical activity, such as aerobic exercise, strength training, and coordination exercises, promotes neuroplasticity and stimulates the release of neurotransmitters that support cognitive function and mood regulation.

3. Brain-training activities: Cognitive exercises, puzzles, and games like Sudoku, crossword puzzles, and memory games challenge the brain and promote neural connections, improving cognitive abilities and mental agility.

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https://scholars.direct/Articles/cognitive-science/acs-4-017.php?jid=cognitive-science

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The central nervous system (CNS) is comprised of the brain and spinal cord. The CNS receives sensory information from the nervous system and controls the body's responses. The CNS is differentiated from the peripheral nervous system, which involves all of the nerves outside of the brain and spinal cord that carry messages to the CNS.

The central nervous system plays a primary role in receiving information from various areas of the body and then coordinating this activity to produce the body's responses.﻿﻿

The Structure of the Central Nervous System

The CNS has three main components: the brain, the spinal cord, and the neurons (or nerve cells).

The brain controls many of the body's functions including sensation, thought, movement, awareness, and memory. The surface of the brain is known as the cerebral cortex. The surface of the cortex appears bumpy thanks to the grooves and folds of the tissue. Each groove is known as a sulcus, while each bump is known as a gyrus.

The largest part of the brain is known as the cerebrum and is responsible for things such as memory, speech, voluntary behaviors, and thought.﻿﻿

The cerebrum is divided into two hemispheres, a right hemisphere, and a left hemisphere. The brain's right hemisphere controls movements on the body's left side, while the left hemisphere controls movements on the body's right side. While some functions do tend to be lateralized, researchers have found that there are not "left brained" or "right brained" thinkers, as the old myth implies.﻿﻿ Both sides of the brain work together to produce various functions.

Each hemisphere of the brain is then divided into four interconnected lobes:

The frontal lobes are associated with higher cognition, voluntary movements, and language.

The parietal lobes are associated with processing sensory information.

The temporal lobes are associated with hearing and interpreting sounds as well as the formation of memories.

The occipital lobes are associated with visual processes.

The Spinal Cord

The spinal cord connects to the brain via the brain stem and then runs down through the spinal canal, located inside the vertebra. The spinal cord carries information from various parts of the body to and from the brain. In the case of some reflex movements, responses are controlled by spinal pathways without involvement from the brain.

Neurons are the building blocks of the central nervous system. Billions of these nerve cells can be found throughout the body and communicate with one another to produces physical responses and actions.

Neurons are the body's information superhighway. An estimated 86 billion neurons can be found in the brain alone.﻿﻿

Protective Structures

Since the CNS is so important, it is protected by a number of structures. First, the entire CNS is enclosed in bone. The brain is protected by the skull, while the spinal cord is protected by the vertebra of the spinal column. The brain and spinal cord are both covered with a protective tissue known as meninges.

The entire CNS is also immersed in a substance known as cerebrospinal fluid, which forms a chemical environment to allow nerve fibers to transmit information effectively as well as offering yet another layer of protection from potential damage.﻿﻿

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Structure and Function of the Central Nervous System

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The central nervous system (CNS) is comprised of the brain and spinal cord. The CNS receives sensory information from the nervous system and controls the body's responses. The CNS is differentiated from the peripheral nervous system, which involves all of the nerves outside of the brain and spinal cord that carry messages to the CNS.

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1. Active learning: Encourage active engagement and participation in learning activities, such as discussions, problem-solving tasks, and hands-on experiments. When students actively manipulate and interact with information, they stimulate neural pathways and reinforce connections between concepts.

2. Spaced repetition: Incorporate spaced repetition into teaching practices by revisiting and reviewing key concepts over spaced intervals. This technique capitalizes on Hebbian principles by repeatedly activating neural pathways associated with learned information, enhancing long-term memory retention.

3. Multisensory instruction: Engage multiple sensory modalities, including visual, auditory, and kinesthetic, to deliver instructional content. By presenting information through diverse sensory channels, educators can activate multiple neural pathways simultaneously, facilitating deeper encoding and stronger memory formation.

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https://thedecisionlab.com/reference-guide/neuroscience/hebbian-learning

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Abstract

Trettenbrein ( 2016) has argued that the concept of the synapse as the locus of memory is outdated and has made six critiques of this concept. In this article, we examine these six critiques and suggest that the current theories of the neurobiology of memory and the empirical data indicate that synaptic activation is the first step in a chain of cellular and biochemical events that lead to memories formed in cell assemblies and neural networks that rely on synaptic modification for their formation. These neural networks and their modified synaptic connections can account for the cognitive basis of learning and memory and for memory deterioration in neurological disorders. We first discuss Hebb's ( 1949) theory that synaptic change and the formation of cell assemblies and phase sequences can link neurophysiology to cognitive processes. We then examine each of Trettenbrein's ( 2016) critiques of the synaptic theory in light of Hebb's theories and recent empirical data. We examine the biochemical basis of memory formation and the necessity of synaptic modification to form the neural networks underlying learning and memory. We then examine the use of Hebb's theories of synaptic change and cell assemblies for integrating neurophysiological and cognitive conceptions of learning and memory. We conclude with an examination of the applications of the Hebb synapse and cell assembly theories to the study of the neuroscience of learning and memory, the development of computational models of memory and the construction of "intelligent" robots. We conclude that the synaptic theory of memory has not met its demise, but is essential to our understanding of the neural basis of memory, which has two components: synaptic plasticity and intrinsic plasticity.

Keywords: Hebb, memory, synaptic theory, molecular mechanisms, epigenetics, neurological disorders

Introduction

When Pavlov first discovered classical conditioning, he was faced with the problem of how to interpret the behavior of his dogs. Some of his students and colleagues tried to interpret their behavior in terms of the subjective, introspective psychology of the day (Windholz, 1995). Pavlov, however, was determined to use an objective physiological interpretation and rejected subjective psychological terms. Once Pavlov's lectures were translated into English (Pavlov, 1927, 1928a, b), Guthrie ( 1930) and Lashley ( 1930, 1931) published critiques which pointed out that Pavlov's theories of brain and behavior ignored psychological concepts, such as attention and intelligence. Pavlov did not take these critiques lightly and wrote a lengthy reply (Pavlov, 1932). The Pavlov-Lashley debates regarding the neural basis of memory continued long after the death of Pavlov in 1936 (Lashley and Wade, 1946) and it was not until Hebb ( 1949) developed his neuropsychological theory that Pavlov's physiological approach was integrated with the psychological concepts of perception and attention in the study of learning and memory.

Neurophysiological theories of synaptic plasticity, based on the experimental phenomena of long-term potentiation (LTP) and long-term depression (LTD), are now used to explain the neural basis of learning and memory (Nabavi et al., 2014; Yang et al., 2016). However, Trettenbrein ( 2016) has called into question the theory that synaptic change provides the neural basis for learning and memory, stating that: "Tentative evidence from a wide variety of work in neuroscience seems to provide support for the idea that the synapse is an ill fit when looking for the brain's basic memory mechanism." In this article, we examine Trettenbrein's ( 2016) critiques of the synaptic theory of learning and memory and show that both theoretical and empirical research supports the concept that synaptic plasticity is an essential part of the complex cellular and molecular neurobiological changes which form the neural basis of learning and memory.

Trettenbrein's Critiques of the Neurophysiologic Explanation of Learning and Memory

Trettenbrein's critique is that the synapse-centered view of learning and memory is ill-focused and that the neurobiological basis of learning and memory is still far from understood. There are six critiques of the synaptic plasticity theory of memory in Trettenbrein's article, which will be addressed here: (1) the synapse may not be the sole locus of learning and memory; (2) a synaptic locus of memory does not fit well with philosophical and cognitive theories of learning and memory; (3) memories survive despite synapse destruction and synaptic and (or) protein turn-over; (4) evidence from spatial training suggests that there is a need to separate learning from memory; (5) existing learning mechanisms cannot explain information that is encoded in a single trial (Gallistel and Balsam, 2014); and (6) memory may be sub-cellular in nature. Trettenbrein ( 2016) makes the argument that the concept of the synapse as the locus of memory is not tenable and that a paradigm shift is necessary. However, he does not provide a new paradigm, except to suggest that "the memory mechanism is (sub-) molecular in nature" (page 3). The purpose of our article is to argue that there is a considerable literature on the neurobiology of learning and memory that shows the importance of synaptic plasticity as the first step in the chain of cellular and biochemical events involved in memory formation, and that, once memories are formed, synaptic modification is essential for their expression. We couch our discussion in terms of Hebb's ( 1949) neuropsychological theory.

Donald O. Hebb's Theory of Learning and Memory

Hebb's ( 1949) theory postulated that the neurophysiological changes underlying learning and memory occur in three stages: (1) synaptic changes; (2) formation of a "cell assembly"; and (3) formation of a "phase sequence," which link the neurophysiological changes underlying learning and memory as studied by physiologists to the study of thought and "mind" as conceived by cognitive psychologists. As pointed out by Trettenbrein ( 2016), Hebb's neurophysiological postulate (Hebb, 1949, page 62) states that:

"When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased."

The "cell assembly" (Hebb, 1949, pages 69-74) is a set of neurons and the pathways connecting them, which act together, such that a stimulus activating pathway 1 will activate a reverberating circuit of N pathways (in Hebb's example, n = 15). A cell assembly is a hypothetical reverberating system, proposed as a mediating process, an element of thought, capable of holding an excitation and thus of bridging a gap in time between stimulus and response (Hebb, 1972, pages 295 and 304). A series of cell assemblies, connected by neural activity over time is a "Phase Sequence," which provides the neural basis for a "train of thought" from one cell assembly to another (Hebb, 1949, pages 79-106). The cell assembly thus "relates the individual nerve cell to psychological phenomenon" such that "a bridge has been thrown across the great gap between the details of neurophysiology and the molar conceptions of psychology" (Hebb, 1949, page 101). Hebb then elaborated on how this theory could account for learning and memory, how new learning could be associated with previous learning, and how "quick learning" (perhaps similar to the single trial learning of Gallistel and Balsam ( 2014)) might occur (Hebb, 1949, chapter 8). Hebb's cell assembly theory thus showed how differences between psychologists and physiologists, who often use different definitions for the same phenomena, could be reconciled into a theory of the neurophysiological basis of learning and memory. It is important to note that Hebb's postulate quoted above contains two concepts: synaptic plasticity and "some growth process or metabolic change" in the neuron, which has been termed "intrinsic plasticity" (Sehgal et al., 2013; Titley et al., 2017).

Resolution of Recent Critiques Using Modern Neurophysiological Research

We believe that the critique of synaptic plasticity theory proposed by Trettenbrein ( 2016) can be resolved using Hebb's synaptic theory, research based on cell assemblies as components of neural networks, and current research on the cellular and molecular basis of memory formation to indicate the essential nature of synaptic plasticity in understanding the neurobiology of learning and memory. The six critiques proposed by Trettenbrein ( 2016) will be addressed sequentially.

The Synapse May Not be the Sole Locus of Learning and Memory

Trettenbrein ( 2016) describes the reservations held by some cognitive neuroscientists that the synapse is the "sole" locus of memory. While the synapse is an essential and highly studied component in the learning and memory process, it is not viewed by neurophysiologists as the sole locus of memory nor are its changes viewed as the sole basis of memory (Josselyn et al., 2015; Lisman et al., 2018). Memory is not represented by change at a single synapse, but by a series of processes involving molecular, biochemical, cellular and circuit level changes in widespread constellations of neurons throughout the brain. Specifically, when a strong stimulus occurs in the external environment, it drives high frequency stimulus trains (tetanic activity) in the neurons of a particular cell assembly, which, through their simultaneous synaptic activity, represent particular elements of the external stimulus (Buzsáki, 2010). Such a cell assembly or neural network has been identified for fear memory (Butler et al., 2015, 2018).

Propagation of strong excitatory currents through the synapses activate biochemical changes within neurons that lead to the strengthening of the synaptic connections within the circuit, or cell assembly. More precisely, N-methyl-D-aspartate receptor (NMDAR) induced calcium transients initiate intracellular signaling cascades leading to up-regulation of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs), synaptic growth and phosphorylation mediated increases in AMPAR conductance at the post-synaptic compartment of synapses between cells participating in a given network (Lee et al., 2003; Bailey and Kandel, 2008; Henley and Wilkinson, 2013). Both pre-synaptic and post-synaptic changes are important in the strengthening of synapses between neurons comprising cell assemblies (Costa et al., 2017). Subsequent activity in the neurons of this cell assembly, with the connections between cells now demonstrating an increased synaptic efficacy, represents the elementary building blocks of learning, memory and other cognitive processes (Choi et al., 2018). Many of these cell assemblies firing consecutively form a phase sequence which connects the individual elements to produce a more complete mental representation, or train of thought (Almeida-Filho et al., 2014). Once the synaptic connections in the cell assemblies forming a phase sequence are modified by experience, subsequent activation of this specific array of neurons results in the experiential recollection of the memory stored in the constellation of modified synaptic connections (Nabavi et al., 2014; Josselyn et al., 2015; Butler et al., 2018).

Synaptic change is thus the first step in a series of events which link molecular activity at the synapse and the subsequent intracellular biochemical cascades and cellular changes to the cognitive aspects of memory. The neurobiological basis of memory exists as a series of synapse-specific molecular and biochemical changes, including de novo protein synthesis, phosphorylation, up-regulation of synaptic receptors and synaptic growth within and between cell assemblies which results in long-term changes to synaptic efficacy (Lee et al., 2003; Bailey and Kandel, 2008; Henley and Wilkinson, 2013; Jarome and Helmstetter, 2014). The synapse is the location where these biochemical changes are initially manifested and is altered through the intracellular changes and, therefore, is an integral part of the neurobiology of memory (Mayford et al., 2012). Any memories stored in the neurons of the cell assembly or phase sequence are expressed through synaptic modifications (Caroni et al., 2014; Khalaf and Gräff, 2016; Knoblauch and Sommer, 2016). Thus, the synapse is not the sole locus of memory nor is it the neurobiological basis of memory, but it is an integral component of the memory process. The locus of memory is the particular set of neurons comprising a cell assembly or phase sequence connected by synapses which are activated and modified by experience. Memory formation is the result of both synaptic plasticity and intracellular (intrinsic) plasticity (Titley et al., 2017; Lisman et al., 2018).

A Synaptic Locus of Memory Does Not Fit Well With Philosophical and Cognitive Theories of Learning and Memory

Memories Survive Despite Synapse Destruction and Synaptic/Protein Turn-Over

Individual neurons are, as a consequence of their inputs, endowed with mnemonic receptive fields representing "bits" of information. Temporally contiguous or contingent stimuli cause coincidently active neurons (a particular constellation of which possess the mnemonic fields configuring to represent the stimulus) to have their connections strengthened via mechanisms of Hebbian spike-timing-dependent synaptic plasticity. Although numerous intracellular processes occur upstream it is this downstream, experience-dependent synaptic re-weighting which is ultimately responsible for the redirecting of information flow linking neurons, and thus the bits of information represented by their mnemonic fields, together into concepts (Hebbian cell assemblies) and associations (phase sequences). Thus, memory expression is an emergent network property resulting from distributed neural activity and destruction of a single synapse may weaken a memory, but does not cause the loss of an entire memory. Learning can produce persistent, sometimes even trans-generational (Lim and Brunet, 2013; Dias et al., 2015), epigenetic modifications as part of the memory formation process and these epigenetic marks may provide the basis of the intrinsic plasticity which allows memories to persist, in some capacity, following synapse destruction (Rajasethupathy et al., 2012; Khalaf and Gräff, 2016; Poo et al., 2016).

Synapses regularly turn over as do the proteins that strengthen these synapses during learning. How then can memories persist for years or even a lifetime, in the face of regular synapse and protein turnover? There are two, non-mutually exclusive, explanations for this. First, as discussed above, memory does not exist as a single modified synapse, therefore even the recycling and regular removal of certain connections allows a great many of the synapses in the cell assembly representing a memory to remain, allowing the memory, even if weakened, to persist. Attardo et al. ( 2015) found that dendritic spines were turned over on time scales comparable with those of systems level memory consolidation. Thus, the transience of individual dendritic spines in the hippocampus may serve to ensure this rapid acquisition system remains labile to encode future experience, and that information is not redundantly represented in the brain, once information has been shuttled to the neocortex. Neocortical dendritic spines have a heightened permanency relative to those in the hippocampus (Yang et al., 2009), suggesting that spine turnover is not a hindrance to the synaptic theory of memory. The second explanation focuses on the persistence of memory in the face of rapid protein turnover. Strong stimulation of a synapse (for example, by late-phase LTP, L-LTP) leads to the synthesis of an atypical protein kinase C isoform known as protein kinase M Zeta (PKMZ; Neves et al., 2008; Sacktor, 2008). PKMZ has been called the "memory molecule" as it maintains the molecular and biochemical alterations at the synapse laid down during learning, allowing the memory trace to persist despite continued protein turnover (Glanzman, 2013), providing the basis of Hebb's "reverberating circuit." For example, AMPAR up-regulation, a common synaptic modification during memory formation which promotes increased synaptic efficacy, is short-lived and is readily susceptible to down-regulation by endocytic internalization. PKMZ both prevents AMPAR endocytosis (Yao et al., 2008; Hardt et al., 2014) and ensures that AMPAR up-regulation continues, maintaining the elevated synaptic efficacy produced by prior learning (Migues et al., 2010). This allows the memory which persists, in part, through sustained biochemical changes at the synapse, to be carried forward in time for durations exceeding the turnover of individual macromolecules.

But what happens when PKMZ, itself a protein susceptible to degradation, is broken down? The answer to this is what makes PKMZ a candidate for a "memory molecule." PKMZ activity is self-sustaining since a continual positive feedback loop exists between the kinase activity and de novo PKMZ synthesis (Kwapis and Helmstetter, 2014). Once up-regulated, PKMZ molecules are autonomous and constitutively active owing to the activity of a catalytic domain and the lack of dependence on second messengers or auto-inhibition due to the absence of regulatory domains (Hernandez et al., 2003). In addition to its synaptic role PKMZ also acts in the nucleus to promote memory maintenance through epigenetic and transcriptional regulation (Ko et al., 2016). Mechanistically, the intracellular cascades activated during intense learning (LTP) drive the translocation of molecules, such as cyclic adenosine mono-phosphate (cAMP)-dependent protein kinase A and CaMKII, to the nucleus where they increase cAMP responsive element binding protein (CREB)-regulated CRE-driven transcription (Kandel, 2012) as shown in Figure ​Figure1, 1 , producing mRNA's of memory-related proteins, including PKMZ (Lisman, 2017b). Although many of these PKMZ mRNAs bind RNA-binding proteins, such as staufen 1, and translocate to afferent neurites (Doyle and Kiebler, 2011), some are translated in free cytoplasmic ribosomes in the soma. These somatically translated PKMZ molecules then undergo importin-α mediated nuclear translocation where they phosphorylate CREB-binding protein (CBP). CBP regulates the accessibility to and transcription of proteins involved in synaptic potentiation and memory maintenance through its histone acetyltransferase and transcriptional co-activator activity, respectively (Ko et al., 2016). As long as PKMZ remains active and there is an absence of forces which terminate its activity (such as LTD), it will continue to sustain the biochemical changes at the synapse which serve as the neurobiological basis of memory, allowing the memory to persist for durations far exceeding the turnover of its component molecules. This mechanism may also underlie the stability of memories following brain remodeling (Blackiston et al., 2015). Additionally, memory persistence may be conferred, in part, by structural changes and growth at the synapse (Bailey et al., 2015), as structural changes last longer than many of the synaptic plasticity-related biochemical modifications (as discussed in "Synaptic Remodeling During Memory Formation" section). Thus, a synaptic theory of memory can account for the persistence of memories in spite of synaptic and protein turnover.

Evidence From Spatial Training Suggests That There Is a Need to Separate Learning From Memory

It is also difficult to separate learning from memory on theoretical grounds as learning and memory are inextricable parts of a continuum. Learning is the acquisition of new information or the modification of existing information, skills, etc., and that information must be represented in neural tissue, even if temporarily as occurs in sensory and working memory (Miller et al., 1960; Cowan, 2008; Bradley and Pearson, 2012). Synaptic plasticity provides a model for describing the continuity between learning and memory. The more incidental the learning (i.e., the less frequently the material being learned is rehearsed), the weaker and more transient the synaptic change by which that learning will be manifest, termed short-term potentiation (STP). On the other hand, more frequent or intense learning bouts will cause information to be stored in a series of persistent synaptic modifications, known as LTP. These stages (STP and LTP) are not discrete, binary states, but form a continuum (Volianskis and Jensen, 2003; Volianskis et al., 2013). Variability in learning intensity and frequency can cause activated synapses to become more or less potentiated, shifting memory strength and duration accordingly. This indicates that learning and memory are inextricable parts of a continuum, where the duration and strength of a memory is graded by the frequency and intensity of learning bouts and memory cannot occur without learning (Takeuchi et al., 2014), except in trans-generational epigenetic memories (Lim and Brunet, 2013; Dias et al., 2015). Therefore, it is not theoretically feasible to separate learning from memory as they are inextricable parts of a continuum, as discussed in "Existing Learning Mechanisms Cannot Explain Information That is Encoded in a Single Trial" and "Memory May Be Sub-Cellular in Nature" sections.

Existing Learning Mechanisms Cannot Explain Information That Is Encoded in a Single Trial

Traditional approaches to associative learning suggest that the number of trials and the CS-US interval are the primary determinants of learning (Hawkins et al., 2006), supporting Hebb ( 1949) principle that the repeated, simultaneous activation of a synapse leads to the strengthening of that synapse. Gallistel and Balsam ( 2014), cited in Trettenbrein ( 2016), suggest that it is physiologically difficult to explain how synaptic learning mechanisms can encode information after a single trial. However, one trial inhibitory avoidance conditioning induces LTP in the CA1 neurons of the hippocampus (Whitlock et al., 2006) and a single exposure to a novel environment can stimulate LTP and activate the immediate-early gene Arc in CA3 neurons of the hippocampus (Miyashita et al., 2009). Novel stimuli also stimulate the release of neuromodulatory transmitters such as dopamine and acetylcholine which contribute to the synaptic modifications underlying one trial learning (Takeuchi et al., 2016). Therefore, a theory which regards the synapse as integral to memory formation can address this criticism. Bissière et al. ( 2003) and Marowsky et al. ( 2005) have shown that dopamine, acting as a neuromodulator, facilitates Hebbian LTP in the lateral amygdala during aversive learning. Furthermore, Bush et al. ( 2010) describe the ability of beta-noradrenergic receptors (β-ARs) to modulate threat learning, perhaps through influencing STDP (Pawlak et al., 2010). Johansen et al. ( 2014) demonstrated that in learning following very few trials, Hebbian plasticity mechanisms alone were insufficient to produce plastic changes in the lateral amygdala. Learning successfully occurred, however, when these Hebbian plasticity mechanisms were paired with the co-activation of β-ARs, emphasizing the importance of neuromodulation in this form of learning. Theoretically, while a single small shock may produce STP (early E-LTP), a larger shock and the concomitant neuromodulation, will result in a more sustained potentiation (long L-LTP). In sum, neuromodulation and the activation of immediate early genes, working in concert with Hebbian plasticity, provide a mechanistic explanation for how instances of single trial learning are explained by a synaptic theory of memory.

Memory May Be Sub-cellular in Nature

Trettenbrein's ( 2016), critique of synaptic theory and in support of a submolecular basis for memory, quotes from Gallistel and King ( 2009, page 282) that "our skepticism rests most strongly on the fact that the synapse is a circuit-level structure, a structure that it takes two different neurons and a great many molecules to realize. It seems to us likely for a variety of reasons that the elementary unit in the memory mechanism will prove to be a molecular or sub-molecular structural unit." This statement contains two points, which need to be addressed independently. First, the fact that the synapse is a structure involving neuronal connections is not a burden to a synaptic theory of memory but an asset. Associative memories are formed through a rewiring of brain circuits (Fuster, 1997; Chau et al., 2014; Van Ooyen and Butz-Ostendorf, 2017) such that the collection of neurons (cell assemblies) representing stimulus "A" and those representing stimulus "B" are either connected by de novo synaptogenesis (Le Bé and Markram, 2006; Kwon and Sabatini, 2011); have existing synapses between these neurons potentiated; or a combination of both (Choi et al., 2018). Subsequent activity in "A" encoding neurons can then, through spreading activation, elicit activity in the neurons encoding "B" thus providing a neural link between the cell assemblies (a phase sequence) representing each of these two originally discrete stimuli (Anderson, 1983). As discussed in "Evidence From Spatial Training Suggests That There Is a Need to Separate Learning From Memory" section, learning and memory occur along a continuum, so the fact that synaptic encoding of a memory requires "a great many molecules to realize" is a benefit as it allows for the gradation of synaptic, and in turn memory, strength along a continuum in accordance with the salience of the information, or effortfulness of original learning (Liu et al., 2017). Furthermore, the biochemical and structural complexity of the glutamatergic synapse, actualized by these "great many molecules," allows for several distinct forms of plasticity: (i) STP; (ii) early or late LTP (E-LTP or L-LTP); (iii) LTD; (iv) synaptic scaling; and (v) distance-dependent scaling (Volianskis et al., 2013; Lisman, 2017a). This diversity helps to explain how various types, durations and strengths of memory can be represented in the brain. So, the nature of the synapse as a circuit-level structure and the number of molecules involved in producing synaptic change are not hindrances to a synaptic theory of memory but strengths.

Second, considerable evidence exists for the intracellular chain of biochemical events, which lead to memory formation. As described by Kandel (Kandel, 2001; Kandel et al., 2014) and others (Guzman-Karlsson et al., 2014), synaptic activity triggers intracellular second messengers such as cyclic AMP, protein kinases, MEK-ERK pathways, CAMKII, CREB and binding to DNA for transcription of genetic information to mRNA and translation of this information for new protein synthesis; an overview of elements common to synaptic potentiation pathways is included as Figure ​Figure1. 1 . These intracellular pathways modulate synaptic activity by inducing changes in the quantity (Fleming and England, 2010) and conformational status of AMPARs (Lee et al., 2000); the surface area of the post-synaptic density, and in turn the size of the axon-spine interface (Desmond and Levy, 1986, 1988); the volume of the dendritic spine head (Matsuzaki et al., 2004; Bozdagi et al., 2010); and the growth of new synapses (Kandel, 2001). In addition, retrograde signals, such as nitric oxide (Padamsey and Emptage, 2013), are generated post-synaptically and travel to the presynaptic compartment where they elicit alterations as part of the potentiation process. These presynaptic alterations act to alter the probability and latency of presynaptic neurotransmitter release, modulate the magnitude of quantal release (Larkman et al., 1992; Sola et al., 2004; Enoki et al., 2009), the size of the pool of readily available synaptic vesicles, the quantity and responsiveness of presynaptic calcium channels and the set point of presynaptic calcium sensors (Kaeser and Regehr, 2014; Costa et al., 2017). These presynaptic modifications can act bi-directionally, to potentiate or depress synaptic efficacy (Fioravante and Regehr, 2011; Regehr, 2012; Yang and Calakos, 2013). Finally, CREB-mediated intrinsic plasticity can be described as being submolecular and has recently been implicated in processes of memory allocation, linking and integration (Josselyn and Frankland, 2018; Lisman et al., 2018; Sehgal et al., 2018). Thus, the suggestion put forth in Trettenbrein ( 2016) that memory is submolecular in nature is correct, but incomplete.

An additional piece of evidence supporting a synaptic plasticity model of memory is that both memory and synaptic plasticity consist of at least two discrete temporal stages. Short-term memory (using the neurophysiological definition) lasts for ~1-2 h. E-LTP, also, lasts for ~1-2 h and results from temporary modifications of pre-existing proteins which are sustained for as long as kinase activity (e.g., CaMKII) exerts dominance over phosphatase activity. Once dominance has shifted towards phosphatase activity, the biochemical substrates underlying the memory are rapidly removed and the memory is lost (Huang, 1998; Genoux et al., 2002; Munton et al., 2004). Long-term memory duration varies but is generally considered to persist for longer than 2 h and can last for days, weeks, years or even a lifetime. L-LTP, not coincidentally, lasts for durations comparable to those described for long-term memory (Costa-Mattioli et al., 2008). The protein synthesis underlying L-LTP is responsible not only for generating a greater number of synaptic potentiation promoting proteins and structural components but also for generating the persistent kinases capable of allowing these long-term memories to be maintained for such impressive durations (Jalil et al., 2015). Thus, this temporal overlap serves as evidence that the synaptic change occurring during E-LTP serves as the neurobiological correlate of short-term memory formation while those synaptic changes involved in L-LTP represent the neurobiological correlate of long-term memory formation.

A final component of Trettenbrein ( 2016) and Gallistel and King's ( 2009) argument that memories might be submolecular is that, "neural computation is demonstrably incredibly fast, therefore making it much more likely that the memory mechanism is (sub-) molecular in nature so that computational machinery and memory can be located in close physical proximity in order to minimize the distance over which a signal has to be transmitted." If it is assumed that by "computation" the authors are referring to the ability of the organism to take in, process and utilize information, then indeed memory is fast, but this rapidity can be explained while maintaining adherence to a synaptic theory of memory. The intracellular signaling cascades, protein synthesis and morphological changes that represent memory in a synaptic theory are indeed slow to occur, but one need not wait for these changes in order to utilize the memory. Memory is initially encoded in the mnemonic fields of a particular constellation of prefrontal cortical neurons as a reverberatory pattern of activity (Lorente De Nó, 1933; Hebb, 1949; Funahashi et al., 1989; Wang, 2005; Lara and Wallis, 2015). These electrical reverberations induce synaptic E-LTP which lays down information as a series of temporary, transcription independent, kinase activity-dependent, biochemical alterations (Huang, 1998) capable of maintaining information in the brain's connectivity until the more permanent, transcription-dependent L-LTP-dependent processes can better establish this memory in the neural tissue (Vickers and Wyllie, 2007). Thus, a synaptic theory of memory ensures that the information is rapidly available for immediate computational usage, despite the temporal void that exists between the acquisition of this information and its later stabilization through the longer lasting de novo protein synthesis and morphological changes involved in consolidation. Once again, this is evidence that one need not stray from a synaptic theory in order to explain the complexities of memory.

In addition, the reactivation of stored memories causes them to become transiently deconsolidated and labile, providing an opportunity to amend preexisting memories with new information. These retrieved memories are then reconsolidated, leading to the confounding of original memories with new information and the storing of false memories (McKenzie and Eichenbaum, 2011; Ramirez et al., 2013; Almeida-Correa and Amaral, 2014). When a memory is explicitly retrieved, it is done so by driving reverberatory activity through the experientially modified synapses in the cell assemblies and phase sequences within which the memory is represented. This subjects it to the operations of working memory where it is Fragile and susceptible to alteration (Lee et al., 2017). The activated synapses produce intracellular biochemical changes and protein synthesis, as discussed in "Synaptic Remodeling During Memory Formation" section, which remodel the synapse in a manner consistent with the pattern of activity experienced at the synapse (i.e., according to the principles of STDP; Lee et al., 2011). The remodeling of the synapses in the cell assemblies and phase sequences underlying a memory can incorporate new information, or change the existing information, comprising the retrieved memory (Bonin and De Koninck, 2015; Hu and Schacher, 2015; Kastner et al., 2016). Thus, the neurophysiological basis of reconsolidation provides support for a synaptic theory of memory.

Synaptic Remodeling During Memory Formation

As stated by Hebb ( 1949), "some growth process or metabolic change" takes place to increase the efficacy of cell A on the firing of cell B. The activation of intracellular biochemical pathways, gene expression, epigenetic factors, and protein synthesis leads to the remodeling (strengthening or weakening) of synapses in cell assemblies. There are many different types of growth processes and/or metabolic changes that lead to changes in synaptic strength, including: (1) excitatory; (2) inhibitory; (3) size of the synapse; (4) ion channels; (5) dendrites, boutons, et cetera (De Roo et al., 2008; Caroni et al., 2014; Bailey et al., 2015; Yang et al., 2016; Choi et al., 2018). Distinct activity patterns drive specific intracellular biochemical signaling cascades which serve to adaptively remodel the synapse during activity-dependent synaptic plasticity (Colicos, 2009, pages 50-52). As an example, adaptive, biologically relevant information which needs to be remembered in order to optimally organize behavior and cognition at a later time point is likely to drive tetanic activity through a particular set of synapses, producing large post-synaptic calcium transients. Calcium then binds to calcium binding proteins, including calmodulin, which activates various effectors including nNOS, CaMKII, post-synaptic adenylyl cyclase (AC), etc. These effectors then activate downstream kinases, genes and other proteins capable of remodeling the pre- and post-synaptic elements in such a way to confer an increased synaptic efficacy (Wong et al., 1999; Kandel, 2012; Choi et al., 2018). These remodeling changes, including the growth of pre-existing and the formation of new dendritic spines are correlated in magnitude with the degree of training (Xu et al., 2009; Yang et al., 2009). Thus, intracellular metabolic changes function to modulate synaptic remodeling and growth processes during memory formation.

Reconciliation of the Differences Between Physiologists and Psychologists About the Role of Synaptic Plasticity in Learning and Memory

Our critique of Trettenbrein ( 2016) has focused on the term "demise" of the synaptic theory of memory. We believe that the synaptic theory of memory has not died but that there are two components of this theory: synaptic plasticity and intra-cellular biochemical changes. At issue is whether "memory" consists of the synaptic changes activated by intracellular biochemical changes OR whether memory consists of the intracellular biochemical changes expressed via synaptic plasticity. Our argument is that memory, as conceived by Hebb, consists inseparably of both synaptic plasticity and "intrinsic plasticity" of the neurons (Sehgal et al., 2013; Titley et al., 2017; Lisman et al., 2018).

Synaptic Change and the Formation of Cell Assemblies Are Fundamental for Theories of Memory

Cell Assemblies Have Been Verified by Neuroimaging

Hebb's theories on the neurophysiological basis of learning and memory integrate synaptic neurophysiology with psychological concepts such as attention, perception, thought and mind-the concepts which Pavlov avoided in his objective approach to memory. Hebb's theory effectively integrated Pavlov's concepts of the physiology of learning with Lashley's ( 1932) criticism that Pavlov ignored psychological concepts. Neuroimaging studies have shown the usefulness of Hebb's ideas for understanding both the psychological and physiological mechanisms of memory. Memory processes have been shown by fMRI and other neuroimaging methods to be distributed across many cortical areas (Miyamoto et al., 2014). For example, Christophel et al. ( 2017) showed that different cortical neural networks are activated in different types of working memory, and O'Neil et al. ( 2012) found that different cortical regions were activated in recognition memory.

Hebb's Synaptic Learning Rule and Cell Assembly Theory Is Used in Computational Neuroscience and Robotics

Abnormalities in Synaptic Plasticity Underlie Cognitive and Motor Dysfunctions Pain Mechanisms and Drug Addiction

The activation of the network of synaptic connections in a cell assembly requires changes in synaptic strength to establish the connectivity of the neurons in the cell assembly. Cell assemblies are essentially a collection of activated synapses and the sufficiently strong activation of these synapses causes biochemical changes in the neurons of the cell assembly. Thus, biochemical changes and gene activation within the neurons of a cell assembly are required to maintain memories (Li et al., 2016). These involve complex interactions between excitatory and inhibitory synapses (see Barron et al., 2017). The biochemical changes in the neurons of a cell assembly that are activated by transient changes in synaptic activity involve epigenetic mechanisms including chromatin remodeling which drives changes in the transcription and translation of information in the DNA, protein synthesis and cellular changes underlying learning and memory formation (Vogel-Ciernia and Wood, 2014). As stated by Hebb in 1949, the synaptic changes following repeated stimulation at a synapse lead to "some growth process or metabolic change... in one or both cells such that A's efficiency, as one of the cells firing B, is increased." Much of the neuroscientific research on the cellular and molecular basis of memory in the last 70 years has been oriented to finding these growth processes and metabolic changes that underlie memory (Josselyn et al., 2015; Poo et al., 2016). Synaptic change is not limited to learning and memory, but forms the basis of neural changes in perception (Dan and Poo, 2006; Yang et al., 2011), pain (Luo et al., 2014) and drug addiction (Jones and Bonci, 2005; Kauer and Malenka, 2007; Lüscher, 2013).

Contrary Opinions

Summary and Conclusions

Trettenbrein's ( 2016) critiques of the synaptic theory of memory can be answered by the published evidence for the synaptic theory of memory, as argued in this article. The synaptic theory of memory remains the most empirically plausible explanation for the neurobiological basis of memory, even if it may need modification (Jirenhed et al., 2017). In this article we have addressed the six critiques raised by Trettenbrein ( 2016) and through the use of recent neurophysiologic literature, we have demonstrated that synaptic change is only the first step in formation of the cell assemblies and phase sequences postulated by Hebb ( 1949), which together constitute the Hebbian account of memory. We have shown how the concepts of synaptic change and the cell assembly are used to understand the neuroanatomical, cellular, molecular and genetic bases of memory and of neurological disorders. We have also shown how neuroimaging studies, computer modeling and robotics have used the Hebbian learning rules and cell assemblies to develop computer learning and "intelligent" robots. Our critique of Trettenbrein ( 2016) has focused on the term "demise" (death, downfall, disappearance or final fate) of the synaptic theory of memory. We believe that the synaptic theory of memory has not died, but has gone from strength to strength. But there are two components of this theory: synaptic plasticity and intra-cellular biochemical changes. At issue is whether "memory" consists of the synaptic changes activated by intracellular biochemical changes OR whether memory consists of the intracellular biochemical changes expressed via synaptic plasticity. Our argument is that memory, as conceived by Hebb, consists of both synaptic plasticity and "intrinsic plasticity" of the neurons (Sehgal et al., 2013; Titley et al., 2017; Lisman et al., 2018). You cannot separate one from the other.

Author Contributions

JL and RB wrote the article. RB edited and supervised the project.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Funding. This work was supported by grant number 7441 from the Natural Sciences and Engineering Research Council (NSERC) of Canada to RB.

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The Synaptic Theory of Memory: A Historical Survey and Reconciliation of Recent Opposition

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Trettenbrein () has argued that the concept of the synapse as the locus of memory is outdated and has made six critiques of this concept. In this article, we examine these six critiques and suggest that the current theories of the neurobiology of memory ...

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Abstract

Research over the past several decades has provided insight into the processes that govern early brain development and how those processes contribute to behavior. In the following article, we provide an overview of early brain development beginning with a summary of the prenatal period. We then turn to postnatal development and examine how brain functions are built and how experience mediates this process. Specifically, we discuss findings from research on speech and on face processing. The results of this research highlight how the first few years of life are a particularly important period of development of the brain.

The past 30 years of research have provided a new and deeper understanding of the brain and its role in psychological functions. In particular, researchers now have a better sense of how brain development affects the development of behavior. Measurement techniques such as electroencephalogram (EEG) and event related potentials (ERP) can be used to study infants, children, and adults, and this flexibility has allowed researchers to investigate a variety of developmental processes.

Research using these measures on the developing brain has clarified several arguments about the nature of child development and informed debates such as those surrounding the state of the infant's brain at birth (whether it is a "blank slate" or not), the identification of critical periods of development, and the relative importance of genes versus environment.

It is important to note that, although much of the research has been conducted on infants, it is a collaborative effort between infant and animal research that has uncovered the neurobiological principles that govern development in humans. Researchers have made use of the homology that exists among developing nervous systems of different species, and many of the cutting-edge ideas discussed in the developmental literature have their origins in animal research-but they have been tested and clarified in neurobehavioral experiments with infants and young children. In humans, researchers can investigate the neural correlates of behavior whereas in animals they can dig deeper into the mechanisms that drive the processes that these neural correlates reflect. To this end, much of human brain research in the past three decades has focused on the brain basis of behavior. A more recent a focus on experience has helped refine researchers' understanding of how developmental processes are fueled.

In the following paragraphs, we will examine some of the essential ideas that have helped researchers understand the development of the human brain in the early years of life. We begin with an overview of the stages involved in the anatomical development of the brain. Subsequently, we examine three topics that research in brain development has uncovered, clarified, and elaborated: how development is hierarchically structured, such that later development depends on early development; how experience in the first year of life modulates the plasticity of the brain; and how early deprivation has strong and lasting effects on the brain.

Early Stages of Brain Development

An account of brain development in the early years of childhood is only complete if we first examine the origins of this process during the prenatal months. Brain development is a protracted process that begins about 2 weeks after conception and continues into young adulthood 20 years later. Brain development that occurs during the prenatal months is largely under genetic control, although clearly the environment can play a role; for example, it is well known that the lack of nutrition (e.g., folic acid) and the presence of toxins (e.g., alcohol) can both deleteriously influence the developing brain. In contrast, much of brain development that occurs postnatally is experience-dependent and defined by gene-environment interactions. Below we provide brief descriptions of the anatomical changes that characterize the early stages of brain development.

Neurulation

About 2 weeks after conception, the developing embryo has organized itself into a three-layered, spherical structure. In one area of this sphere, the cells thicken to form what is called the neural plate. This plate then folds over onto itself, forming a tube that gradually closes first at the bottom and then at the top, much like a zipper. This creates the neural tube, the inner cells of which will lead to the formation of the central nervous system (brain and spinal cord) while the outer cells will give rise to the autonomic nervous system (nerves outside the brain and spinal cord).

Once the neural tube is closed, it becomes a three-vesicle structure and shortly thereafter a five-vesicle structure. The different regions of tissue around the ventricles will become distinct brain structures. The anterior portion of the tube will become the forebrain, which includes the cerebral hemispheres; the diencephalon (the thalamus and the hypothalamus); and the basal ganglia. The cells around the middle vesicle will become the midbrain, a structure that connects the diencephalon to the hindbrain. The rear-most portion of the tube will give rise to the hindbrain, which will consist of the medulla oblongata, the pons, and the cerebellum. Finally, the cells that remain will give rise to the spinal cord.

Proliferation

Once the general structure of the neural tube has been laid out, the cells that line the innermost part of the tube, called the ventricular zone, proliferate at a logarithmic rate. As these cells multiply, they form a second zone, the marginal zone, which will contain axons and dendrites. This proliferative stage continues for some time, with the consequence that the newborn brain will have many more neurons than the adult brain. The overproduction of neurons is eventually balanced by a process of apoptosis, or programmed cell death. Apoptosis is responsible for a decrease in the cell numbers to adult levels and is completely under genetic control.

Cell migration

After the cells are born, they travel to their final destinations. The cerebral cortex is composed of multilayered tissue several millimeters thick. It is formed by the movement of cells in an inside-out direction, beginning in the ventricular zone and migrating through the intermediate zone, with the cells eventually reaching their final destination on the outside of the developing brain. The earliest migrating cells occupy the deepest cortical layer, whereas the subsequent migrations pass through previously formed layers to form the outer layers. About 25 weeks after conception, all six layers of the cortex will have formed.

The inside-out pattern of migration described here is that of radial migration, which applies to about 70%-80% of migrating neurons, most of which are pyramidal neurons and glia. Pyramidal neurons are the large neurons in the cortex that are responsible for sending signals to different layers of the cortex and other parts of the brain. Glia are nonneuronal brain cells that are involved in the support of neuronal processes (such as producing myelin or removing debris, such as dead brain cells). In contrast, interneurons -relatively smaller neurons that are involved in communication between pyramidal cells within a particular layer of the cortex-follow a pattern of tangential migration.

Differentiation

Once a neuron has migrated to its target destination, it generally proceeds along one of two roads: It can differentiate into a mature neuron, complete with axons and dendrites, or it can be retracted through apoptosis. Current estimates suggest that the number of neurons that are retracted is between 40% and 60% (see Oppenheim & Johnson, 2003). The development of axons is facilitated by growth cones, small structures that form at the edge of an axon. The cellular processes that occur at the growth cone promote growth toward certain targets and away from others. Such processes are driven by molecular guidance cues as well as by anatomic structures at the tip of the growth cone.

Dendrite formation occurs by a slightly different process, one that is thought to be driven by genes controlling calcium-regulated transcription factors ( Aizawa et al., 2004). Early dendrites appear as thick strands with few spines (small protuberances) that extend from the cell body. As dendrites mature, the number and density of spines increases, which in turn increases the chances that a dendrite will make contact with a neighboring axon. Connections between dendrites and axons are the basis for synaptic connections between neurons, which, as we will describe below, is essential for brain function.

Synaptogenesis

A synapse is a point of contact between two brain cells, often two neurons and frequently a dendrite and an axon. The first synapses are generally observed by about the 23rd week of gestation ( Molliver, Kostovic, & Van der Loos, 1973), although the peak of production does not occur until some time in the first year of life. As is the case with neurons, massive overproduction of synapses is followed by a gradual reduction. This process of synapse reduction, or pruning, is highly dependent on experience and serves as the basis of much of the learning that occurs during the early years of life. It is important to note that the various structures of the brain reach their peak of synapse production at different points. In the visual cortex, for example, the peak is reached somewhere between the 4th and 8th postnatal month, but areas of the prefrontal cortex do not reach their peak until the 15th postnatal month. The difference in timing in peak synapse production is important because it affects the timing of the plasticity of these regions; the later the peak synapse production, the longer the region remains plastic.

Synapse pruning

The overproduction of synapses is followed by a pruning back of the unused and overabundance of synapses. Until the stage of synaptogenesis, the stages of brain development are largely gene driven. However, once the brain reaches the point where synapses are eliminated, the balance shifts; the process of pruning is largely experience driven. As with synapse production, the timing of synapse pruning is dependent on the area of the brain in which it occurs. In the parts of the cortex involved in visual and auditory perception, for example, pruning is complete between the 4th and 6th year of life. In contrast, pruning in areas involved in higher cognitive functions (such as inhibitory control and emotion regulation) continues through adolescence ( Huttenlocher & Dabholkar, 1997). The processes of overproduction of synapses and subsequent synaptic reduction are essential for the flexibility required for the adaptive capabilities of the developing mind. It allows the individual to respond to the unique environment in which he or she is born. Those pathways that are activated by the environment are strengthened while the ones that go unused are eliminated. In this way, the networks of neurons involved in the development of behavior are fine-tuned and modified as needed.

Myelination

The final process involved in the development of the brain is called myelination. In this process the axons of neurons are wrapped in fatty cells, which ultimately facilitates neuronal activity and communication because this insulation allows myelinated axons to transmit electrical signals faster than unmyelinated axons. The timing of myelination is dependent on the region of the brain in which it occurs. Regions of the brain in certain sensory and motor areas are myelinated earlier in a process that is complete around the preschool period. In contrast, regions involved in higher cognitive abilities, such as the prefrontal cortex, the process is not complete until adolescence or early adulthood (for recent reviews see Nelson, de Haan, & Thomas, 2006; Nelson & Jeste, 2008).

Summary

In general, brain development begins a few weeks after conception and is thought to be complete by early adulthood. The basic structure of the brain is laid down primarily during the prenatal period and early childhood, and the formation and refinement of neural networks continues over the long term. The brains' many functions do not develop at the same time nor do their developmental patterns follow the same time frame. Although basic sensation and perception systems are fully developed by the time children reach kindergarten age, other systems such as those involved in memory, decision making, and emotion continue to develop well into childhood. The foundations of many of these abilities, however, are constructed during the early years.

The principles of anatomical change described above are essential to the maturation and development of the brain. These processes are in turn responsible for the development of a vast repertoire of behaviors that characterizes the early years of life. In terms of motor development, both synaptic pruning and myelination are responsible for the improved precision and speed of coordinated movement. In addition, they are important in the development of cognitive skills. Improved perception of speech sounds and face recognition, for example, are likely the result of synaptic reorganization, a process that is dependent on experience.

Although development continues into early adult years, early childhood represents a period particularly important to development of a healthy brain. The foundations of sensory and perceptual systems that are critical to language, social behavior, and emotion are formed in the early years and are strongly influenced by experiences during this time. This is not to say that later development cannot affect these behaviors-on the contrary, experiences later in life are also very important to the function of the brain. However, experiences in the early years of childhood affect the development of brain architecture in a way that later experiences do not. In the following pages we will elaborate on how experience affects development between birth and 3 years of age.

Brain Beginnings: Constructing a Foundation for the Future

The development of the brain is a life-long process. Indeed, recent research suggests that the brain is capable of changing throughout the lifespan ( Crawford , Pesch, & von Noorden, 1996; Jones, 2000; Keuroghlian & Knudsen, 2007), although perhaps not in all ways (e.g., humans do not "learn" to see or hear better as they age). However, the changes that take place during the early years are particularly important because they are the bedrock of what comes after. Higher level functions are dependent on lower level functions, the evidence for which is primarily in the basic cognitive processes and sensory perceptual systems. When infants are born, their brains are prepared for certain types of experience. For example, as discussed below, infants' brains are tuned to the sounds of virtually all languages, but with experience, their brains become most tuned to their native language (see Kuhl, 2004, for discussion). This perceptual bias is the basis for learning language; the brain is partially tuned to be sensitive to language sounds but not so broadly tuned as to be sensitive to all possible sounds.

Subsequent language development builds on this initial sensitivity. Within the first year of life, infants learn to discriminate among sounds that are specific to the language they are exposed to in their particular environment. Before the time they are 6 months old, infants can discriminate among sounds of almost any language. Between 6 and 12 months, the brain begins to specialize in discriminating sounds of the native language and loses the ability to discriminate sounds in nonnative languages ( Kuhl, Tsao, & Liu, 2003). This narrowing of perceptual sensitivity is important because it is related to later language ability in that better discrimination of native language sounds predicts better language skills later in life ( Kuhl, 2004).

Sensitive Period: Plasticity Is Affected by Experience

The brain is much more sensitive to experience in the first few years of life than in later years. The plasticity of the brain underlies much of the learning that occurs during this period. In the language example in the previous section, we noted that infants are sensitive to most language sounds in the first half-year of life but during the second half they begin to specialize in their native tongue at the expense of the broad sensitivity to nonnative language sounds. The period of heightened sensitivity to language exposure is not, however, a critical period in the sense that infants can no longer learn the sounds of another language once it is over. In fact, 12-month-old infants given additional experience with speech sounds from a nonnative language continue to be able to discriminate among sounds ( Kuhl, Tsao, & Liu, 2003).

Similarly, in the domain of face processing, an index of development of visual perception important to social behavior, 6-month-olds, 9-month-olds, and adults are all equally capable of discriminating between two human faces, whereas 6-month-olds alone can discriminate between two monkey faces ( Pascalis, de Haan, & Nelson, 2002). However, 6-month-olds given 3 months of experience viewing a range of monkey faces retain the recognition ability at 9 months ( Pascalis et al., 2005). Thus, the plasticity that characterizes brain processes during this time suggests that although the brain is particularly sensitive to experiences that occur, experience-dependent change is not limited to this short window. The sensitive period is effectively extended by specific experience.

A similar phenomenon exists in visual acuity, which is demonstrated by the natural occurrence of cataracts, rather than the laboratory manipulations discussed above. Maurer, Lewis, Brent, and Levin (1999) reported that for infants who are born with cataracts, a few moments of visual experience after the cataracts have been removed and replaced with new lenses leads to substantial improvements in visual acuity. This effect is stronger the sooner after birth this corrective procedure takes place. The longer the cataracts are left untreated, however, the lower the effect of experience on the outcome.

As demonstrated above, both speech and faces are initially processed by a broadly tuned window that then narrows with experience, yet the window can remain broader if experience includes a wide range of inputs. These studies suggest that the early period of life is characterized by sensitive periods that are dependent on the pattern of input from the environment. In response to certain input, the networks become biased, and future modifications become more difficult.

Deprivation: Environmental Effects on Brain Structure and Function

The effects of experience go beyond the simple modulation of plasticity. In fact, experience shapes the structure of the brain, a finding that has been demonstrated by the Bucharest Early Intervention Project (BEIP). This ongoing longitudinal study has found that institutionalization at a young age leads to severe consequences in the development of both brain and behavior. The study is following three groups of children: an Institutionalized group, children who have lived virtually all their lives in an institutional setting in Bucharest, Romania; a Foster Care group, which includes children who were institutionalized at birth and then placed in foster care ( at a mean age of placement of 22 months); and a Never Institutionalized group, which includes children living with their biological families in the Bucharest region (for details see Zeanah et al., 2003). As discussed above, for healthy development of brain circuits, the individual needs to have healthy experiences; the lack of these may lead to the underspecification and miswiring of brain circuits. Children raised in institutional settings in Romania lack experiences that stimulate healthy growth and thus we would expect to see consequent "errors" in brain development giving rise to a range of problems. Indeed this is the case; the institutionalized children show patterns of physical and cognitive growth that are stunted and delayed, and they have very different patterns of brain activity when compared to children who have never been institutionalized ( Marshall, Fox, & the BEIP Core Group, 2004). In addition, the effect of timing of experience is also important in preventing and amelioriating the effects of deprivation: children who were placed in foster care before they were 2 years old show patterns of brain activity that are more similar to never-institutionalized children than do those placed in foster care after they turn 2 ( Marshall et al., 2008). The same general trends are also observed for IQ ( Nelson et al., 2007) and language ( Windsor et al., 2007). These results support the idea that the lack of good quality experience has detrimental effects on brain function and that once the child is older than 2 years these effects tend to be worse.

An important distinction that must be made here is between deprivation and enrichment. The studies described earlier took place in the context of deprivation, in comparison to a baseline norm in which certain needs of the child are met, not enrichment beyond the norm, and they clearly showed that a child deprived of a certain quality of experience will have abnormal brain development. These findings do not indicate, however, whether environments that provide more than the baseline norm will produce brain development that is in some way superior. So although the BEIP studies do suggest that a lack of good quality experience is detrimental, they do not provide evidence for the effects of enriched experience.

Conclusions

In this article we have attempted to illustrate how the developmental neurosciences can shed light on early childhood development. Prenatal development is largely driven by genetic processes, many of which are sensitive to the biochemical makeup of the mother's body but are under genetic regulation. In postnatal development, however, the environment plays a crucial role in fostering development, and the interactions between genetics and experiences account for most developmental outcomes. Brain research suggests that development is a hierarchical process of wiring the brain, in that higher level processes build on a foundation of lower level processes. For example, language development depends critically on sensory and perceptual development (e.g., discrimination of speech sounds). The types of stimuli infants and children are exposed to help shape the brain and behavior. Although the brain may come equipped with biases for certain perceptual information, such as for speech, language, or faces, it is the specific speech, language, and range of faces they are exposed to that drives subsequent development. Depriving young children of the kinds of experiences that are essential to later development-that is, the building blocks that create the scaffolding upon which development depends-leads to severe consequences in both brain structure and function. Studies of institutionalized children suggest that quality psychosocial experiences are necessary for the development of a healthy brain.

It is important to emphasize that the individual does not play a passive role in this process. By experience we do not mean events and circumstances that simply happen in an individual's life; rather, we define experience as the interaction between the individual and his or her environment. The individual is an agent that can shape his or her experience ( Scarr & McCartney, 1983). For example, a child who appears happy in response to a caregiver singing a song may elicit more singing. This child consequently may have more experience with songs, which could affect his or her language development and the brain processes that underlie it.

Much of brain research is descriptive and simply tells us how the brain contributes to the development of behavior that is typical of young children (e.g., language and face processing). However, some of this research has implications on the decisions we make for young children. Research on deprivation can be used to make the case that environments that adversely affect infants and young children need to be remedied before they have long lasting consequences on both brain and behavior. Intervening in adverse circumstances is more successful if it occurs before brain processes become entrenched and in turn harder to rewire.

Contributor Information

Adrienne L. Tierney, Harvard Graduate School of Education.

Charles A. Nelson, III, Children's Hospital Boston Harvard Medical School.

References

Kuhl PK, Tsao FM, Liu HM. Foreign-language experience in infancy: Effects of short-term exposure and social interaction on phonetic learning. Proceedings of the National Academy of Sciences. 2003;100:9096-9101. [[PMC free article] [PubMed] Google Scholar]

Marshall P, Reeb BC, Fox NA, the BEIP Core Group Effects of early intervention on EEG power and coherence in previously institutionalized children in Romania. Development and Psychopathology. 2008;20:861-880. [[PMC free article] [PubMed] Google Scholar]

Nelson CA, de Haan M, Thomas KM. Neural bases of cognitive development. In: Damon W, Lerner R, Kuhn D, Siegler R, editors. Handbook of Child Psychology. Vol. 2. John Wiley & Sons; New Jersey: 2006. pp. 3-57. [Google Scholar]

Nelson CA, Jeste S. Neurobiological perspectives on developmental psychopathology. In: Rutter M, Bishop D, Pine D, Scott S, Stevenson J, Taylor E, et al., editors. Textbook on Child and Adolescent Psychiatry. 5th ed Blackwell Publishing; London: 2008. pp. x-y. [Google Scholar]

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Brain Development and the Role of Experience in the Early Years

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Research over the past several decades has provided insight into the processes that govern early brain development and how those processes contribute to behavior. In the following article, we provide an overview of early brain development beginning with a summary of the prenatal period.

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Behavioral theory | Behavior | MCAT | Khan Academy

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1. Positive reinforcement: Utilize positive reinforcement by providing rewards or incentives to strengthen desired behaviors. Rewards can be tangible, such as praise, tokens, or privileges, or intangible, like verbal praise or acknowledgment.

2. Negative reinforcement: Employ negative reinforcement by removing aversive stimuli or consequences to increase the likelihood of desired behaviors. For example, removing a chore or assignment after a task is completed reinforces the behavior of completing tasks.

3. Punishment: Apply punishment when necessary to decrease undesirable behaviors. However, it's important to use punishment judiciously and appropriately, ensuring that it is consistent, immediate, and proportional to the behavior being addressed.

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1. Active learning: Engage students in active learning experiences that stimulate multiple regions of the brain. Incorporate hands-on activities, experiments, and problem-solving tasks to promote engagement, critical thinking, and long-term memory retention.

2. Multisensory instruction: Appeal to diverse learning modalities by incorporating visual, auditory, kinesthetic, and tactile elements into instruction. Use visual aids, demonstrations, music, movement, and manipulatives to enhance learning and reinforce neural connections.

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https://soeonline.american.edu/blog/brain-based-learning/

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- Dr. Bobbi Hansen

To non-educators, the term "brain-based learning" may sound redundant. Doesn't all learning involve our brains? Yes - but brain-based learning delves a little deeper into the science of how we learn in order to deliver more effective instruction.

This introduction to brain-based learning serves as an accompaniment to Dr. Bobbi Hansen's presentation, "Teaching with Superpowers: Instructing Through Brain-Informed Practices," delivered during a University of San Diego Master of Education town hall.
View the recorded presentation here. >>
What Is Brain-Based Learning? Key Principles Benefits of Brain-Based Learning 8 Brain-Based Teaching Strategies How to Build a Brain-Based Curriculum: 7 Ideas
What Is Brain-Based Learning?

Brain-based learning involves teachers creating conditions that increase student motivation, engagement and long-term retention by tapping into the natural ways the brain receives, processes and stores information. This approach can be applied to classroom teaching methods, lesson planning, curriculum design and any other educational engagement. As long as the planning and delivery is rooted in the latest research on the science of learning, it's considered brain-based instruction.

The research in question centers primarily around the brain's ability to change and reorganize itself to receive and retain new information in a process called neuroplasticity. New information and experiences can physically reshape the brain's neural pathways; the more we practice new skills or review new information, the stronger those pathways become and the better we retain the information or ability.

Our brain's neuroplasticity is influenced, both positively and negatively, by motivation, stress and challenge, as well as our emotional state as we're learning. Understanding how neuroplasticity works (especially in young, developing brains) can help curriculum designers and educators build lessons that are more likely to "stick." When students master new concepts or gain new skills in the classroom, teachers see this neuroplasticity in action. Perhaps Dr. David Sousa, an educational neuroscience consultant, puts it best: "Successful teachers are brain-changers!"

History

The theory of the left and right brain has been synonymous with learning styles since it was introduced in the 1960s. Neuropsychologist Roger W. Sperry posited that all humans are either right-brain or left-brain dominant, depending on how they think and receive information. More informed neurological research, however, has since disproven this theory; we now know that humans use each hemisphere of their brain for different tasks, and that neither is dominant over the other.

As neurological research and discovery gained traction in the 1990s, some researchers began to apply their findings to the classroom. In " Understanding a Brain-Based Approach to Learning and Teaching," Geoffrey Caine and Renate Nummela Caine proposed that educators leverage neuroscience to improve students' knowledge retention and performance. The Caines found that students had better comprehension and retention in a collaborative, hands-on, brain-based learning environment than those in a traditional lecture-based classroom. Scientifically speaking, active learning leads to the growth of dendrites - nerve cell extensions in the brain that influence how neurons collect and process information. This is good news for knowledge retention.

Brain-based learning has since gained significant traction in schools. As evidenced by texts like Neuroteach: Brain Science and the Future of Education (Rowman & Littlefield, 2016), there remains a continued insistence that all educators have a working knowledge of how the brain receives and retains information in order to be better teachers.

Key Principles of Brain-Based Learning

The overall goal of brain-based learning is not complicated, nor is it unattainable:
To create a learning environment and classroom strategy where all students can thrive.
To achieve this goal, educators need to break down their instruction (and their students' learning) into smaller components. In her town hall presentation to USD MEd students, Dr. Bobbi Hansen identifies four key principles of brain-based learning:

1. Experience: New experiences only reshape the brain when we are actively participating in those experiences.

2. Flow: Stress affects memory, so instructors need to facilitate a "flow state" in which students are comfortable, safe and focused.

3. "Sticky" learning: Utilize teaching methods that result in higher knowledge retention, such as learning by doing or student-led instruction.

4. Brain-informed teaching practices: Identify and implement the teaching strategies that will best support the current group of students.

The following are some specific classroom examples that incorporate these four key principles:
Health and wellness
Physical activity isn't only for the gym. When students are able to take stretch breaks, go for short walks or otherwise move their bodies during lessons, the more engaged they will be when they come back. Movement-based learning is more common in younger grades, but teachers should not count older students out - bringing some light physical engagement to classroom lessons can:

Energize and revitalize students (and teachers)

Improve students' attention spans

Help students receive and retain more information

Positivity
When students are in a positive emotional state, they are more willing to engage in lessons and classroom activities. Social validation from their peers and affirmations from their teacher can contribute to students' feelings of belonging.
Group work
When learning new material, it can be highly valuable to hear other students' reactions or experience how they take in new information. Group activities help students learn from one another and may even help them understand information better than they would have from their teachers' presentations.
Peer teaching
Educators may occasionally feel unprepared to teach certain course material but find that their comprehension grows as they begin to deliver their lessons. Teaching others is the most effective way to learn, and this applies to students as well. If a handful of students grasp a concept right away, they can then turn around and teach their peers, thereby strengthening their own understanding and delivering this new information in a way that resonates with their classmates.
Practice
Repetition, experimentation and productive failure are all more effective than simple memorization of facts or vocabulary. Students should have an opportunity to practice new skills and retrieve new knowledge before any major assessment.
Limiting lectures
Traditional lecture-based teaching has been proven not as effective as active learning. Making lessons more active rather than passive promotes students' knowledge retention.
Meaningful information
Academic concepts can often feel disconnected from students' actual lives. How many teachers have fielded the question, "But when am I ever going to need this?" Finding ways to connect course materials to the real world boosts student engagement and gives relevant meaning to abstract concepts.
Written and verbal information
Lessons and assignments that incorporate varied modes of delivery give students a better chance of logging information in their long term memory. This involves the instructor delivering both spoken and written lessons, and the students demonstrating their understanding through both written and verbal assignments.
Stimulation
Not all students learn the same way, but there are ways to capture their attention that generally garner a positive reaction. Incorporating movement, humor, games or current events that are of interest to their age group can increase students' participation, processing abilities and the likelihood that they will remember the course content.
Stress mitigation
In general, stress negatively impacts our brains' neuroplasticity, preventing us from retaining information. By and large, students learn and perform better in a classroom environment that is calm, safe, supportive and positive (and with a teacher who exhibits the same qualities).

Benefits of Brain-Based Learning

When information is presented in a way that accounts for the brain's ability to process it, it can vastly improve the learning experience. Brain-based learning supports:

Skills retention

Higher academic performance

Academic progression

Resiliency

Long-term memory

Brain health and function

Mood

Classroom cooperation

Motivation

A positive attitude

8 Brain-Based Teaching Strategies

As long as a lesson is built around the way students receive and retain information, brain-based learning can look like almost anything. Educators can utilize multiple instructional methods while following the principles of experience, flow, "sticky" learning and brain-based teaching strategies. These principles remain the same no matter the age of the student; it is up to the teacher and their creativity to build age-appropriate lessons rooted in a brain-based foundation.

Here are some high-level strategies teachers can use when building their brain-based lesson plans:

Combine written and verbal information to boost learning retention

Prioritize engagements that are hands-on, creative, useful and have an emotional impact

Favor lessons and activities that help students develop critical thinking skills over simple memorization

Model lessons and assignments on real-world challenges that students can relate to

Create lessons that teach students social and team-building skills in addition to course content

Build opportunities to practice course content into every lesson, or before moving on to the next unit

Incorporate movement or brain breaks into lessons, regardless of grade level

Ensure that neither lessons nor the classroom environment contribute to unnecessary stress or anxiety

How to Build a Brain-Based Curriculum: 7 Ideas

As mentioned above, a brain-based curriculum can look like anything, so long as lessons are built to encourage students' neuroplasticity and optimal knowledge retention. It's important to note that not all strategies work for all students, so rely on trial and error to see what works and what doesn't for your classroom.

Break learning into chunks.

This may seem obvious to many educators, but it bears repeating. Students learn best when they can digest new concepts bit by bit; break lessons down into bite-sized pieces that fall into introductory, practice and review segments before moving on to a new concept or topic.

Include "turn and talk" time in your lessons.

Take breaks during each lesson or unit for students to talk amongst themselves and solidify their understanding of the topic. This can fall into the "review" stage mentioned above. You can also task students with explaining a new concept to a parent or someone unfamiliar with it. After all, teaching is the best way to grasp a new skill or subject.

Not all "chunks" need to involve learning! Leave space in each lesson for movement or stretch breaks. You can even create lessons that get students moving while they learn - e.g. role playing a historical event or reading out loud "popcorn"-style with a tossed ball.

Include multisensory elements in each lesson.

For some topics, using visual elements may be a no-brainer; but challenge yourself to activate other senses during your lessons. Can you help students "physicalize" parts of speech? How can you bring a history lesson to life through sound?

Encourage students to try to remember what they've learned from a lesson, even if the topic is fairly new to them. The process of remembering strengthens memory and identifies gaps that call for a refresher. For example, rather than providing a study guide with bullet points covering key points of a lesson, build your study guides around questions that students will need to answer, thereby creating their own study guide through retrieval.

Concept mapping is the act of connecting separate elements of a topic or unit into a web of comprehension. For example, in a unit on cell structure, students can list out what they've learned about each component of a cell, then visually connect the components to each other by explaining their relationships.

Whenever possible, get your students out of the classroom to apply their learning in the real world. Field trips are a classic example of experiential learning, but even taking your students for a walk around campus to talk about the weather or photosynthesis can help make abstract concepts easier to understand.

This is just a sampling of the ways in which creative educators can leverage neuroscience to help their students succeed in the classroom.

The best teachers never stop pursuing new ways to make learning accessible for all students. The specializations offered by USD's Master of Education program enable educators to drill down into specific areas of interest that not only improve their own teaching practice, but can also lead to higher student achievement and a love of learning. Explore all the ways in which you can strengthen your teaching skill set and boost your earning potential with an advanced degree.

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Brain-Based Learning: 15 Strategies to Improve Student Learning & Retention

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What exactly is brain based learning, and what does it look like? An introduction to the science of learning, plus brain based learning strategies and examples.