# data[].attributes.headline
AI tools could reduce the use of animal testing!

# data[].attributes.subject
AI tools could reduce the use of animal testing!

# data[].attributes.body
- Can predict a chemicals toxicity through computer generated models

# data[].attributes.attachment_caption
forms from the dorsal part of the neural tube, telencephalon. pallium eventually forms cerebral cortex. 

# data[].attributes.body
A project mapping the brains of 1200 Americans to understand the many connections in the Brain

# data[].attributes.body
So detailed that scientists can see features smaller than a strand of hair!

These are just some of many recent big neuroscience initiatives.

# data[].attributes.body
Researchers have created the first high-resolution 3D digital model of the human brain - 'The big brain'

# data[].attributes.body
AI models can be used to predict diseases that a brain may develop over time and enable preventative treatment - ensures patient privacy

# data[].attributes.attachment_caption
week 16-24 cerebral cortex begins to form; neural stem cells located in deep germinative 'ventricular' zone begin to differentiate. 

# data[].attributes.attachment_caption
radial glial cells create bipolar fibres as a scaffold, cells travel perpendicular to cortical surface and settle in outer meningeal surface

# data[].attributes.attachment_caption
algorithm can generate an infinite amount of data, simulate how the brain will change when ageing. apply to preventative treatments

# data[].attributes.attachment_caption
sparseness: probability of connection between two groups of neurons. classed as 'dense' or 'sparse' depending on PROBABILITY

# data[].attributes.attachment_caption
ANNs can predict patterns in incomplete data sets, used in pharmacology to predict dosage

# data[].attributes.attachment_caption
algorithms recognise patterns from MRI scans to diagnose neurological disease

# data[].attributes.attachment_caption
language, vision, visuospatial recognition, awareness etc

# data[].attributes.attachment_caption
ANN vs CNN vs RNN: Neural Networks Guide [cited 2023 Mar 28]

# data[].attributes.attachment_link.readable_text
Neural Networks can be challenging to understand. But if you look at them in the context of what they do and how they can help you, you can come to grips with them in no time.

While we are going to get a bit technical in this post, we have also written a post about specific business applications of Neural Networks. From recognizing speech to analyzing sentiment in a data set and classifying data, there's always a dedicated neural model that you can build - or use.

So in the guide, let's walk through:

What exactly is a Neural Network is.

The three major types and how they compare with each other, and

A brief summary of other Neural Networks that you can use.

Let's dig in!

What is a Neural Network - and why should you care?

A Neural Network is a working system at the heart of a Deep Learning algorithm that helps it process raw data.

In fact, Neural Networks are pretty much like the human brain.

Like our brain that's formed of a network of neurons, Neural Networks are based on connected nodes or units - technically known as artificial neurons. Their job? To identify hidden correlations and patterns in raw data, classify them, and continuously improve.

But why bother learning about Neural Networks? As mentioned, neutral networks sit at the center of all Deep Learning algorithms. So to understand a model or create one, you need to first dive into the architecture of a Neural Network.

With the basics out of the way, let's look at the three main types of Neural Networks.

Artificial Neural Networks (ANN)

Most folks consider Artificial Neural Networks synonymous with Neural Networks. However, both aren't one and the same. Instead, an ANN is a type of Neural Network.

ANN is essentially a feed-forward network because of the way information travels through it - from one layer to another without touching a node twice.

This type of neural system - patterned around how neurons work in our brain - recognizes patterns in raw data, helping solve complex processes.

Another similarity with the human brain: the ANN improves with every new input it gets. In a nutshell, ANN makes independent improvements - or is continuously improving.

Now to dive into the composition of this Neural Network. The ANN is based on three or more interconnected layers of nodes - again, similar to the brain.

All layers are responsible for inputting, processing, and outputting data to the deeper layers. Such an interlayered system is what helps this Neural Network understand and learn complex things.

‍

How Artificial Neural Networks work

What should you use ANNs for?

Owing to its tiered layering, the ANN is used in technology focusing on complex problem solving such as pattern recognition problems.

Examples include:

Predictive analysis for business intelligence.

Speech-to-text transcription application.

Handwriting and facial recognition.

Spam email detection.

Weather prediction.

T ake a look at the more ways AI is used in business.

Advantages of using an ANN

ANNs can bring many perks with their use, here are some of the main ones:

Adaptive nature. Meaning: the Neural Network modifies itself post the initial training with subsequent information that it processes.

Enhanced learning ability. Once trained, the ANN can learn events on its own. It can also make decisions or generate outcomes by comparing the problem with similar events. Due to their node strength, ANNs can perform multiple tasks at a time.

Gradual corruption. A fault in one or a handful of cells in the ANN doesn't stop it from delivering output. Not only does this benefit make the Neural Network fault-tolerant but it also means that corruption tends to be slow. In fact, it occurs over time so you aren't left with an immediate problem on your hand.

Distributed information storage. Unlike information storage in traditional programming, the ANN stores information on the entire network - not a specific database. This is another reason why it's fault-tolerant. A few pieces of missing information in one place don't bring the entire system down but are treated as outliers

Challenges of using an ANN

However, despite their many advantages, Artificial Neural Networks can also pose some challenges, which make it worth it to assess your company's needs and resources before deciding to train ANN:

It takes copious amounts of data to train an ANN. The training consists of providing input to the network and telling it what the output should be.

Difficulty training the network. There is no specific rule for determining the structure of Artificial Neural Networks - it often takes trial and error to get it right.

The architecture can be fuzzy. When ANN gives a problem solution, it does not explicitly outline why and how this result was achieved.

Convolution Neural Networks (CNN)

Convolution Neural Networks are mainly credited for their role in image and video recognition, recommendation systems, and image analysis and classification.

Prior to it, manual labor was involved in identifying objects in images. CNN, however, has helped scale the process by using linear algebra principles to identify patterns in images.

So how do CNNs work? CNNs are based on three main layers. These are:

Convolutional layer

Pooling layer

Fully-connected layer

With each layer, the CNN's complexity in understanding the image increases. This means the first layers focus on interpreting simple features in an image such as its edges and colors. As the image processes through layers, the network is able to recognize complex features such as object shapes. Finally, the deepest layer is able to identify the target object.

What should you use CNNs for?

Broadly, this type of Neural Network is used for Computer Vision.

Essentially, Computer Vision is an AI field that's responsible for identifying meaningful information from visual inputs, images, and videos. For example, identifying dogs from a pile of images.

Such CNN-powered Computer Vision has several applications including:

Advantages of using CNNs

CNNs show one main advantage:

It automatically detects features without human supervision. This gives it an edge over its predecessors.

Challenges of using CNNs

As for its drawbacks, there are two main ones:

Difficulty in dealing with variance in the data presented. CNN has a hard time processing objects in images that are hidden to an extent. With image classification too, the network has difficulty classifying titled or rotated images. Put simply, CNN can't encode an object's orientation and position and can't process spatially invariant data.

Computationally demanding. Training CNN requires numerous graphical processing units (GPUs). To add to that, if you lack good GPUs, the training becomes slow.

Recurrent Neural Networks (RNN)

RNNs are unique on account of their ability to process both past data and input data - and memorize things - and were developed to overcome the weaknesses of the feed-forward network. Practical applications include Google's voice search and Apple's Siri.

Like ANN and CNN, RNN also learns with training data. From there on, it doesn't process data on inputted data alone. Instead, it uses data from past inputs to make decisions too. In a nutshell, this architecture is built for having a 'memory'.

What should you use RNN for?

Because RNN has an internal memory, it can make relatively precise predictions. What's more, it's useful for solving sequential data-based problems.

Keeping this in mind, RNN applications include:

Prediction problems

Machine translation

Speech recognition

Sentiment Analysis

Stock price forecasting

Generating text and language modeling

Advantages of RNNs

Recurrent Neural Networks have unique abilities as opposed to other kinds of Neural Networks, which open a wide range of possibilities for their users - however, also bring some challenges with them. Here's a rundown of the main benefits:

It's the only Neural Network with memory and double data processing.

It can map out several inputs and outputs. Unlike other algorithms that deliver one output for one input, the benefit of RNN is that it can map out many to many, one to many, and many to one input and outputs.

Above all, RNNs have an in-depth understanding of sequences and their context in contrast with other Neural Networks.

Challenges of RNNs

With great benefits, naturally, come a few challenges:

Slow and complex training. In comparison with other networks, RNN takes a lot of time in training. To add to that, the training is quite complex and difficult to implement.

Exploring or vanishing gradient concern. Due to the way the layers are arranged in an RNN model, the sequence typically gets rather long. This results in the model training with exploding or null weights, leading to gradient concerns.

RNNs can't be stacked up. For the RNN model to work, each node requires the output of the previous one. This means you can't stack up training or pair this model with another model. In turn, this leads to high computational expenses.

How should you evaluate your Neural Networks?

As with most things in Machine Learning & Deep Learning, it mostly boils down to what you're trying to achieve - and what your success metrics are. You'll likely find structural and application differences in all NNs.

To help you see how each algorithm compares with another, we've summarized some of the parameters in the table below:

Table comparing the characteristics of CNN, ANN and RNN

Other types of NNs & when you should consider using them

Before we wrap this up, here's a quick rundown of other types of Neural Networks:

Perceptron. This algorithm uses numerical inputs, weights, and a bias to make classifying predictions. Use it for data classification into two groups.

Multilayer perceptron. This is a type of NN that has multiple layers of perceptrons. It's used for solving stochastic problems, giving you approximate solutions for complex problems, e.g. encoding databases or routinely checking database security consistency.

Feed-forward Neural Network. Any Neural Network in which information flows in one direction is a FF Neural Network. Use it for weather prediction and other purposes that involve learning the relationship between independent variables.

Radial Basis Function (RBF) Neural Network. RBF is a common type of feed-forward Neural Network based on three layers. It is, however, differentiated from other models due to its faster learning speed. Uses include classification, time series prediction, system control, and function approximation.

Modular Neural Network. This is a type of ANN known for solving complex AI problems. Its structure is based on a set of Neural Networks - all independent (working on separate inputs and performing subtasks) and moderated using an intermediary.

Long Short-Term Memory (LSTM). This is a type of RNN, named so because it uses short-term memory processes for creating long-term memory. It's used mainly in Deep Learning, e.g. for making predictions in speech recognition.

Sequence to sequence model (seq2seq). This is another type of RNN in which either both or one of the input and output are sequences. It's involved in solving complex language problems. Consequently, its uses include creating chatbots, machine translation, and text summarization.

Get started

In short, you need different tooling to solve different problems. If you're just starting out on your Machine Learning journey, it's important to know and define the problem you're trying to solve. It takes a great deal of effort to become proficient enough to build something on your own - and building literacy in this topic will help implementation processes.

Remember:

ANNs (Artificial Neural Networks) are helpful for solving complex problems.

CNNs (Convolution Neural Networks) are best for solving Computer Vision-related problems.

RNNs (Recurrent Neural Networks) are proficient in Natural Language Processing.

So... what are you building? Check out how businesses use AI here.

# data[].attributes.attachment_link.title
ANN vs CNN vs RNN: Neural Networks Guide

# data[].attributes.attachment_link.description
Neural Networks can be challenging to understand. But if you look at them in the context of what they do and how they can help you, you can come to grips with them in no time. While we are going to get a bit technical in this post, we have also written a post about specific business applications of Neural Networks.

# data[].attributes.attachment_caption
The Development and Shaping of the Brain. [cited 2023 Mar 28]

# data[].attributes.attachment_link.title
The Development and Shaping of the Brain - Discovering the Brain - NCBI Bookshelf

# data[].attributes.attachment_link.description
The making of the human brain from the tip of a 3 millimeter neural tube is a marvel of biological engineering. To arrive at the more than 100 billion neurons that are the normal complement of a newborn baby, the brain must grow at the rate of about 250,000 nerve cells per minute, on average, throughout the course of pregnancy. But it is not the volume of growth alone that makes the production of a human brain staggering to consider. The great number of functions that the brain reliably carries out and the specificity with which these are assigned to one or another type of cell or small location in the whole assembly are stunning in their complexity; yet the feat of growing a human brain occurs in hundreds of millions of individuals each year. The brain’s 100 trillion or so interconnections provide the physical basis for its speed and sophistication. But how is such an intricate network constructed in the first place? Does the genetic material of the fertilized egg already contain a full set of building specifications for the human brain, in which every cell is created as a minute increment in the overall design? And if the set of instructions is indeed so closed and specific, how could chance or random mutations or the influence of the environment have played a role—as they so evidently have done and continue to do—in the emergence of the first human brains?

# data[].attributes.attachment_caption
Brain and Artificial Neural Networks: Differences and Similarities. [cited 2023 Mar 28]

# data[].attributes.attachment_link.readable_text
There is no point in reiterating that AI is on the rise. And one major contribution to this rise is the inception of artificial neural networks (ANN) which have been extensively used to make models for a variety of scenarios. Since its inception, a number of variations have been made of an ANN, each with its own utility. But with such easy implementation (what with Keras in our arsenal), we often tend to forget the roots of it. The idea of an ANN has its roots in the way the brain works, or at least that's what their inventors claimed. ANN was developed keeping in mind the way the human brain works. There has been no dearth of research in the field of studying the brain either. We know more about our brain now more than we knew a century ago. We have come a long way since then and maybe now is the right time to check how accurate that analogy between an ANN and an actual brain is.

The correct way of doing it is to first study human behaviour. The human brain has a biological neural network which has billions of interconnections. As the brain learns, these connections are either formed, changed or removed, similar to how an artificial neural network adjusts its weights to account for a new training example. This is the reason why it is said that practice makes one perfect since a greater number of learning instances allows the biological neural network to become better at whatever it is doing. Depending upon the stimulus, only a certain subset of neurons are activated in the nervous system. The figure shows the diagram of a neuron. There is a dendritic network that takes input from other neurons and feeds it to the nucleus. The signals received are aggregated in the nucleus and if it exceeds a threshold, the axon passes a signal down to the other neurons. This is similar to the perceptron, the very first artificial neuron which just output 0 or 1 depending on whether the Heaviside function applied on the input gave a positive value or not. The neuron is either 'activated' or 'inhibited' based on the level of aggregation.

Let's now compare it with an artificial neural network. The most obvious similarity between a neural network and the brain is the presence of neurons as the most basic unit of the nervous system. But the manner in which neurons take input in both cases is different. In our understanding of the biological neural network, we know that input is taken in from dendrites and output through the axon. These have significantly different ways of processing input. Research shows that dendrites themselves apply a non-linear function on the input before it is passed to the nucleus. On the other hand, in an artificial neural network, the input is directly passed to a neuron and output is also directly taken from the neuron, both in the same manner.

While a neuron in an artificial neural network has the capability to give a continuous set of outputs, a neuron in an artificial neural network can give only a binary output which is of the order of a few tens of millivolts. The manner in which the signal is passed and aggregated is like this. The resting potential of the neuron's membrane is around -70mV. If the signals aggregated by the nucleus hit a certain threshold, the axon transmits a certain high voltage known as the action potential (hence the binary). For instance, if the threshold is -55mV, and each neuron gives out 5mV, a minimum of 3 neurons will have to activate this neuron for it to pass information forward. This is demonstrated by the graph below. After the threshold has been hit and the signal is passed by the axon, the cell may not be stimulated for a brief period of time known as the absolute refractory period. So there is a significant difference in the type of output that can be expected from a biological neuron and an artificial neuron.

Since transmission of information is binary in a biological neural network, research has shown that the nervous system encodes information in the frequency at which the impulses are transmitted in which the average frequency at which impulses are transmitted becomes important. Some research suggests that information is passed using temporal encoding in which the time at which the impulses are received is more important. In either case, the mechanism is starkly different from some of the techniques used to transmit information between neuron layers in artificial neural networks such as one-hot encoding of categorical information.

The learning at a very abstract level in both the systems is similar. But the method of learning is different. Artificial neural networks use gradient descent to minimize a loss function and to reach the global minimum. Biological neural networks have a different kind of learning method. Gradient descent in artificial neural networks required backpropagation which is possible only to the extent of one neuron in a biological neural network. Biological neural networks use the process of Hebbian learning using which the efficiency of one neuron being able to activate another neuron is made better by as many learning instances as possible. Spike-Dependent-Timing Plasticity strengthens the connection between neurons if input spikes in the first neuron occur immediately before output spikes in the second neuron. If the input spike of the first neuron occurs immediately after the output spike of the second neuron, the connection is weakened.

Generalization, or the ability to abstract knowledge from what one has previously learned, is an extremely useful capability that allows problem-solving across different domains quickly via minor weight adjustment - a process called fine tuning - which is a neural network's solution to transfer learning and domain adaptation problems. The fact that not so many neuronal connections require re-wiring is one reason why if a person has one skill then it doesn't take much time to take up a similar skill.

One major point of difference between an artificial neural network and the brain is that for the same input the neural network will give the same output but the brain may falter. It may not always give the same response to the same input and this is commonly known in the business language as human error.

Many variations have been introduced in artificial neural networks now. A convolutional neural network is one which is used to process images and each layer applies a convolution process followed by other operations on images which reduces or expands the dimensions of the image, leading the network to capture only the details that matter. The main features and computations done by convolutional neural networks were directly inspired by some of the early findings about the visual system. It was discovered that neurons in the primary visual cortex respond to specific features in the environment, like edges. Two kinds of cells were discovered: simple cells and complex cells. Simple cells responded only in a particular orientation and complex cells responded in more orientations. It was concluded that complex cells pooled over inputs from simple cells due to which there was spatial invariance in complex cells. This inspired the idea of a convolutional neural network.

Encoding models are used for predicting brain activity in response to sensory stimuli to understand how sensory information is represented in the brain. Encoding models usually apply a nonlinear transformation of stimuli to features and a linear convolution of features to responses.

There are many other differences. For instance, the number of neurons in our brain is about 86 billion. The number of neurons in normal artificial neural networks is less than 1000 which is nowhere close. Research also suggests that the power consumption by biological neural networks is around 20W whereas by artificial neural networks is around 300W.

As the gap between the human brain and neural networks closes in, the world inches towards an era of greater artificial intelligence.

# data[].attributes.attachment_link.title
Brain and Artificial Neural Networks: Differences and Similarities

# data[].attributes.attachment_link.description
There is no point in reiterating that AI is on the rise. And one major contribution to this rise is the inception of artificial neural networks (ANN) which have been extensively used to make models for a variety of scenarios. Since its inception, a number of variations have been made of an ANN, each with its own utility.

# data[].attributes.body
• single layered/unlayered networks exist.
• fully connected: each neuron in one layer connects to every neuron in the next
• pooling: group of neurons in one layer connects to one neuron in the next, reduces neurons in next layer

# data[].attributes.body
like biological networks ANNs can be organised into layers. signals travel from input layer to output layer.
outputs of the final output neurons accomplish ANN task

# data[].attributes.body
ANN neurons can produce a continuous set of outputs whereas biological neurons have an absolute refractory period

# data[].attributes.body
artificial + biological neurons produce a binary output once a threshold potential has been reached

# data[].attributes.body
mechanism allows program to compute data as a human brain would. dynamic model is used in image + speech recognition, medical diagnosis etc

# data[].attributes.body
can simulate many neurons at once and reproduces firing patterns observed in nature

# data[].attributes.body
uses two differential equations that describe the membrane potential and recovery variable of a neuron

# data[].attributes.body
Eugene Izhikevich mathematical model simulates behaviour of neurons.
used in computational neuroscience research.

# data[].attributes.body
Neurons are connected within their group and to groups of other neurons.

# data[].attributes.attachment_caption
Clinical Applications of ANNs: pharmacokinetics. [cited 2023 Mar 28]

# data[].attributes.attachment_link.readable_text
Author links open overlay panel Syeda Saba Kareem ¹ , Yashwant Pathak ²

Show more

Abstract

Artificial neural networks (ANNs) were designed to simulate the biological nervous system, where information is sent via input signals to a processor, resulting in output signals. ANNs are composed of multiple processing units that work together to learn, recognize patterns, and predict data. ANNs do not require regimented experimental design and have the ability to function even with incomplete data. They can be used in multifaceted, nonlinear systems with applications in the field of pharmacokinetic modeling. Pharmacokinetic/pharmacodynamic studies are used to predict meaningful correlations among doses administered, drug concentration levels, and pharmacological response. ANNs can be useful tools in analyzing the data involved in physiological processes, which can have vast amounts of complex variables with nonlinear relationships. ANNs are also convenient for handling data involving dosage form technology because they can simultaneously handle multiple independent and dependent variables without initial definition of causal relationships between the variables and response. The clinical applications of using ANNs in pharmacokinetic modeling are further discussed in this chapter, with specific examples from clinical studies being conducted.

Keywords

Artificial neural networks (ANNs)

# data[].attributes.attachment_link.title
Clinical Applications of Artificial Neural Networks in Pharmacokinetic Modeling

# data[].attributes.attachment_link.description
Author links open overlay panel Syeda Saba Kareem 1 , Yashwant Pathak 2 Show more Artificial neural networks (ANNs) were designed to simulate the biological nervous system, where information is sent via input signals to a processor, resulting in output signals. ANNs are composed of multiple processing units that work together to learn, recognize patterns, and predict data.

# data[].attributes.body
Initially we chose to make a 3D printed model but resulted in an AI program due to limitations such as: technology, current understanding of the brain, and materials to make a physical model.

# data[].attributes.attachment_caption
computational input and output nodes attempt to emulate cortical layers

# data[].attributes.body
The topic is still in the process of research.

Therefore it was difficult finding evidence for the project

Issues determining the legitimacy/validity of the articles we found.

Limited information made the idea of synthesizing an organoid brain difficult.

# data[].attributes.attachment_caption
Scientists create first digital brain tissue. [Internet]. www.youtube.com. [cited 2023 Mar 14]. Available from: https://youtu.be/x-WEbywUWek

# data[].attributes.attachment_link.title
Scientists create first digital brain tissue.

# data[].attributes.body
Mirrors synaptogenesis!

# data[].attributes.body
Mirrors differentiation of NSC in nature!

# data[].attributes.body
The group decided instead of creating a physical 'ORGANOID' brain we should instead model the brain as a programme: the DIGITAL brain

# data[].attributes.body
1. We still don't fully understand how the brain works

2. New computing technologies will need to be developed to mimic the brain's processes

3. The power requirements would be astounding.

4.There are ethical considerations in developing a synthetic AI brain.

5.Without a supercomputer the AI models would take too long to train and the image quality would be poor

# data[].attributes.body
The connection weight simulates the strength of connection

# data[].attributes.headline
How can we use the computer brain model to create a synthetic brain?

# data[].attributes.subject
How can we use the computer brain model to create a synthetic brain?

# data[].attributes.headline
How can we use artificial brains to understand how real brains work?

# data[].attributes.subject
How can we use artificial brains to understand how real brains work?

# data[].attributes.body
Teaching a computer model how to read can aid in a better understanding of dyslexia.

# data[].attributes.headline
Inspiration - King's involvement in the Synthetic Brain Project

# data[].attributes.subject
Inspiration - King's involvement in the Synthetic Brain Project

# data[].attributes.body
Project aim - create 3D models of the human brain to aid understanding of what it looks like across a variety of ages, genders, and diseases

# data[].attributes.body
Neurons in different layers are connected according to connection weights and sparseness

# data[].attributes.body
Carbon based (organic) material, less likely to be rejected by human tissue.

Invasive nature means material could evade surrounding tissue, promoting cancers/lesions

# data[].attributes.body
Neurons expressed as 80% excitatory and 20% inhibitory.

# data[].attributes.attachment_caption
week 27-38: neurons differentiate and form connections with each other through synaptogenesis.

# data[].attributes.body
This project used a python programme to simulate high cortical processes.

# data[].attributes.attachment_caption
A Computational Model of the Brain Cortex and Its Synchronization. [cited 2023 Mar 28]

# data[].attributes.attachment_link.readable_text
Obtaining the computational models for the functioning of the brain gives us a chance to understand the brain functionality thoroughly. This would help the development of better treatments for neurological illnesses and disorders. We created a cortical model using Python language using the Brian simulator. The Brian simulator is specialized in simulating the neuronal connections and synaptic interconnections. The dynamic connection model has multiple parameters in order to ensure an accurate simulation (Bowman, 2016). We concentrated on the connection weights and studied their effect on the interactivity and connectivity of the cortical neurons in the same cortical layer and across multiple layers. As synchronization helps us to measure the degree of correlation between two or more neuronal groups, the synchronization between the neuronal groups, which are connected across layers, is considered. Despite its obvious importance, there are no sufficient studies concerned about the synchronization in the simulated cortical models. Such studies can help in examining the hypothesis and the dynamical behavior of the simulated model. In this paper, we simulated a cortical model and dynamical behavior and then studied the effect of input noise on its internal neuronal networks and their synchronization.

1. Introduction

In the last decade, the work on obtaining computational models for the brain has been increased tremendously. To understand the connectivity and functionality of the neuronal groups, models at different levels have been proposed. Especially, modeling the cortex has been the most studied subject among the computational neuroscientist. Mostly, the focus is on improving the measurement and imaging techniques like EEG (electroencephalogram) and fMRI (functional magnetic resonance imaging) and collecting and processing data with these techniques. These modern brain imaging techniques are valuable as they provide data for the approval of the computational models focusing on understanding the relationship between cognition and the brain [ 1- 3]. The models developed for the cortex are ranging from a detailed single neuron model [ 4] to the mass models where the collective activity of a population of the neuron is considered [ 3].

Still, the field of brain modeling is in development and needs more ideas to improve the already available models and theories. The most accurate modeling of the human brain occurred in a Japanese university; using a Japanese supercomputer, it took 40 minutes to simulate 1 percent of the human brain neuronal connectivity working for 1 second [ 2]. The modeling of the brain, at different levels, would provide means to understand the cognitive processes and improve new approaches for diagnosis and treatment of neurological diseases and disorders. Of course, as nature inspires engineers, work on the brain would provide new ideas to develop intelligent agents and systems. New approaches in machine learning already are due to work on the brain as Barto et al.'s reinforcement learning [ 5, 6] and Grossberg's adaptive resonance theory [ 7]. The spiking neuron models are also considered in developing new machine learning methods [ 8], and working on the spiking neural network models would improve these approaches.

In this work, the activities of a cortical population will be considered, and the model will be constructed using the Izhikevich neuron model; it has been recorded in all mammal brains in a band starting from as low as one wave every forty seconds (0.025 Hz) to as high as six hundred waves per second (600 Hz) [ 9]. Since the synchronization of neuronal activity is considered to give rise to brain oscillations observed in EEG [ 10, 11], synchronization of the neuronal groups will be investigated. Synchronization is the degree of correlation between two or more spiking neuron activities in the same time interval and it depends on many chemical and electrical spiking interactions [ 12].

The measurement of synchronization has many applications in monitoring the activity of the brain and the degree of interaction between different brain parts, which gives us a thorough idea about brain functionality both normal and deficiency, where synchronization can give us a right indication about the degree of the engagement of two or more neuronal groups in some mental task, and the more coherence between the firing patterns of these neuronal groups, and the physiological interactivity between them [ 4].

In this way, we can state that the degree of synchronization can be an indication of how good the mental activities of a person are and also can be used as an indication of the spreading of some mental diseases such as epilepsy, Parkinson's disease, autism, and Alzheimer's disease (AD) [ 13- 15].

From the engineering point of view, the synchronization index is an important indication of the dynamical behavior of nonlinear large network systems, since in the case of computational modeling, we are more interested in the overall behavior of the entire system. On the other hand, as we know the brain consists of anatomically separated parts called lobes. Physiologically, it is found that these lobes can be engaged together to do a certain mental task [ 16].

Although of the obvious importance of synchronization, there are no studies about the synchronization of the simulated neuronal models. In this paper, we simulated the brain cortex and its dynamical behavior and calculated the occurred synchronization between the internal neuronal groups due to implying thalamic noise as a stimulus. Calculating the synchronization between the internal neuronal groups of our model will not just ensure the stability of the model [ 17] but will also ensure the right interconnectivity and dynamical behavior of the simulated model. This latter property can be helpful when using this model to study cortical behavior through different types of learning and other applications.

In this work, we are inspired by Izhikevich's and Edelman's work on a large-scale brain model [ 18], and we simulated 10000 neuron activities where 22 different types of multicompartmental neurons are considered. The model is constructed with the Brian (v1.4) simulator, and this work gives an example of realizing a large-scale model of the brain using a computer with ordinary computational properties [ 5]. The results obtained with the model are similar to neuronal activity observed with EEG measurements of the normal human brain cortex. Using this model, we investigated the effect of changing the connection weights on synchronization. It is claimed that the relation between synchrony and the neuronal code aroused from the activation of a group of neurons will inspire new learning techniques [ 7]. This model can be used also to study the role of short-term plasticity and STDP (spike timing-dependent plasticity) on learning as in [ 5, 19], where the Brian simulator has a library that is related to STDP, by defining the pre- and postsynaptic neuronal groups, which can be applied to our model [ 20].

2. Firing Patterns in the Cortex and Simple Neuron Model

The cortex has an important role in the functionality of the brain. It includes the sensory areas, motor areas, and the association areas, and has interactions with the subcortical areas. All these make the cortex responsible for most of the activities of our daily life. Association areas are especially important, in combining different sensory inputs with the processing of subcortical areas to give rise to high-order cognitive tasks. The association functions include generally everything related to language, analyzing information, memorizing, planning, and conscious thinking (creativity) [ 21].

The cortex consists of six layers; each one of these layers has different cell types with its own firing type which are listed in Table 1; we worked to simulate the brain cortex both anatomically (by using the same rate of the existence of neurons of the cortex and simulated number of synapsis between them) and physiologically (by simulating their group behavior) [ 22, 23]. On the other hand, spiking in the cortical layers can be divided functionally into excitatory and inhibitory groups. While the excitatory groups work to increase the sympathetic activity in the body, the inhibitory groups work to increase the parasympathetic actions in the body [ 24].

To model the different behaviors of different cell types in the cortex, we used Izhikevich's neuron model, which is expressed mathematically through the use of the following nonlinear dynamical equations [ 23].

There is a reset condition: where and are dimensionless variables, with representing the membrane potential and representing the membrane recovery variable.

The Izhikevich model is capable of expressing different firing types due to chance in the parameter values; so for a large-scale model of the cortex, it provides a suitable model of neuron behavior. Equations ( 2) and ( 3) include also other parameters ( and these parameters are responsible on giving the spike its characteristic shape and can be found in Table 2 for each spiking type.

3. Creating the Cortical Layers

As we mentioned previously, the cortical brain neurons can be classified functionally to excitatory and inhibitory neurons. The distribution of these two different functional groups of cells is 80% excitatory and 20% inhibitory [ 25]. In the cortex model which we proposed, the excitatory cells are pyramidal and spiny cells, and their distribution with the spiking type is illustrated in Table 3, according to the level considered. The cortex also has two types of inhibitory cells which are called GABA cells due to the effective neurotransmitter. Like excitatory neurons, they are also distributed in all six layers [ 16]; the way they are considered in the proposed model is illustrated in Table 3. From Tables 3 and 4, we can see that the percentage of excitatory and inhibitory neurons considered in the model matches the human cortex [ 25].

Up to this point, we have discussed the neuron types that are considered in the proposed model where pyramidal and spiny cells are taken as excitatory neurons, and GABAergic neurons represent the inhibitory. Now, to simulate this cortical model, these neurons in the different layers should be connected according to connection weights and their sparseness. In Brian environment, the connections are defined with certain weight and sparseness values as dynamic variables and this connection is defined as follows [ 26]:

Connection (RS_p2, LTS_nb2,' g_ampa',

structure =' sparse', sparseness = 0.1,

where denotes the group from which spikes will be propagated, denotes the group to which spikes will be propagated.

With this structure, it is possible to define the types of connections between the connected layers to be either sparse or dense. Sparse which means a spike will either occur or not between the connected layers compared to the dense which means a spike will certainly occur between all the connected layers; it will be the default choice in our case since it is more suitable for modifying the dynamical connections [ 27, 28]. The dynamical part of the synaptic connections (here for the groups, in our example it is ) is represented by another differentiation equation, Equation ( 5), where denotes the dynamic variable corresponding to synaptic connection and it is initialized with random values. is the recovery time, which is the time required by the membrane to return to its rest potential; each neuronal group has its own dynamical variable and recovery time ( The whole types of dynamic variables ( used in our model are listed in Table 5.

Returning to the example of connection we mentioned, sparseness denotes the probability of a connection between two groups of neurons to occur and its value is taken to be between 0.1 and 0.2 in our model [ 25]. The synaptic weight between the neurons can also be changed dynamically, where the weight here denotes the values added to the dynamical variable ( of each connection in order to increase or decrease the strength of the connection as in Equation ( 6).

Once connected, neurons start to fire; the strength of the synaptic connections between them will either increase or decrease according to the functional property of the neurons considered (excitatory neurons or inhibitory neurons). While excitatory neurons increase the synaptic connections, the inhibitory neurons decrease them. These changes in the synaptic connections due to firing of neurons are called Hebbian rule and it is specified by changing the weights of connections. Where according to the definition of Hebbian learning and according to general Equation ( 7), STDP is a good choice for long-term synaptic plasticity exploiting, where spike timing-dependent plasticity (STDP) is a Hebbian synaptic learning rule in which the millisecond timing of pre- and postsynaptic-paired activities induces either synaptic long-term potentiation (LTP) or long-term depression (LTD) [ 29]. where is the change in synaptic weight, is the learning rate, is the presynaptic neuron, and is the postsynaptic neural response.

Also about the weight values, it has been taken from 0 to 100 for statistical purposes. In our model, the weight will be changed throughout iterations to investigate its role in synchronization. We took the weight values to be between 1 and 100. Now, as we defined our neuron structures and connections, we are ready to form the neuronal groups that compromise the activity of the cortex.

4. Creating Neuronal Groups

In order to model the behavior of the cortex and corticothalamic connections, the most effective firing pattern for each layer is considered and parameters of the Izhikevich neuron model are chosen to create the relative firing pattern. The neurons in each group are not only connected to the neurons in the group but also to the neurons in other groups. Even though the connection between and in groups has different properties, still the establishment of connections is done in such a manner that with the parameters defining connections in Brian, the biologically plausible model is obtained.

The thalamic model was also built, where it consists of both the excitatory and inhibitory groups. While the excitatory groups consist of regular spiking (RS) and bursting (IB), the inhibitory groups consist of the GABA groups (see Tables 3 and 4). The connections of the thalamic groups were targeted towards cortical layers of L2/3, L4, and L6. The effect of the corticothalamic connections was introduced as a noise input to the cortical model at the mentioned layers.

The entire network proposed for the cortex with connections properties and different cortical neuronal groups is shown in Figure 1. Here, each one of the five blocks represents a neuronal group; inside each block is the name of the group and its connection sparseness is given as a percentage. One can notice that the names of the blocks are the same as the names of spiking types mentioned in Table 1. The circles inside each block stand for the subgroups of the main group, which represents the percentage of distribution of the neurons. The main group in each layer in the cortex can be realized from the name of each circle which coincides with Tables 3 and 4; each subgroup has its own connection properties defined with the parameters of dynamic variable as illustrated in Table 4.

The three upper blocks (RS, CH, IB) are the excitatory groups, while the lower two (FS, LTS) are the inhibitory ones, with the sum of total percentage of 80% for the excitatory groups and 20% for the inhibitory groups, respectively, as shown in Tables 3 and 4. Arrows represent connections, and as we can see, the connections are established in such a way that every single neuron has the ability to connect to its own followers in the same group and the neurons in other groups via bidirectional connections. The arrows are colored as red for excitatory and blue for inhibitory. In this way, we can make a model that is capable of simulating the behavior of microcolumns in the cortex, even in its learning capabilities [ 30].

The results obtained from the simulation of the model are given in Figures 2 and 3, where the title of each figure ( in Figure 2 for example) illustrates the spiking type which is also the name of the neuronal group, while (p2) means pyramidal cells located in layer (2) in the cortex (see Tables 3 and 4). In Figures 2 and 3, some of the excitatory and inhibitory groups are plotted, where the first row is the raster plot of the firing neurons which shows us the overall activity of the groups by showing the number and temporal location of the firing neuron in each unit time (in our model 1 ms), the second row is the single spike (the membrane voltage of one neuron) which in our case is (RS) type in Figure 2, the third row is variable of this neuronal group which is the time recovery after each spike, the fourth row is the dynamic variable and finally, the fifth row is the firing rate which represents the number of spiking neurons per unit time which in our case milliseconds (ms). So, from Figures 2 and 3, we can get the overall activity of the groups which is given by raster plots; the behavior of single neuron, the dynamics of the connections, and the firing rate illustrate activeness in the neuronal population which are also shown.

If we take a second look at Figures 2 and 3 and by examining the first and fifth rows, we can see that the spiking of the group has a random behavior compared to the single spiking behavior shown in the second row of the same figures. This behavior is a sign of existence of an interaction between different groups in the model, i.e., this kind of randomness in rows (1 and 5) means that the spiking of this group of neurons has been affected by the spikes of other groups since normally these two rows were to show more rhythmic spiking behavior that match with the single spike behavior shown in the second rows of the figures if there was no synchrony at all. This interaction between this group and the other groups can be measured then by measuring the degree of synchrony that occurred between them. In the next section, we will calculate how much each group of neurons has been affected by other groups and by the weight of the connections and this will be done by calculating the synchrony measuring factor. The obtained results represent both the cortical model, the thalamic model, and their connection dynamical behavior.

5. Measuring Synchrony

Finally, in order to ensure the biological postulate of our model, one way is to monitor its dynamical behavior through the synchronization measurement. To decide on the level of synchronization, a synchronization measurement has to be considered. Especially, to see the synchronization between groups, the interactivity of each one of the neuronal groups created with other groups is considered while changing the connection weights between the groups. This will help us to understand the effect of connection weights on the whole model, the degree of effectiveness of each layer by the action of the other layers. The synchronization measurement can be considered as the estimator of connectivity in the cortex [ 18]. Measuring the interactivity between cortical layers is important especially in the case of cognitive acts, which need a harmonic and cooperative action from most of the cortical layers [ 31]; it is important also in many applications like detecting and measuring the degree of brain disorders [ 32]. The synchrony measurement of the neuronal groups increases the ability to predict the behavior for long-term neuronal interaction. It can also give us a prediagnostic about the availability of specific mental diseases [ 33]. Furthermore, it helps the investigation of the active brain areas during some activities like movement or cognition [ 26, 34]. The synchrony measure for neuronal activity of neurons is given as follows [ 34]:

In Equation ( 8), is the variance of the activity of all neurons; so to calculate it, the mean of neuron behavior is considered and the signal is defined as follows:

So, the behavior of averaged over is calculated first; then, is obtained as follows:

The variance of each neuron is calculated in a similar way:

The synchrony measure is calculated while changing the connection weights in an interval of 1 to 100 incrementing each time by 10 mV for the inhibitory and excitatory groups. Regular spiking (RS) and chattering (CH) have neuron behavior in the excitatory group, while the inhibitory group has fast spiking (FS) neurons. The simulation results are given in Figures 4 and 5.

Figures 4 and 5 show the results of synchronization measurement of each group. In Figure 4, we plotted the synchrony measurements of the excitatory groups ( and ), while in Figure 5, we plotted the synchrony measurements of the inhibitory groups ( and ). The axis is the degree of synchronization which is normally between 0 and 1, where 0 means no synchrony at all and 1 means total synchronization between this group (under study) and the other groups in the model. The axis is the value of weight of connection; it changes during the run in each iteration.

Generally, the figures show good synchronization between the neuronal groups in our model, which means there is good connectivity between them.

6. Conclusion

In this simulated model, we worked to simulate the brain cortex both anatomically (by using the same rate of the existence of neurons of the cortex and simulated number of synapsis between them) and physiologically (by simulating their group behavior). The effect of connection weights over the strength of the connection is investigated. The degree of connectivity was tested thoroughly by calculating the synchronization between the connected neuronal groups. The synchronization can give us a right indication and a thorough idea about brain functionality both in normal and deficiency cases, and about the degree of the engagement of two or more neuronal groups in some mental task, it also serves as an index to indication of the dynamical behavior of nonlinear large network systems, since in the case of computational modeling, we are more interested in the overall behavior of the entire system, which here can justify the simulated model and ensures its, biological-like, dynamical behavior. Even though synchronization within a group has been considered in previous works, the synchronization between groups is considered for the first time in this work up to our knowledge. The result shows that there is an increase in the synchronization factor with the increase occurs in the connection weights, which shows that there is an increase in the connectivity between neurons. This conclusion can be used in multiple applications especially in machine learning and cognitive research fields. As a future work, this model can be improved and used in the field of AI learning, especially the STDP method.

Data Availability

No data were used to support this study.

Conflicts of Interest

The author declares that there are no conflicts of interest.

Acknowledgments

The author thanks Prof. Dr. Neslihan Serap Sengor from Istanbul Technical University for her valuable support.

# data[].attributes.attachment_link.title
A Computational Model of the Brain Cortex and Its Synchronization

# data[].attributes.attachment_link.description
Obtaining the computational models for the functioning of the brain gives us a chance to understand the brain functionality thoroughly. This would help the development of better treatments for neurological illnesses and disorders. We created a cortical model using Python language using the Brian simulator.

# data[].attributes.body
Week 24-26: cerebral cortex develops it's characteristic six-layered structure

# data[].attributes.body
divide and differentiate into excitatory pyramidal neurons and inhibitory neurons

# data[].attributes.attachment_caption
Artificial neurons must adapt to changes in their environment: reshape itself.

# data[].attributes.attachment_caption
It is fundamental to understand the principles of neural development in order to create an accurate digital model

# data[].attributes.body
Fundamentally, the human brain is a complex organ to emulate and scientists do realize that it may take years to build anything close to the brain, chiefly imitating every part of its neural functions.

However, the efforts to mimic our biological synapses do synthesize similar capabilities by developing algorithms and technologies in information processing with the help of medicine, computer science and engineering integrations.

# data[].attributes.attachment_caption
Chemists grew a “synthetic brain” that stores memories in silver [Internet]. Futurism. [cited 2023 Mar 14]. Available from: https://futurism.com/self-assembling

# data[].attributes.attachment_link.readable_text
Natural Growth

In the ongoing quest to build an artificial human brain, scientists from UCLA may have just taken a big step forward. While a real synthetic brain is still far away, a team of chemical engineers found out how to grow self-assembling circuitry that resembles the structure and electrical activity of parts of a brain, according to ZDNet.

The research is the pet project of UCLA chemical engineer James Gimzewski, who proclaimed that he wanted to create a synthetic brain back in 2012.

"I want to create a synthetic brain," Gimzewski wrote at the time. "I want to create a machine that thinks, a machine that possesses physical intelligence... Such a system does not exist and promises to cause a revolution one might call the post-human revolution."

Reaching Out

Gimzewski and his team found that a grid of tightly-packed copper posts, when treated with silver nitrate, grew nanowires out in seemingly random directions that mirror the branching, interconnect neurons found in a brain.

On the atomic scale, the connections among the silver nanowires resemble synapses, which are the junctions at which two neurons meet up and transmit signals among with each other. The way that the nanowires organized themselves mirrors the sort of structures that would pop up during an MRI of a brain as it stores memories, according to ZDNet.

When the network of wires is hit by an electrical signal, the nanowires seemed to be organizing information as a brain does - all on their own.

"When they're all combined, they start to talk to each other," Gimzewski told ZDNet. "In a way, the whole circuitry comes alive, in a sense, in that every part is interacting with every other part. And there are pathways in which we can establish stronger neuromorphic connections."

Not Quite There

In spite of his 2012 proclamation, Gimzewski now urges caution.

"It's dangerous to directly correlate things like, 'This is a brain!'" Gimzewski told ZDNet. "It's exhibiting electrical characteristics which are very similar to a functional MRI of brains, similar to the electric characteristics of neuronal cultures, and also EEG patterns."

READ MORE: Neuromorphic computing and the brain that wouldn't die [ ZDNet]

More on brain-like circuitry: Brain-Based Circuitry Just Made Artificial Intelligence A Whole Lot Faster

# data[].attributes.attachment_link.title
Chemists grew a "synthetic brain" that stores memories in silver

# data[].attributes.attachment_link.description
In the ongoing quest to build an artificial human brain, scientists from UCLA may have just taken a big step forward. While a real synthetic brain is still far away, a team of chemical engineers found out how to grow self-assembling circuitry that resembles the structure and electrical activity of parts of a brain, according to ZDNet .

# data[].attributes.body
Studying the human brain is challenging - complicated and has many ethical limitations.

If we can create synthetic versions of this data, we can do as many studies as we want without having to jeopardize the patients' privacy!

# data[].attributes.body
- Neurological diseases affect 1 in 6 people worldwide
- Leading cause of disabilities
- Cause around 140,000 deaths every year in the UK

# data[].attributes.body
AI intelligence can solve many challenges we face in modern healthcare

# data[].attributes.headline
Encoding the Human Brain into Digital Data 

# data[].attributes.subject
Encoding the Human Brain into Digital Data 

# data[].attributes.attachment_caption
A synthetic healthy human brain generated by King’s College London and NVIDIA AI models.

# data[].attributes.attachment_caption
Scientists create artificial brain that can 'think' and 'feel' just like humans [cited 2023 Mar 28]

# data[].attributes.attachment_link.readable_text
(Image: Penn State)

A team of scientists have created a material that can 'feel' things and translate them into information just like humans.

Researchers in Pennsylvania, USA, have created a rubber-like material which 'acts like a brain'.

It's capable of taking in information from the real world, analysing it, and taking action on it. The scientists behind the discovery believe it will make engineering materials and robots even more intelligent by giving them physical 'senses'.
R EAD NEXT: Amazon's creepy Alexa feature that mimics the voices of dead loved ones
Professor Ryan Harne, who led the Penn State University study, said that his team's invention is the first to allow any material to 'think' about what's happening around it.

Harne said: "We have created the first example of an engineering material that can simultaneously sense, think and act upon mechanical stress without requiring additional circuits to process such signals.

"The soft polymer material acts like a brain that can receive digital strings of information that are then processed, resulting in new sequences of digital information that can control reactions."

It might sound a bit techie, but Harne is calling it a "fundamental form of intelligence" because it means machines can use a similar thinking process to humans.

"What makes humans smart is our means to observe and think about information we receive through our senses, reflecting on the relationship between that information and how we can react." The material his team created, he argues, can do the same thing.

Indeed, the material has hundreds of different uses as it can translate different inputs into outputs.

Harne and his team demonstrated that it could interpret 'mechanical force' such as in a factory or manufacturing plant and translate this into hard sums and data.
READ MORE:
They also claim it can detect and interpret radio waves and use these to communicate through different light signals, or even be used in AI-powered search and rescue systems.

# data[].attributes.attachment_link.title
Scientists create artificial brain that can 'think' and 'feel' just like humans

# data[].attributes.attachment_link.description
A team of scientists have created a material that can 'feel' things and translate them into information just like humans. Researchers in Pennsylvania, USA, have created a rubber-like material which 'acts like a brain'. It's capable of taking in information from the real world, analysing it, and taking action on it.

# data[].attributes.attachment_link.title
This brain-inspired material may be able to adapt to its environment

# data[].attributes.attachment_link.description
A resilient new material inspired by AI can adapt to unexpected shifts in force, making it impressively strong and energy-efficient.

# data[].attributes.body
Researchers in USA developed a material that can transduct sensation into information, made from soft, rubber-like material

# data[].attributes.attachment_caption
Synthetic Brains NSF - National Science Foundation. [cited 2023 Mar 14]. 

# data[].attributes.body
3D modelling material, has the opportunity for synapse-like connections in all directions

# data[].attributes.attachment_link.readable_text
SEE ALSO: THE "SQUISHY" FUTURE OF MIND CONTROL BRAIN-COMPUTER INTERFACES
Interfacing electronics with human tissue proved to be a major challenge as traditional materials such as silicon, steel, and gold lead to scar tissue formation in their implantation site.

While scar tissue is a problem in itself at certain times, it also poses an additional challenge due to reduced electrical conductivity of the affected area. So, computer to human electrical information transfer remains largely unviable with these materials. Researchers aim to overcome this issue by using various polymers.

The lead of the study Dr. David Martin said "We got the idea for this project because we were trying to interface rigid organic microelectrodes with the brain, but brains are made out of organic, salty, live materials,"

It did not go well at first so they thought they could come up with a better way to do this. Martin continues "We started looking at organic electronic materials like conjugated polymers that were being used in non-biological devices. We found a chemically stable example that was sold commercially as an antistatic coating for electronic displays"

# data[].attributes.attachment_link.title
Bio-Synthetic Material Could Pave the Way for Reliable Computer-Brain-Interfaces

# data[].attributes.attachment_link.description
SEE ALSO: THE "SQUISHY" FUTURE OF MIND CONTROL BRAIN-COMPUTER INTERFACES Interfacing electronics with human tissue proved to be a major challenge as traditional materials such as silicon, steel, and gold lead to scar tissue formation in their implantation site.

# data[].attributes.headline
What materials do we need to replicate neurons?

# data[].attributes.subject
What materials do we need to replicate neurons?

# data[].attributes.body
Since neurons are the important element in relaying information in the brain, building a synthetic brain can start by designing our own neurons.

# data[].attributes.body
Neurons allow different areas of the brain to communicate and relay information to the nervous system.

# data[].attributes.body
Modelling a synthetic brain requires thorough understanding of the structure of the actual human brain