# data[].attributes.headline
On the way, I saw this interesting articles...

# data[].attributes.subject
On the way, I saw this interesting articles...

# data[].attributes.body
This Ted Talk was about a computer being a brain. Kwabena Boahen talks about how the computer processes and compared to the brain. He also states that maybe in the future there will be computers that works like a brain.

# data[].attributes.attachment_link.title
Kwabena Boahen: A computer that works like the brain

# data[].attributes.attachment_link.description
Researcher Kwabena Boahen is looking for ways to mimic the brain's supercomputing powers in silicon -- because the messy, redundant processes inside our heads actually make for a small, light, superfast computer.

# data[].attributes.body
Scrolling down the New York Time articles about brain filters, this article caught my attention on the spot. This article talks about how our brain can be computers and it provided many examples proving this point.

# data[].attributes.attachment_link.readable_text
Gray Matter

Credit... Gérard DuBois

SCIENCE has a poor track record when it comes to comparing our brains to the technology of the day. Descartes thought that the brain was a kind of hydraulic pump, propelling the spirits of the nervous system through the body. Freud compared the brain to a steam engine. The neuroscientist Karl Pribram likened it to a holographic storage device.

Many neuroscientists today would add to this list of failed comparisons the idea that the brain is a computer - just another analogy without a lot of substance. Some of them actively deny that there is much useful in the idea; most simply ignore it.

Often, when scientists resist the idea of the brain as a computer, they have a particular target in mind, which you might call the serial, stored-program machine. Here, a program (or "app") is loaded into a computer's memory, and an algorithm, or recipe, is executed step by step. (Calculate this, then calculate that, then compare what you found in the first step with what you found in the second, etc.) But humans don't download apps to their brains, the critics note, and the brain's nerve cells are too slow and variable to be a good match for the transistors and logic gates that we use in modern computers.

If the brain is not a serial algorithm-crunching machine, though, what is it? A lot of neuroscientists are inclined to disregard the big picture, focusing instead on understanding narrow, measurable phenomena (like the mechanics of how calcium ions are trafficked through a single neuron), without addressing the larger conceptual question of what it is that the brain does.

This approach is misguided. Too many scientists have given up on the computer analogy, and far too little has been offered in its place. In my view, the analogy is due for a rethink.

To begin with, all the standard arguments about why the brain might not be a computer are pretty weak. Take the argument that "brains are parallel, but computers are serial." Critics are right to note that virtually every time a human does anything, many different parts of the brain are engaged; that's parallel, not serial.

But the idea that computers are strictly serial is woefully out of date. Ever since desktop computers became popular, there has always been some degree of parallelism in computers, with several different computations being performed simultaneously, by different components, such as the hard-drive controller and the central processor. And the trend over time in the hardware business has been to make computers more and more parallel, using new approaches like multicore processors and graphics processing units.

Skeptics of the computer metaphor also like to argue that "brains are analog, while computers are digital." The idea here is that things that are digital operate only with discrete divisions, as with a digital watch; things that are analog, like an old-fashioned watch, work on a smooth continuum.

But just as either format is possible for a watch, either format is possible for a computer, and many "digital" computer switches are built out of analog components and processes. Although virtually all modern computers are digital, most early computers were analog. And we still don't really know whether our brains are analog or digital or some mix of the two.

Finally, there is a popular argument that human brains are capable of generating emotions, whereas computers are not. But while computers as we know them clearly lack emotions, that fact itself doesn't mean that emotions aren't the product of computation. On the contrary, neural systems like the amygdala that modulate emotions appear to work in roughly the same way as the rest of the brain does, which is to say that they transmit signals and integrate information, and transform inputs into outputs. As any computer scientist will tell you, that's pretty much what computers do.

Of course, whether the brain is a computer is partly a matter of definition. The brain is obviously not a Macintosh or a PC. And we humans may not have operating systems, either. But there are many different ways of building a computer.

The real payoff in subscribing to the idea of a brain as a computer would come from using that idea to profitably guide research. In an article last fall in the journal Science, two of my colleagues (Adam Marblestone of M.I.T. and Thomas Dean of Google) and I endeavored to do just that, suggesting that a particular kind of computer, known as the field programmable gate array, might offer a preliminary starting point for thinking about how the brain works.

FIELD programmable gate arrays consist of a large number of "logic block" programs that can be configured, and reconfigured, individually, to do a wide range of tasks. One logic block might do arithmetic, another signal processing, and yet another look things up in a table. The computation of the whole is a function of how the individual parts are configured. Much of the logic can be executed in parallel, much like what happens in a brain.

Although my colleagues and I don't literally think that the brain is a field programmable gate array, our suggestion is that the brain might similarly consist of highly orchestrated sets of fundamental building blocks, such as "computational primitives" for constructing sequences, retrieving information from memory, and routing information between different locations in the brain. Identifying those building blocks, we believe, could be the Rosetta stone that unlocks the brain.

To put this differently, it is unlikely that we will ever be able to directly connect the language of neurons and synapses to the diversity of human behavior, as many neuroscientists seem to hope. The chasm between brains and behavior is just too vast.

Our best shot may come instead from dividing and conquering. Fundamentally, that may involve two steps: finding some way to connect the scientific language of neurons and the scientific language of computational primitives (which would be comparable in computer science to connecting the physics of electrons and the workings of microprocessors); and finding some way to connect the scientific language of computational primitives and that of human behavior (which would be comparable to understanding how computer programs are built out of more basic microprocessor instructions).

If neurons are akin to computer hardware, and behaviors are akin to the actions that a computer performs, computation is likely to be the glue that binds the two.

There is much that we don't know about brains. But we do know that they aren't magical. They are just exceptionally complex arrangements of matter. Airplanes may not fly like birds, but they are subject to the same forces of lift and drag. Likewise, there is no reason to think that brains are exempt from the laws of computation. If the heart is a biological pump, and the nose is a biological filter, the brain is a biological computer, a machine for processing information in lawful, systematic ways.

The sooner we can figure out what kind of computer the brain is, the better.

# data[].attributes.attachment_link.title
Opinion | Face It, Your Brain Is a Computer

# data[].attributes.attachment_link.description
Gray Matter SCIENCE has a poor track record when it comes to comparing our brains to the technology of the day. Descartes thought that the brain was a kind of hydraulic pump, propelling the spirits of the nervous system through the body. Freud compared the brain to a steam engine.

# data[].attributes.body
This article talks about what part of the brain stores memory. I used this article to explain the part of the brain that hold memory. "There also seems to be a functional separation between left and right sides of the PFC: the left is more involved in verbal working memory while the right is more active in spatial working memory..."

# data[].attributes.attachment_link.readable_text
Memories aren't stored in just one part of the brain. Different types are stored across different, interconnected brain regions. For explicit memories - which are about events that happened to you (episodic), as well as general facts and information (semantic) - there are three important areas of the brain: the hippocampus, the neocortex and the amygdala. Implicit memories, such as motor memories, rely on the basal ganglia and cerebellum. Short-term working memory relies most heavily on the prefrontal cortex.

The parts of the brain involved in memory (Illustration by Levent Efe)

Explicit memory

There are three areas of the brain involved in explicit memory: the hippocampus, the neo-cortex and the amygdala.

Hippocampus

The hippocampus, located in the brain's temporal lobe, is where episodic memories are formed and indexed for later access. Episodic memories are autobiographical memories from specific events in our lives, like the coffee we had with a friend last week.

How do we know this? In 1953, a patient named Henry Molaison had his hippocampus surgically removed during an operation in the United States to treat his epilepsy. His epilepsy was cured, and Molaison lived a further 55 healthy years. However, after the surgery he was only able to form episodic memories that lasted a matter of minutes; he was completely unable to permanently store new information. As a result, Molaison's memory became mostly limited to events that occurred years before his surgery, in the distant past. He was, however, still able to improve his performance on various motor tasks, even though he had no memory of ever encountering or practising them. This indicated that although the hippocampus is crucial for laying down memories, it is not the site of permanent memory storage and isn't needed for motor memories.

The study of Henry Molaison was revolutionary because it showed that multiple types of memory existed. We now know that rather than relying on the hippocampus, implicit motor learning occurs in other brain areas - the basal ganglia and cerebellum.

Neocortex

The neocortex is the largest part of the cerebral cortex, the sheet of neural tissue that forms the outside surface of the brain, distinctive in higher mammals for its wrinkly appearance. In humans, the neocortex is involved in higher functions such as sensory perception, generation of motor commands, spatial reasoning and language. Over time, information from certain memories that are temporarily stored in the hippocampus can be transferred to the neocortex as general knowledge - things like knowing that coffee provides a pick-me-up. Researchers think this transfer from hippocampus to neocortex happens as we sleep.

Amygdala

The amygdala, an almond-shaped structure in the brain's temporal lobe,attaches emotional significance to memories. This is particularly important because strong emotional memories (e.g. those associated with shame, joy, love or grief) are difficult to forget. The permanence of these memories suggests that interactions between the amygdala, hippocampus and neocortex are crucial in determining the 'stability' of a memory - that is, how effectively it is retained over time.

There's an additional aspect to the amygdala's involvement in memory. The amygdala doesn't just modify the strength and emotional content of memories; it also plays a key role in forming new memories specifically related to fear. Fearful memories are able to be formed after only a few repetitions. This makes 'fear learning' a popular way to investigate the mechanisms of memory formation, consolidation and recall. Understanding how the amygdala processes fear is important because of its relevance to post-traumatic stress disorder (PTSD), which affects many of our veterans as well as police, paramedics and others exposed to trauma. Anxiety in learning situations is also likely to involve the amygdala, and may lead to avoidance of particularly challenging or stressful tasks.

QBI researchers including Professor Pankaj Sah and Dr Timothy Bredy believe that understanding how fear memories are formed in the amygdala may help in treating conditions such as post-traumatic stress disorder.

Implicit memory

There are two areas of the brain involved in implicit memory: the basal ganglia and the cerebellum.

Basal ganglia

The basal ganglia are structures lying deep within the brain and are involved in a wide range of processes such as emotion, reward processing, habit formation, movement and learning. They are particularly involved in co-ordinating sequences of motor activity, as would be needed when playing a musical instrument, dancing or playing basketball. The basal ganglia are the regions most affected by Parkinson's disease. This is evident in the impaired movements of Parkinson's patients.

Cerebellum

The cerebellum, a separate structure located at the rear base of the brain, is most important in fine motor control, the type that allows us to use chopsticks or press that piano key a fraction more softly. A well-studied example of cerebellar motor learning is the vestibulo-ocular reflex, which lets us maintain our gaze on a location as we rotate our heads.

Working memory

Prefrontal cortex

The prefrontal cortex (PFC) is the part of the neocortex that sits at the very front of the brain. It is the most recent addition to the mammalian brain, and is involved in many complex cognitive functions. Human neuroimaging studies using magnetic resonance imaging (MRI) machines show that when people perform tasks requiring them to hold information in their short-term memory, such as the location of a flash of light, the PFC becomes active. There also seems to be a functional separation between left and right sides of the PFC: the left is more involved in verbal working memory while the right is more active in spatial working memory, such as remembering where the flash of light occurred.

# data[].attributes.attachment_link.description
Memories aren't stored in just one part of the brain. Different types are stored across different, interconnected brain regions. For explicit memories - which are about events that happened to you (episodic), as well as general facts and information (semantic) - there are three important areas of the brain: the hippocampus, the neocortex and the amygdala.

# data[].attributes.attachment_link.readable_text
No, it's not a metaphor

Neuroscience is a funny discipline which can demand a level of interdisciplinary knowledge that is hard to achieve. At its heart, neuroscience is concerned with understanding the organ that is responsible for generating our behaviour, and thus, it is a branch of physiology and psychology. At the same time, most neuroscientists will have heard, or even used, the words "calculate", "algorithm", and "computation" many times in their professional lives. I think there's a good reason for this: the brain is a computer, and neuroscience is also branch of computer science, in my opinion. However, many neuroscientists do not see it that way.

In online discussions I have often read the phrase " The Brain as a Computer Metaphor". The implication of this phrase is clear: the brain is not a computer, and at best, we can use computers as a metaphor to understand the brain. (At worst, the "metaphor" does not hold and should be abandoned.) Similarly, I have heard neuroscientists say things like, " neural circuits don't truly run algorithms ". Again, the conclusion is clear: the brain doesn't run any algorithms in reality, so our constant use of the words "algorithm" and "computer" when talking about the brain is misguided.

Unfortunately, what these discussions demonstrate is that many researchers do not, as a rule, actually understand the formal definitions of "computer" or "algorithm" as provided by computer science. (Or alternatively, if they do understand them, they don't accept them for some reason.) If you understand the formal definitions of computer and algorithm as given by computer science, then you know that the brain is very clearly a computer running algorithms, almost trivially so.

My goal in this article is to provide a brief, intuitive explanation for those formal definitions of "computer" and "algorithm", one pitched at neuroscientists. My hope is that this will help neuroscientists to move away from these confused, misleading discussions about whether or not a computer (the brain) is actually a computer, because as others have noted, it most certainly is.

I want to make clear that my goal here is not to engage with the much larger, more complicated discussions in cognitive science and philosophy of mind as to whether we can understand mentation using computational frameworks. I consider that to be a separate question regarding the nature of the mind, and there, the discussions get much more complicated. I personally think that computer science has a lot to contribute to neuroscience (indeed, as I said above, I consider neuroscience to be a branch of computer science), and I happen to think computational frameworks can explain mental activity, but that's not my target here. Here, I seek only to articulate why, if we ignore questions regarding the nature of the mind, then according to first principles and formal definitions, the brain necessarily is a computer, and there really is no discussion to be had once you understand and accept the definitions.

Defining an algorithm

Okay, what is an "algorithm" and what is a "computer"? The formal definitions for these words originate in the work of mathematicians in the early half of the 20th century. Back then, many mathematicians were concerned with some questions that David Hilbert had put forward in a very famous lecture he delivered in 1900 on 23 Mathematical Problems to be Solved in the Next Century.

One of those problems, the " 10th problem", was concerned with determining whether a polynomial (e.g. 6x³yz⁴ + 4y²z + z - 9) had roots made up solely of integers (e.g. x = 5, y = -3, z = 0, etc.). Hilbert wanted mathematicians to develop an effective method, a "recipe", for deciding whether this was the case for any arbitrary polynomial. Such a recipe is, colloquially, what we call an "algorithm". That's the intuitive definition of algorithm: an algorithm is a finite set of instructions that can be followed mechanically, with no insight required, in order to give some specific output (e.g. an answer to yes/no integer roots) for a specific input (e.g. a specific polynomial like 6x³yz⁴ + 4y²z + z - 9). To put this into context, what was desired at this point in history was a way to specify how any human being could sit down at a desk with a paper and pencil and work out the answer to something without having to actually generate any novel insight. So, for example, long division provides an algorithm for calculating the result of dividing one number by another - you can just sit there with a paper and pencil and follow the finite set of instructions like an automaton, and you will arrive at the correct result. Hilbert assumed there must be an algorithm for the particular task of polynomial integral roots, and the job for mathematicians was simply to find it.

As luck would have it, Hilbert had stumbled onto a very deep problem. Mathematicians took on his challenge, but they had a hard time coming up with a recipe. More and more, various mathematicians began to ask whether some problems in mathematics, including Hilbert's "10th problem" were, in fact, not decidable, meaning there was no algorithm for solving them. Of course, mathematicians being mathematicians, they desired proofs that an algorithm didn't exist for these problems. The issue was that with only an informal, intuitive definition of the word "algorithm", proving that no algorithm existed for a given problem was basically impossible.

Given this, Alonzo Church and Alan Turing set about to develop a formal definition for algorithms. The two researchers worked independently, and came to different solutions, but their solutions turned out to be mathematically equivalent. Church invented lambda calculus, and defined an algorithm as anything that could be done with lambda calculus. Turing invented Turing machines, and defined an algorithm as anything that could be done with Turing machines. I will leave it to the reader to learn more about the specific definitions of lambda calculus and Turing machines, but it is important to understand that both are simply a mathematical tool. This is fairly obvious with lambda calculus, but often people get confused about Turing machines because of the word "machine" - confusingly, Turing machines are not machines. Indeed, the definition assumes some very unrealistic components (like an infinite tape). It is important to think of Turing machines as just a mathematical tool. I draw attention to this because I will return to it when we discuss the relationship between Turing machines and brains.

As mentioned, it turned out that Church and Turing's definitions were equivalent, meaning that anything which you could do with lambda calculus you could do with a Turing machine. As time has gone on, empirical experience has suggested that any attempt to formalize the intuitive definition of algorithm will settle on something that is equivalent to Turing machines. Therefore, most computer scientists today accept something known as the Church-Turing thesis:

The Church-Turing thesis:
Any algorithm can be implemented by a Turing machine.

It should be recognized that this statement can actually be viewed as either a definition,or a hypothesis. If we view it as a hypothesis, then the Church-Turing thesis hypothesizes that any problem we have a means of solving using only a finite set of instructions and no insight, will turn out to be implementable by a Turing machine, i.e. it hypothesizes that our intuitive definition is covered by Turing's definition (see e.g. page 183 here). That leaves open the possibility that someday the Church-Turing thesis will be violated. However, Church and Turing were seeking to formalize the concept of "algorithm". Moreover, the Church-Turing thesis is generally taken as a given by researchers, so when people seek a proof that there is no algorithm for some function, they sometimes do so by proving that you can't do it with a Turing machine. Therefore, it is safe to say that it has become a definition in computer science, meaning that the formal definition of algorithm is given by the Church-Turing thesis:

The Church-Turing thesis (as definition):
An algorithm is anything that a Turing machine can do.

With the definition of algorithm in hand, we can now define "computable functions" and "computer". A "computable function" is defined as any task that can be solved using an algorithm. A "computer" is then defined as any physical machinery that can solve computable functions via algorithms. If we think back to the use of the word "computer" at this point in history, this makes sense. "Computers" at this time were people whose job was to sit down with pencil and paper and use an algorithm to solve computable functions (e.g. to integrate some equation). Clearly, these people were computers according to this definition, because they were providing solutions to computable functions via algorithms.

Importantly, there are many processes that consist of a finite set of instructions, but not discrete, step-by-step instructions, and which can still be done with a Turing machine. This is where things get confusing. Consider, for example, an analog audio system. Many people mistakenly think that Turing machines cannot implement analog functions because they are not defined by a discrete set of step-by-step values. But remember, Turing machines are not machines. They're abstract mathematical constructs that can operate with infinite memory and no time restrictions (any finite amount of time counts, no matter how large). As such, an analog system can be implemented by a Turing machine to any desired level of precision. That's why we consider all of the functions that audio systems perform to be computable functions ( and why we can simulate them well). Of course, it must be recognized that if we require infinite precision for an analog function, then it isn't computable, but that is rarely a situation we are faced with practically.

Indeed, any procedure for solving a computable function can be implemented by a Turing machine, by definition, no matter the specific mechanism involved. Part of the reason a lot of confusion sets in, even amongst really smart people, is that when you study Turing machines and computable functions in school as a computer science student, the concern is rarely with analog, or stochastic, or distributed processes. Example after example are deterministic step-by-step algorithms that are easy to teach with. That's good for learning the basics, but it leads many people to mistakenly think that any system which isn't clearly a step-by-step, discrete system can't be running algorithms, and that's simply not true.

That's not to say that Turing machines can do everything. For example, it has been proven mathematically that there are some problems that Turing machines can't do, like Hilbert's 10th problem. But the reason a Turing machine cannot do some things is not because those things are analog, or stochastic, or distributed, etc. It's because there are certain limits to mechanical, axiomatic methods - some questions simply have no concrete answer within mathematics as we know it.

To summarize the formal definitions:
(1) an algorithm is anything a Turing machine can do,
(2) computable functions are defined as those functions that we have algorithms for,
(3) a computer is anything which physically implements algorithms in order to solve computable functions.

Is everything a computer?

Given these definitions, is the brain a computer? Most certainly, because our brains implement various algorithms to solve computable functions, ranging from the obviously computable (you can mentally sort a list, pick which of two numbers is larger, multiply two numbers, etc.) to the less obviously, but still, computable (you can run, talk, play music, etc.). However, a big issue with this approach, and more broadly with the definitions provided above, is that they can be applied to almost anything. For example, the macroscopic laws of physics are computable functions (e.g. a parabolic path is computable). Therefore, technically, every object in the universe, every planet, every rock, every feather, every snowflake, every grain of sand, etc., can be viewed as a computer, implementing algorithms to solve the functions that describe that object's evolution through time. That would appear to render the definitions above so broad as to be useless.

In my opinion (and as a subscriber to Wittgenstein's later philosophies), the way to contend with this problem is to examine our usage of the word "computer". What do we usually use this word for, and how do computer scientists use this word? Can we generate some mapping between the two that renders our usage non-trivial? Obviously, we are all comfortable with calling the machines we now carry in our pockets and keep on our desks "computers". That's how we usually use the word, e.g. when we get a new laptop we say "I just bought a new computer", rather than, "I just bought a new Von Neumann architecture programmable machine" (which is a reasonable description of what they are). And it's not just the average person: computer scientists also use the word "computer" to refer to these machines. So, what is it about these machines that makes this usage less trivial than, say, calling every grain of sand a "computer"? Is this usage actually grounded in a scientific, formal language game, or is it just a funny quirk of our everyday, non-scientific language games?

There are very good, formal reasons to use the word "computer" when referring to our laptops and phones. One of the things that Turing proved was that some Turing machines could be used to implement any other Turing machine. These are called universal Turing machines. Now, remember, Turing machines are not actually machines, they're a formalism, a mathematical tool. A universal Turing machine is a mathematical tool that can be used to perform the functions of any other mathematical tool that is also a Turing machine. Moreover, a mathematical tool that can be proven to be capable of implementing any algorithm is called " Turing complete". For example, the programming languages familiar to us all (C++, Python, etc.) are Turing complete, because it can be proven that any algorithm can be programmed with these languages.

Given this, we can see why the machines we apply the word "computer" to deserve their name. These machines provide a physical substrate for running algorithms using Turing complete programming languages. This means that, with the right programming, they can solve any computable function for us. (Note: this does not provide a practical guarantee, e.g. it may take billions or trillions of years for any given function to run in any given language. The question of practical computation is addressed by computational complexity theory.) As such, these machines that are now ubiquitous in our lives are a much more powerful form of computer than a stone or a snowflake, which are limited to computing only the functions of physics that apply to their movement. Your laptop is an "uber-computer", as it were, and therefore, arguably deserves the title "computer" much more than any other object in your house (except maybe you).

What about brains then?

What does all this business about Turing completeness have to do with brains? As many people have noted, the internal workings of brains are nothing like the internal workings of the Von Neumann architecture machines that we usually call computers. So, surely brains aren't computers, right? Well, no, brains are also "uber-computers", they just work in a different manner.

First, we can consider the anecdotal evidence. Consider all the functions that an adult human with sufficient education is capable of implementing. It's a pretty big list! In fact, with no more than a pencil and piece of paper, any human is arguably capable of running any program that has been programmed in a language like Python. Even without a pencil and paper, you can implement a huge swathe of functions that well outstrips the functions that a stone, a chair or a thermometer, say, can implement.

Second, we can consider the formal evidence. Computer scientists have demonstrated that multilayer and recurrent artificial neural networks are Turing complete, meaning you can implement any algorithm with them. That's one of the reasons why we can do all sorts of impressive stuff with neural networks! This doesn't mean that any given neural network can compute any function, though. Rather, it means that for any given function there exists a neural network that can compute it.

Put another way, we can think of neural networks as a sort of programming language: the synaptic connections in a neural network define the functions that they implement, so the set of all possible neural network architectures is effectively a programming language, and this language is Turing complete. Although there are many differences between artificial neural networks and brains, arguably those differences only render real brains more computationally powerful than artificial neural networks. This means that when we consider the set of all possible brains, then for any given computable function, there is probably a hypothetical brain that can solve it. Thus, the "language of brains", as it were, is probably Turing complete.

Moreover, we can make some stronger statements about individual animals, including of course, humans. Our brains have roughly 10¹⁴ synapses, and there is a surprising amount of flexibility in how those synapses are set. This means that we can implement many, many different functions. Is any individual human Turing complete, then, i.e. can we implement the entire set of computable functions? Probably, if we were immortal, but practically, no. However, the same caveat applies to our laptops and phones. The languages they can be programmed with are Turing complete, but like us, they would require immortality to guarantee that they could implement any computable function. Thus, the set of functions that we can implement as humans is not radically different in scope than the set of functions that our trusty old Von Neumann architecture machines can implement.

Of course, not all animals are as flexible as humans. I worked for many years with the toad species Xenopus laevis, and I can tell you, they're much more limited in their behavioural repertoire than humans. Nonetheless, they can still do a hell of a lot more than a stone. Any animal can, thanks to their brains. Rodents can replicate spatial probability distributions in their searches and optimize their strategies in oppositional games. C. elegans implement a Markov model in their search behaviour. Because these functions are computable (e.g. solvable with a Turing machine) we can say these animals are running algorithms.

For example, one of the most appropriate uses of the word "algorithm" in recent neuroscience work came from Ortezia et al. They demonstrated that zebrafish embryos use the following recipe for rheotaxis:

Determine the change in the gradient of water flow.

If the gradient decreases, swim straight.

If the gradient increases, turn in the direction of flow field rotation.

That's an algorithm, both intuitively and formally. It's a finite set of instructions to solve a given function, and it can be implemented with a Turing machine. Of course, the zebrafish probably isn't engaged in a step-by-step program that looks anything like a computer program on your laptop. It must be using analog sensors, an array of neurons with fluctuating voltages, stochastic transmitter release, etc. Nonetheless, Ortezia et al. are right, the zebrafish is using an algorithm to solve this ecologically relevant problem. That makes the zebrafish embryo's nervous system a computer.

Hence, when we consider both the formal definitions and the common usage of the word "computer", it is perfectly reasonable to say that the brain is a computer. Any brain is a sophisticated device capable of implementing a very large set of algorithms to solve many different computable functions. It should be noted that where we draw the line is unclear, e.g. is a single cell a computer? What about DNA? Both can implement more functions than a rock. We have to accept that there is a subjective decision to be made in these borderline cases. The definitions do not say how many algorithms a machine needs to implement to be a computer. But, given that the set of neural networks is Turing complete (and therefore also probably the set of possible brains), it is pretty clear that brains deserve the title "computer". Therefore, the brain is a computer.

It's not a metaphor, it's not an analogy, it's a fact.

When I've advanced this argument in public, I've encountered a number of objections, some very strong and worth considering, others indicating a basic misunderstanding of the logic. I'll try to briefly contend with the common objections I've encountered here, moving from objections founded on misunderstanding to valid objections that need to be contended with:

Objection 1. Brains are nothing like Turing machines (or laptops), therefore they're not computers

This objection is borne of a misunderstanding of the definitions above. Nothing in the definition of "algorithm", "computable function", or "computer" states that computers must be like Turing machines or Von Neumann architecture machines. A computer need not operate with binary codes, it doesn't need to use discrete-time processes, it doesn't need registers or memory banks, or step-by-step instructions - it doesn't need to be remotely like our laptops of phones for the definitions to apply.

The definitions simply say that algorithms are things that you can do with a Turing machine, and computers are things that run algorithms. Importantly, those definitions do not state any practical limits on how a Turing machine could do it. So, for example, if you have an algorithm that uses analog, continuous processes (like the zebrafish algorithm for swimming upstream probably does), then the Turing machine corresponding to it will have to run for potentially very long in order to approximate that analog process to sufficient precision. But, Turing machines aren't real machines! They're mathematical constructs, so there's no problem with this. Nothing in the definition of "algorithm" says that it must be economical to implement an algorithm with a Turing machine. As long as you are solving computable functions, you've got yourself a computer running algorithms. Thus, it doesn't matter that the brain is nothing like a Turing machine (nothing is, really) or nothing like your laptop. Yes, the brain is a jumble of cells using voltages, neurotransmitters, distributed representations, etc. Yes, it has no programmer, and yes it is shaped by evolution and life experience. None of that matters. As long as we don't require infinite precision in our description of neural processes (and I see no reason to think we would), then the brain is a computer running algorithms, according to the definitions provided by the Church-Turing thesis.

Objection 2. Just because you can simulate the brain with a Turing machine doesn't make the brain a computer

This objection is related to the first objection, in that it stems from a failure to understand the definitions and the role of Turing machines in those definitions. The thrust of this objection is that even though we can run a Turing machine for as long as we want in order to implement analog processes, we're really just simulating those processes with a Turing machine, so we cannot say that they count as something a Turing machine can do. Thus, Turing machine can't actually do what brains do, so brains aren't running algorithms.

At the risk of sounding like a broken record, the point that must be returned to here is that Turing machines are not machines. We don't simulate anything with Turing machines, just as we don't "simulate" things with lambda calculus. We use Turing machines to mathematically determine whether a particular process counts as an algorithm or not. Therefore, if we can demonstrate mathematically that a given analog process can be implemented by a Turing machine to any arbitrary degree of precision, then we have demonstrated that we have an algorithm. Yes, we simulate many things, including brains, with our Von Neumann machines. But, Von Neumann machines are not Turing machines. It's important to remember this, because many people fall into the trap of thinking that computers are defined as Turing machines, when they most certainly are not. No physical object is a Turing machine, just as no physical object is an algebra.

Objection 3. Computers cannot explain the mind, therefore the brain is not a computer

This one is tough because it is a thorny philosophical argument, and how one reacts to it depends on one's philosophical stances. Many philosophers have argued that purely mechanical accounts of the brain cannot account for the mind, and given that the definition of computer provided above is very much mechanical, it is true that if the mind cannot be accounted for by purely mechanical means, then there must be something missing when I say "the brain is a computer".

Ultimately, as I noted in the introduction, I do not have time to contend with the long debates on this issue in philosophy of mind and cognitive science here. All I will say, is that I personally find all of the philosophical objections to the idea that the mind can arise from mechanical processes unconvincing. Whether it's Searle's Chinese Room Argument, Chalmer's zombies, or Jackson's Mary the Scientist, I've never been persuaded that there's anything really worthy of serious consideration for neuroscientists in these thought experiments. I'm much more convinced by the works of philosophers like Daniel Dennett and Patricia Churchland, who seem much more clear-eyed and logical to me. But hey, that's my perspective. If someone rejects the idea that the mind can be reduced to physical mechanism, then I recognize that my arguments above are unconvincing. But, anyone who is comfortable with reductionism, and who understands the definitions above, should also recognize that the brain is a computer by definition.

Objection 4. Brains can implement non-computable functions, therefore the brain is not a computer

This is a very interesting argument that has been advanced by various people. The physicist Roger Penrose has claimed that humans have various insights and behaviours that require non-Turing computation and quantum effects for explanation. Like Dennett, I consider Penrose's claims questionable. It's not clear from scientific evidence that human behaviour requires any radical rethinking of computation or physiology.

But, there are other more serious attempts in this vein. Notably, Hava Siegelmann, who provided one of the proofs that recurrent neural networks are Turing complete, also published an article in Science in which she argued that recurrent neural networks can, in fact, be super Turing. This means that they can solve the set of computable functions (as defined by Turing machines) and more, i.e. they can implement algorithms that Turing machines cannot. It's a very interesting result. I will admit that I find Siegelmann's proof in that paper hard to understand, so I prefer to remain agnostic as to whether this is an accepted fact. (I will note though, that if Sigelmann's proof is valid, it calls for a revision of the Church Turing thesis, so I wonder why computer science has not apparently taken this leap.)

But, for argument's sake, let's say that Siegelmann's proof is correct, and that recurrent neural networks are super Turing. That would suggest that brains are also super Turing. If the space of possible brains is super Turing, then someone, someday, could engage in computations that no laptop could ever do. In that case, arguably, the brain is not just a computer, it's a super computer. This would only make my case that the brain is a computer stronger.

Objection 5. The brain has many constraints placed on it by evolution that would prevent it from being Turing complete

This objection raises some important points. Certainly, we know that evolution can take odd paths to get to certain physiological functions, and brains are likely no exception. Moreover, brains face major energy constraints, both spatial and temporal, and memory capabilities limited by the size of our mnemonic networks. Thus, the chances that the entire set of computable functions could be implemented by any real brain are zero.

All of this is true, but it fails to recognize that many of the same things could be said about our trusty Von Neumann machines. Though we seem to have little concern for our energy usage as a species, the reality is that we cannot devote infinite energy to our digital computers. Moreover, although the cloud is growing rapidly, we still don't have infinite memory capabilities (at least, not us mere mortals who don't run big tech companies). As such, your laptop also has many functions that it cannot calculate for you given reasonable energy and memory constraints. That doesn't stop you from calling your laptop a "computer", because it is still an incredibly powerful machine for solving computable functions with algorithms, just like our brains.

Of course, the energetic and evolutionary constraints that impinge on brains are very different from the constraints on our Von Neumann machines, and that is also important. As we compile the principles of neural computation in the coming century, some of those principles will surely have to do with the unique constraints that brains face. But, that doesn't render brains non-computers, it makes them a special kind of computer, one shaped by evolution.

Objection 6. These definitions are useless and potentially misleading

This objection is potentially valid. As I noted, the technical definitions of "computer" and "algorithm" are so abstract that they can be applied to everything in the universe. As I argued above, I think it's reasonable to restrict their usage to machines, like the brain, that not only solve the functions of physics, but a much larger array of computable functions, potentially even all of them (assuming the space of possible brains is Turing complete). But, what do we achieve by applying the words "computer" and "algorithm" to the brain? How does that help us to understand the brain, given that the formal definitions are mechanism agnostic? If many people do not know these formal definitions, and they think that "computers" are Von Neumann architecture machines and "algorithms" are discrete step-by-step instructions for Von Neumann architecture machines, then we run the risk of grossly misleading these people when we use these words in relation to the brain. My response to this objection is twofold.

First, there are many terms that scientists use formal definitions for, but which many people misuse. For example, the term "significant" means something very specific in statistical hypothesis testing, even though its usage in common language means anything that's "big", "meaningful", or "impactful". Should we stop using the term "significant", then? I don't think so. I think we merely need to be careful to articulate clearly to people what we mean when we use this word.

I think it's the same with "computer" and "algorithm". When we use these words, we need to be clear what definitions we're operating with. If speaking to the lay public, that means highlighting the fact that the brain is not a computer that is anything like their laptops or phones, lest they think otherwise. If speaking to other scientists, it means articulating clearly the formal definitions and asserting only that the brain implements a large array of algorithms to solve many different computable functions.

Second, I think we actually gain something when we say the brain is a computer. There is a rich vein of theories in computer science that are mechanism agnostic, including computability theory, computational complexity theory, and information theory. When we note that the brain is a computer, we are highlighting that these theories are applicable to the brain - which they are! Neuroscientists can, and do, examine how the brain implements various algorithms, how it deals with computationally intractable functions, and how it does things like store, compress or transmit information. All of this scientific activity is useful, and it derives from our understanding that the brain is a computer.

The desire to reject the statement "the brain is a computer" out of fear that it misleads those of us who don't quite understand or know the definitions is throwing the baby out with the bathwater. Can we not simply work hard at being precise and transparent in our definitions? Need we reject a true statement that feeds various streams of scientific inquiry simply because some people don't actually understand what it means? This seems overly myopic and pessimistic to me. I think neuroscientists can, and should, use the formal definitions of "algorithm" and "computer" provided by computer science, and simply be careful not to overstate what those words imply.

That last argument, that it's useful to say the "brain is a computer", is really the key one. For example, if in the distant future of neuroscience it turns out that information theory provides little in the way of helpful insight, then maybe using these abstract definitions and applying them to the brain has been a silly waste of time. Maybe I've just been wasting my glucose on banging out this argument. We know the brain works nothing like our laptops or phones, so maybe all I've ever done is confuse more people than I've helped with this argument.

Obviously, my bet is that this will turn out to be important for understanding the brain. Indeed, as I stated in the introduction, I think that neuroscience is just as much a branch of computer science as it is of physiology and psychology. I am one of a number of researchers who view artificial intelligence and neuroscience as intimately related, and who consider artificial intelligence as key to theoretical inquiry in neuroscience. Perhaps people like me who think this way are just part of a trend that has grown into a bubble due to hype in machine learning. Perhaps... but this line of thinking certainly predates today's hype in artificial intelligence. Whether it will be important to recognize that the brain is a computer according to the definitions of computer science is something that we cannot fully grok at this moment in history. Time will tell...

# data[].attributes.attachment_link.description
No, it's not a metaphor Neuroscience is a funny discipline which can demand a level of interdisciplinary knowledge that is hard to achieve. At its heart, neuroscience is concerned with understanding the organ that is responsible for generating our behaviour, and thus, it is a branch of physiology and psychology.

# data[].attributes.body
Shamli Prakash talks about many different similarities of the brain and the computer and it is shown in the article. I used the similarity of storage that the brain and computer has in common and focused my topic there. "Computers have two types of storage. A short-term storage, which we commonly refer to as RAM, that stores limited amounts of information"

# data[].attributes.attachment_link.readable_text
About three years ago, when I was expecting my son, a friend gifted me a copy of Lise Eliot's 'What's Going on in There?'. It was a very welcome deviation for me from all the parenting books that insisted on telling me how my life was about to turn into living hell with sleepless nights, exhaustion and a general lack of sanity. So I started perusing through the book and soon realized that it was the single most important thing I read in preparation for my impending parenthood. The book talks about brain development in humans from birth until early childhood. It's a fascinating subject in itself, but was even more so in the context of Artificial Intelligence.

Neurobiologically, a human brain functions through a network of neurons - or nerve cells - that interact with each other to communicate and process information based on which we see, hear, move, think, make decisions and generally function. The central nervous system and its network of neurons are at the heart of all the activity that happens in the body.

If we break it down, the human body functions very similarly to modern day computers - or rather, computers are very closely aligned to the most complex processing unit there has ever been, namely the human brain. Going back to basics for a moment - any information processing system consists of 5 main components - input, output, storage, processing and program. We can draw parallels between the brain and computers for each of these elements.

1. Input: Inputs are stimuli, data and objects that act as the basis for action.

In the machine world, inputs come through input devices like keyboards, mouse, scanner, camera, microphone etc. They come in different formats like images, text and structured data.

For the human brain, input is provided through internal and external stimuli. Input 'devices' for brain would include sensory cells (those associated with seeing, hearing, touching, tasting), motor/muscle cells, and even some cells within the brain. These cells pick up the stimulus provided and take it in for further processing.

2. Output: Output is the information or action that comes as a result of processing the input.

Output actions in the case of machines could be things like printing something, adding numbers, projecting an image, solving a complex problem based on an algorithm, identifying patterns etc.

The output from processing that occurs in a human brain is obviously more sophisticated and includes activities like controlling internal and external muscles, sensory perception, managing internal bodily functions (like blood pressure, heart rate, temperature), decision making, problem solving, emotions, social behavior, etc.

3. Storage: This is the place where things like input information, output information and programs are held.

Computers have two types of storage. A short-term storage, which we commonly refer to as RAM, that stores limited amounts of information (data or programs) required by the computer to perform an immediate task. And a long-term storage with much greater capacity in the form of Hard Disk Drives, Optical Disks, Tapes etc.

Very similar to this construct is the memory system of the human brain. The brain's short-term storage is called 'Working Memory' which uses the brain's memory and attention capabilities in short bursts of time to perform the immediate task at hand (e.g. remembering what the teacher is saying when taking notes in class). Long term storage for humans is through their 'Long Term Memory' or LTM function which enables them to store almost unlimited amounts of information for an indefinite period of time. It includes memory (or storage) of things like specific events, factual information and experiences.

4. Processor: This is probably the simplest parallel to grasp. It refers to the central processing circuitry that carries out instructions provided. In a computer, this is managed by its CPU, whereas for humans it is the Central Nervous System comprised of the brain and spinal cord and their internal circuitry.

5. Program: Or 'code', in computer language.

This refers to the intricate set of instructions that lay out what requires to be done with the input in order to arrive at an output, bringing together a set of functions that have to be performed, in a defined order, within defined parameters.

This is the heart of what we are talking about today. In humans, this code is executed via the neurons present in the brain. The neurons make connections with each other through which they pass on signals which are processed to achieve desired action, the entire process controlled by the central nervous system. The signal or information exchange happens through very critical nervous system structures called Synapses.

Computer programs run via machine code, which are patterns of bits (i.e. binary units of information like '1' and '0'). Similarly, a human brain runs its 'code' via patterns of chemical or electrical signals which are passed from one neuron to the next forming a neural network.

Artificial Neural Networks (or ANNs) work exactly like the biological ones. ANNs are a web of artificial neurons that send signals to each other. These networks function just like the brain, learning through seeing, without needing to be specifically programmed. Consider how a baby recognizes a dog for the first time - you show them a dog, you say the word 'dog', you do this several times, and now the baby's brain has learned to identify the pattern between the visual of a dog and the term used for it. A machine learns in exactly the same manner.

Interestingly, Human Intelligence, just like Artificial Intelligence, needs to be developed. During the early years of brain development (typically from conception to 4-5 years of age), there is a very rapid creation of synapses that connect neurons. In data science terms, this phase is somewhat akin to 'Model Training' - the more things a child is exposed to the more the synapses associated with it get built. A good example is language - the more a child is exposed to linguistics in early years, the more evolved the area of her brain that is associated with languages would get. Ditto for other cognitive and non-cognitive abilities.

This was revelatory for me and it changed the way I looked at early childhood. The bottom-line roughly for me was, the more you expose your child to different things in these early days, the stronger and better trained their synapses for those functions. In other words, human intelligence can be shaped, just like its artificial counterpart. This understanding is also at the crux of the Nature vs. Nurture debate we often hear about.

Our understanding of the human brain architecture and its functioning is still developing and there are multiple aspects that we are yet to even begin scratching the surface around. For me, it all comes down to one single question: Are all brain functions computational, i.e. can they all be boiled down to the 'input-processing-output' equation which in theory can be replicated in the physical world, however complex the 'processing' aspect of it. Or are there parts of a human brain's roles (like consciousness, free will, memory classification etc.) that are just not 'programmable'?

The jury is out on this, and the limits of AI will depend largely on the answer to this question.

# data[].attributes.attachment_link.title
Neural Networks: Is Your Brain Like A Computer?

# data[].attributes.attachment_link.description
About three years ago, when I was expecting my son, a friend gifted me a copy of Lise Eliot's 'What's Going on in There?'. It was a very welcome deviation for me from all the parenting books that insisted on telling me how my life was about to turn into living hell with sleepless nights, exhaustion and a general lack of sanity.

# data[].attributes.attachment_link.readable_text
Computers have been beating chess masters for several years now, and just last month, a computer dominated the TV quiz show Jeopardy. What's to stop a computer from achieving human-like consciousness?

Let's back up a bit. Computers can do some things that the human brain can do, like store information and perform rapid calculations, albeit much better. So it is tempting to see the computer as a kind of brain and, reciprocally, the brain as a kind of computer, but ultimately the analogy is misleading. This essay reviews a few elements of brain biology, pointing out some key differences between brains and machines.

The brain does indeed perform computations like a computer. The brain has units in networks (neurons instead of semiconductors) that take in and compile data, operate on the data, and generate output. But brains are far more than fancy calculators. It is no small matter of detail that their circuits are made of biological stuff, not metallic conductors. Their information content is defined not only by the binary logic of a circuit diagram, but also by the dynamic physiological state of the conductors and the context in which the system operates.

Computers typically perform the same operation repeatedly exactly the same way, ad infinitum, whether the end result is the calculation of the fuel consumption of a smart bomb or of the trajectory of a ball in a Wii tennis match. In contrast, the repeated use of a brain circuit alters the performance of the circuit, so the same input on two separate occasions will likely yield a different outcome. This quality is inherent in the makeup of the biological components of the circuit.

Individual neurons in the brain comprise the elements of a vast network. Indeed, the brain is more than a network - it is a network of networks. Some neurons synapse locally with other neurons in the same region; others send out long projections to other parts of the cortex (the outer layers of the brain) or to subcortical structures (clusters of neurons deep within the brain that send projections to and receive projections from the cortex and other subcortical structures).

The wiring in a simple electrical circuit tends to have fixed conductive properties, under standard environmental conditions. However, the conductive properties of neurons change markedly with use. Without getting too much into the biological detail, heavy activity through a network can induce a physical change in the neurons that comprise that network: after heavy use, the same network may be activated with lighter stimulation than was needed to activate it before the heavy usage.

Computer circuits communicate via electrical charges, or electrons. An electron is an electron is an electron, whether it transmits charge in a computer or a toaster. Neurons in a network communicate via a variety of chemicals called neurotransmitters. There are several neurotransmitter systems, each with a unique distribution through the brain and nervous system, and each serves a unique set of functions.

Some neurons primarily encode information. These include both neurons within the cortex and neurons that connect the cortex to subcortical structures, like the hippocampus and basal ganglia. Information-encoding neurons tend to employ neurotransmitters like glutamate that promote depolarization in the receiving neuron and gamma-amino-butyric acid (GABA) that inhibit depolarization. The integration of activating and inhibiting influences on neurons and networks enriches the potential for finely tuned responses. Networks that include glutamate-mediated neurotransmission can alter their properties as described above; that is, they are capable of plasticity.

Other sets of neurons emanate primarily from arcanely named subcortical structures, like the substantia nigra, raphe nucleus, and locus coeruleus, and have activating or inhibiting effects on populations of neurons over various regions of the cortex. These neurons secrete neurotransmitters like serotonin, dopamine, and norepinephrine. These three systems differ in how they radiate to the cortex. Behaviorally, the end result from the arousal of dopamine neurons tends to be heightened mobilization for action, whereas norepinephrine sharpens thinking and perception, and serotonin modulates action.

Altered function in these neurotransmitters is associated with symptoms that reflect their influential role in the regulation of cognition, emotions, and behavior. Suicide and aggression specifically have been associated with perturbations in serotonin, psychosis and addiction with dopamine, anxiety and mood disorders with norepinephrine.

In contrast to the arousing effects on specific regions of the cortex generated by dopamine, serotonin, and norepinephrine, neurons that secrete acetylcholine serve as the messengers to induce arousal in general, both deep in the brain and across the cortex.

A host of other molecules serves to translate the physiological state of the body into adaptively useful information. The hypothalamus sits at the center of this chemical network. Its neurons radiate, emitting a variety of neurotransmitters to points throughout the brain in response to changes in hormone levels, nutritional state, stress, and so on.

So one could argue that although computers can already do repetitive things better, a human brain has the capacity for a rich variety of nuanced responses to the environment, as well as the un-machine-like ability to alter its function in response to experience.

# data[].attributes.attachment_link.description
Computers have been beating chess masters for several years now, and just last month, a computer dominated the TV quiz show Jeopardy. What's to stop a computer from achieving human-like consciousness? Let's back up a bit. Computers can do some things that the human brain can do, like store information and perform rapid calculations, albeit much better.

# data[].attributes.body
David Eagleman explains many different things in the beginning of the book and one is about our memory. He talks about how our memory cannot be recalled perfectly.
"Rather than memory being accurate video recording of a moment in your life, it is a fragile brain state from bygone time that must be resurrected for you to remember."

# data[].attributes.attachment_link.description
Read 1,725 reviews from the world's largest community for readers. Locked in the silence and darkness of your skull, your brain fashions the rich narrative...

# data[].attributes.attachment_link.readable_text
Computer Memory and Function

So, what is computer memory, and why is it so important?

Computer memory is technically any type of electronic storage. Without it and without access to it, a computer is just a useless box. From startup to shutdown, and all processes in between, a computer relies on several types of memory in order to function.

Memory's Place in a Computer

This diagram displays all inputs/outputs and processing pieces of a computer.

At the core of the computer is the central processing unit or CPU, the source of control that runs all programs and instructions. In order to function, computers use two types of memory: primary and secondary. The main storage is the primary memory, and data and programs are stored in secondary memory. However, memory is not stored in the CPU, but the CPU would only be a mess of wires without it!

Memory

Nevertheless, if you had the memory of a computer, you would have a large capacity for forgetting information! That is because most computers make use of what is called random access memory (RAM). This memory is used only when the computer is completing a task. To help you better understand this, imagine the following:

You are an expert gardener, except for one important drawback. Each day, when you leave the garden, everything that you completed that day, from planting to weeding, is erased from your memory. But you are still are woken up the next morning, and asked to garden. At this command, all memories return, and you begin toiling. When the day is done, all is forgotten again.

The garden, the tools, seeds, etc., can be thought of as the secondary memory in this analogy.

Memory and Storage

While the terms 'primary memory', 'main storage', 'primary storage', 'internal storage', 'main memory', and 'RAM' are used interchangeably, the most common term used is random access memory or RAM, which is the data that contains instructions for processing computer operations: like the hapless gardener, the memory is used only for as long as the program needing it is running. Some of the reasons that a computer only needs the memory for processing include:

The computer needs to be powered on for most programs to run; once the power is off, the memory storage is wiped out.

No single program can use all the memory. Programs running simultaneously need to share memory, meaning it is split among those programs.

The memory storage may not be big enough to hold the processing data, so it is released when it's done.

How a Computer Uses Memory

RAM may be the most common reference to computer memory; however, computers will use all types in a basic hierarchy.

Once the computer is turned on, the computer accesses read-only memory (ROM) and makes a quick test of the pieces of memory to ensure there are no errors. Next, the computer starts the basic input/output (BIOS) from the ROM. The operating system is started after the BIOS makes it through the startup routine; the operating system is loaded into RAM. This gives the CPU quick access to the operating system, improving performance and functionality. The CPU accesses this in a continuous cycle--and it happens millions of times per second!

# data[].attributes.attachment_link.keywords[]
how do computers store data? - memory & function - video & lesson transcript | study.com

# data[].attributes.attachment_link.title
How Do Computers Store Data? - Memory & Function - Video & Lesson Transcript | Study.com

# data[].attributes.attachment_link.description
So, what is computer memory, and why is it so important? Computer memory is technically any type of electronic storage. Without it and without access to it, a computer is just a useless box. From startup to shutdown, and all processes in between, a computer relies on several types of memory in order to function.

# data[].attributes.attachment_link.readable_text
Storage is the more or less passive process of retaining information in the brain, whether in the sensory memory, the short term memory or the more permanent long-term memory. Each of these different stages of human memory function as a sort of filter that helps to protect us from the flood of information that confronts us on a daily basis, avoiding an overload of information and helping to keep us sane. The more the information is repeated or used, the more likely it is to be retained in long-term memory (which is why, for example, studying helps people to perform better on tests). This process of consolidation, the stabilizing of a memory trace after its initial acquisition, is treated in more detail in a separate section.

Since the early neurological work of Karl Lashley and Wilder Penfield in the 1950s and 1960s, it has become clear that long-term memories are not stored in just one part of the brain, but are widely distributed throughout the cortex. After consolidation, long -term memories are stored throughout the brain as groups of neurons that are primed to fire together in the same pattern that created the original experience, and each component of a memory is stored in the brain area that initiated it (e.g. groups of neurons in the visual cortex store a sight, neurons in the amygdala store the associated emotion, etc). Indeed, it seems that they may even be encoded redundantly, several times, in various parts of the cortex, so that, if one engram (or memory trace) is wiped out, there are duplicates, or alternative pathways, elsewhere, through which the memory may still be retrieved.

Therefore, contrary to the popular notion, memories are not stored in our brains like books on library shelves but must be actively reconstructed from elements scattered throughout various areas of the brain by the encoding process. Memory storage is, therefore, an ongoing process of reclassification resulting from continuous changes in our neural pathways, and parallel processing of information in our brains.

The indications are that, in the absence of disorders due to trauma or neurological disease, the human brain has the capacity to store almost unlimited amounts of information indefinitely. Forgetting, therefore, is more likely to result from incorrectly or incompletely encoded memories, and/or problems with the recall/retrieval process. It is a common experience that we may try to remember something one time and fail but then remember that same item later. The information is therefore clearly still there in storage, but there may have been some kind of a mismatch between retrieval cues and the original encoding of the information. "Lost" memories recalled with the aid of psychotherapy or hypn osis are other examples supporting this idea, although it is difficult to be sure that such memories are real and not implanted by the treatment.

A recent study has shown scientifically what criminal lawyers have known for decades, namely that memory is an adaptive process.

Apparently trivial or mundane memories from just before an important or traumatic event appear to be kept for a period in a kind of "just-in-case file", and maybe retroactively enhanced in case they are useful in interpreting the event.

This retroactive strengthening is not immediate and can take several hours or days to take effect.

Having said that, though, it seems unlikely that, as Richard Schiffrin and others have claimed, ALL memories are stored somewhere in the brain, and that it is only in the retrieval process that irrelevant details are "fast-forwarded" over or expurgated. It seems more likely that the memories which are stored are in some way edited and sorted, and that some of the more peripheral details are never stored.

Forgetting, then, is perhaps better thought of as the temporary or permanent inability to retrieve a piece of information or memory that had previously been recorded in the brain. Forgetting typically follows a logarithmic curve, so that information loss is quite rapid at the start, but becomes slower as time goes on. In particular, information that has been learned very well (e.g. names, facts, foreign-language vocabulary, etc), will usually be very resistant to forgetting, especially after the first three years. Unlike amnesia, forgetting is usually regarded as a normal phenomenon involving specific pieces of content, rather than relatively broad categories of memories or even entire segments of memory.

Theorists disagree over exactly what becomes of material that is forgotten. Some hold that long-term memories do actually decay and disappear completely over time; others hold that the memory trace remains intact as long as we live, but the bonds or cues that allow us to retrieve the trace become broken, due to changes in the organization of the neural network, new experiences, etc, in the same way as a misplaced book in a library is "lost" even though it still exists somewhere in the library.

Research using functional magnetic resonance imaging (fMRI) suggests that verbs and nouns are stored in different ways in the brain.

Concrete nouns are stored in areas of the brain used to sense or manipulate the referent objects, leading to a theory of meaning based largely on function.

Increasing forgetfulness is a normal part of the ageing process, as the neurons in ageing brains lose their connections and start to die off, and, ultimately the brain shrinks and becomes less effective. The hippocampus, which as we have seen is crucial for memory and learning, is one of the first areas of the brain to deteriorate with age. Recent studies in mice involving infusions of blood from young mice into older mice have shown that the old mice that received young blood showed a significant burst of brain cell growth in the hippocampus region (and vice versa), leading to speculation that young blood might represent the antidote to senile forgetfulness (and other ravages of old age). Similar studies on humans with Alzheimer's disease are currently in progress.

Interestingly, it appears NOT to be possible to deliberately delete memories at will, which can have negative consequences, for example, if we experience traumatic events we would actually prefer to forget. In fact, such memories tend to be imprinted even more strongly than normal due to their emotional content, although recent research involving the use of beta-blockers (such as propranolol) suggests that it may be possible to tone down the emotional aspects of such memories, even if the memories themselves cannot be erased. The way this works is that the act of recalling stored memories makes them "malleable" once more, as they were during the initial encoding phase, and their re-storage can then be blocked by drugs which inhibit the proteins that enable the emotional memory to be re-saved.

# data[].attributes.attachment_link.title
Memory Storage | Memory Processes In The Human Brain

# data[].attributes.attachment_link.description
Storage is the more or less passive process of retaining information in the brain, whether in the sensory memory, the short term memory or the more permanent long-term memory. Each of these different stages of human memory function as a sort of filter that helps to protect us from the flood of information that confronts us on a daily basis, avoiding an overload of information and helping to keep us sane.