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Posts Tagged ‘computational modeling’

Levels of Analysis and Emergence: The Neural Basis of Memory

February 12, 2011 Leave a comment

Cognitive neuroscience constantly works to find the appropriate level of description (or, in the case of computational modeling, implementation) for the topic being studied.  The goal of this post is to elaborate on this point a bit and then illustrate it with an interesting recent example from neurophysiology.

As neuroscientists, we can often  choose to talk about the brain at any of a number of levels: atoms/molecules, ion channels and other proteins, cell compartments, neurons, networks, columns, modules, systems, dynamic equations, and algorithms.

However, a description at too low a level might be too detailed, causing one to lose the forest for the trees.  Alternatively, a description at too high a level might miss valuable information and is less likely to generalize to different situations.

For example, one might theorize that cars work by propelling gases from their exhaust pipes.  Although this might be consistent with all of the observed data, by looking “under the hood” one would find evidence that this model of a car’s function is incorrect.

On the other hand, a model may be formulated at too low a level.  For example, a description of the interactions between molecules of wood and atoms of metal is not essential for a complete, thorough understanding of how a door works.

Emergence

One particularly exciting aspect of multi-level research is when one synthesizes enough observations to move from one level to a higher one.  Emergence is a term used to describe what occurs when simpler rules interact to form complex behavior.   It’s when a particular combination of properties or (typically nonlinear) processes gives rise to something surprising and/or non-obvious.  To give a basic example, hydrogen and oxygen both support fire.  Surprisingly, their combination — water — puts fires out and expands when frozen.

An Example of Emergence: The Neural Basis of Memory

A recent article by Raymond and Redman (Journal of Neurophysiology, 2002) takes a close look at three separate subcellular mechanisms that appear to support LTP (reminder: LTP is long-term potentiation, which is one of the best candidates to-date for the neural basis of memory.

Raymond and Redman replicate the earlier finding that longer bouts of electrical stimulation can cause LTP to be more powerful (resulting in larger postsynaptic responses), and last longer.  They demonstrated three different levels of LTP in their experiment by using three different length trains of electrical stimulation.  This stimulation-dependent property of LTP has been taken as the basis for synaptic modification rules used in neural network models; (Neural Network “Learning Rules”)

Interestingly, the researchers then demonstrated that by blocking three different cellular mechanisms –  ryanodine receptors, IP3 receptors and L-type VDCCs respectively –  they were able to selectively block LTP from the shortest, intermediate or longest stimulation trains.

Taken together, these results suggest that the high-level phenomenon of LTP is actually composed of (at least) three separate underlying processes.  These separate processes appear to cover different timespans, contributing to an exponential curve relating LTP to the time and strength of neuronal activity.

Insight Gained

The study mentioned in this post contributes to the field by helping to lending additional evidence to our current theoretical understanding of a mechanism which is likely to underpin memory.  From a theoretical perspective LTP appears to be a meaningful construct which emerges from mutliple, dissociable subcellular processes.

More generally, the study is an excellent demonstration of emergence: three separate processes from a particular level (subcellular receptor proteins) appear to jointly support a more abstract, single processes at a higher level (LTP in cellular electrophysiology). As a result, computational modelers can feel more comfortable with assumptions of an LTP-like assumption in their simulations.

A final thought is that this type of research also clearly highlights the importance of interdisciplinary research in the neurosciences.

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Neural Network “Learning Rules”

February 11, 2011 1 comment

Most neurocomputational models are not hard-wired to perform a task. Instead, they are typically equipped with some kind of learning process.  In this post, I’ll introduce some notions of how neural networks can learn. Understanding learning processes is important for cognitive neuroscience because they may underly the development of cognitive ability.

 

Let’s begin with a theoretical question that is of general interest to cognition: how can a neural system learn sequences, such as the actions required to reach a goal?

Consider a neuromodeler who hypothesizes that a particular kind of neural network can learn sequences. He might start his modeling study by “training” the network on a sequence. To do this, he stimulates (activates) some of its neurons in a particular order, representing objects on the way to the goal.

After the network has been trained through multiple exposures to the sequence, the modeler can then test his hypothesis by stimulating only the neurons from the beginning of the sequence and observing whether the neurons in the rest sequence activate in order to finish the sequence.

Successful learning in any neural network is dependent on how the connections between the neurons are allowed to change in response to activity. The manner of change is what the majority of researchers call “a learning rule“.  However, we will call it a “synaptic modification rule” because although the network learned the sequence, it is not clear that the *connections* between the neurons in the network “learned” anything in particular.

The particular synaptic modification rule selected is an important ingredient in neuromodeling because it may constrain the kinds of information the neural network can learn.

There are many categories of mathematical synaptic modification rule which are used to describe how synaptic strengths should be changed in a neural network.  Some of these categories include: backpropgration of error, correlative Hebbian, and temporally-asymmetric Hebbian.

  • Backpropogation of error states that connection strengths should change throughout the entire network in order to minimize the difference between the actual activity and the “desired” activity at the “output” layer of the network.
  • Correlative Hebbian states that any two interconnected neurons that are active at the same time should strengthen their connections, so that if one of the neurons is activated again in the future the other is more likely to become activated too.
  • Temporally-asymmetric Hebbian is described in more detail in the example below, but essentially emphasizes the importants of causality: if a neuron realiably fires before another, its connection to the other neuron should be strengthened. Otherwise, it should be weakened.

Why are there so many different rules?  Some synaptic modification rules are selected because they are mathematically convenient.  Others are selected because they are close to currently known biological reality.  Most of the informative neuromodeling is somewhere in between.

An Example

Let’s look at a example of a learning rule used in a neural network model that I have worked with: imagine you have a network of interconnected neurons that can either be active or inactive.    If a neuron is active, its value is 1, otherwise its value is 0. (The use of 1 and 0 to represent simulated neuronal activity is only one of the many ways to do so; this approach goes by the name “McCulloch-Pitts”).

Consider two neurons in the network, named PRE and POST, where the neuron PRE projects to neuron POST. A temporally-asymmetric Hebbian rule looks at a snapshot in time and says that the strength of the connection from PRE to POST, a value W between 0 and 1, should change according to:

W(future) = W(now) + learningRate x  POST(now)  x  [PRE(past) – W(now)]

This learning rule closely mimics known biological neuronal phenomena such as long-term potentiation (LTP) and spike-timing dependent plasticity (STPD) which are thought to underlie memory and will be subjects of future Neurevolution posts. (By the way, the learning rate is a small number less than 1 that allows the connection strengths to gradually change.)

Let’s take a quick look at what this synaptic modification rule actually means.  If the POST neuron is not active “now”, the connection strength W does not change in the future because everything after the first + becomes zero:

W(future) = W(now) + 0

Suppose on the other hand that the POST neuron is active now.  Then POST(now) equals 1.  To see what happens to the connection strength in this case, let’s assume the connection strength is 0.5 right now.

W(future) = 0.5 + learningRate x [PRE(past) – 0.5]

As you can see, two different things can happen: if the PRE was active, then PRE(past) = 1, and we will get a stronger connection in the future because we have:

W = 0.5 + learningRate x (1 – 0.5)

But if the PRE was inactive, then PRE(past) = 0, and we will get a weaker connection in the future because we have:

W = 0.5 + learningRate x (0 – 0.5)

So this is all good — but what can this rule do in a network simulation?  It turns out that this kind of synaptic modification rule can do a whole lot.  Let’s review an experiment that shows one property of a network equipped with this learning mechanism.

Experiment: Can a Network of McCulloch-Pitts Neurons Learn a Sequence with this Rule?

Let’s review part of a simulation experiment we carried out a few years ago (Mitman, Laurent and Levy, 2003).  Imagine you connect 4000 McCulloch-Pitts neurons together randomly at 8% connectivity.  This means that each neuron has connections coming in from about 300 other neurons in this mess.

When a neuron is active, it passes this activity to other neurons through its weighted connections.  The strengths of all the connections start out the same, but over time they change according to the temporally-asymmetric Hebbian rule discussed above.  In order to keep all the neurons from being active at once, there’s a cutoff so that only about 7% of the neurons are active (see the paper full details).

To train the network, we then turned on groups of neurons in order: this was a sequence of 10 neurons at a time, turned on for 10 time ticks each.  This is like telling the network the sequence “A,B,C,D,E,F,G,H,I,J”.

Here’s a picture showing what happens in the network at first when training the network as we activated blocks of neurons.  In these figures, time moves from left to right.  Each little black line means that a neuron was active at that time; to save space, only the part of the network where we “stimulated” is shown.  The neurons that we weren’t forcing to be active went on and off randomly because of the random connections.

After we exposed the network to the sequence several times, something interesting began to happen.  The firing at other times was no longer completely random.  In fact, it looked like the network was learning to anticipate that we were going to turn on the blocks of neurons:

Did the network learn the sequence?  Suppose we jumpstart it with “A”.  Will it remember “B”, “C”,… “J”?

Indeed it did!  A randomly-connected neural network equipped with this synaptic modification rule is able to learn sequences!

 

This was an example of a “learning rule” and just one function — the ability to learn sequences of patterns — that emerges in a network governed by this rule.  In future posts, I’ll talk about more about these kinds of models and mechanisms, and their emphasize their relevance to cognitive neuroscience.