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 Understanding the nervous system by walking in a neuron's shoes

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Date posted: 22/10/2008

The paradigm known to psychologists as the theory of mind, is the basis for a new theory of brain function proposed by Fiorillo, a professor at Stanford University. His model attempts to provide an understanding of the nervous system by looking at the world from a neuron?s perspective. This ?first-person? approach differs from the conventional ?third-person? approach to understanding the nervous system, which is based on observing inputs and outputs and trying to figure out the relationship between the two.

?The problem [with the conventional approach] is that the relationship between inputs and outputs is very complicated, even for a single neuron,? Fiorillo said. ?By contrast, I have tried to figure out what a neuron knows about the world. This is possible because we already know a great deal about the biophysical properties of neurons. I think that if we can figure out what information a neuron has, then we will be able to make better sense of its inputs and outputs. I think that this approach to information will prove to be very useful, regardless of the success of the rest of the theory.?

In Fiorillo?s model, each of the billions of neurons in the nervous system shares the same basic computational function. Also, a neuron?s function mirrors the function of the system as a whole. After all, Fiorillo explains, the entire system originally developed from a single cell. However, even though neurons may use the same general computation method, they still have differences, since the information that a neuron has is the result of the particular statistical pattern of inputs to which it has been exposed. Since different neurons develop in different environments, each neuron acquires its own unique set of information.

Because the stimulus of each neuron is selected under the influence of reward feedback, the further a neuron is from the system?s sensory input, the more informative its stimulus is about the abstract notion of reward and the less informative its stimulus is about the concrete sensory world. As the ?last? neuron in the circuit, a motor neuron has the most information and the least uncertainty about reward, so it?s appropriate that the motor neuron determines the system?s output. As Fiorillo explained, the anatomy of the nervous system supports this proposal. For example, scientists know that taste is usually a better predictor of future reward than light intensity. Appropriately, there are fewer neurons lying between the gustatory cells in the tongue and motor neurons, than there are between photoreceptor cells in the eye and motor neurons.

?A great deal of past work has focused on how neurons form synaptic connections with one another,? Fiorillo said. ?However, a neuron has many inputs that are not synaptic but are instead mediated by non-synaptic ion channels. The computational function of these channels has not been well understood, and these channels are often completely absent in the neurons of artificial neural networks.

?I propose that the function of these ion channels in the temporal domain is analogous to the function of synapses in the spatial domain. A synapse, like the neuron from which it originates, is dedicated to a particular region of space. A neuron has many potential synapses, and by choosing its synapses it can choose which regions of space are the most interesting. Similarly, a neuron has many different non-synaptic ion channels (particularly potassium channels) encoded in its genome, and it chooses to express only a small number. These channels are known to differ from one another in their kinetic properties (how rapidly they change in response to changes in voltage). Thus, different channels remember different periods of the past. What I propose is that a neuron selects which of these channels to express in fundamentally the same way that it selects its synaptic inputs. I propose that a neuron selects those channels, or those memories of the past, that are the best predictors of its current synaptic input. These channels would therefore allow a neuron to make predictions through time.?

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