Friday, December 19, 2008

Neural network processing and brain chemistry

Cholinergic Neuromodulation Changes Phase Response Curve Shape and Type in Cortical Pyrimidal Neurons (Stiefel, Gutkin, and Sejnowski)

One of the most important and fundamental findings of neuroscience is that neuron behavior is essentially digital: all-or-nothing, on or off, active or resting. The neuron either fires an action potential or not; there's no such thing as a partial action potential. This digital behavior, along with differential elicitation of activity in regions of the brain given exposure to various stimuli, leads to the prevailing hypothesis that the brain is a modular biological computer that processes information through networks of massively-interconnected neurons. A major challenge, then, in describing the link between brain tissue behavior and the needs of the animal in the "real world" is that many stimuli are analog, differing in small degrees sometimes across a wide range of values: sound volume, light brightness, pressure on skin, etc. To represent (and process and react to) analog phenomena, neurons alter their firing rate - often, a strong stimulus elicits a rapid firing of action potentials over a given period of time, whereas a weaker stimulus elicits a slower firing of action potentials. It's also important for the brain to be able to alter these "spike trains" as new stimuli come in, and as they are further processed - the pattern in which these spike trains are altered by new stimuli is called the "phase response curve". The above article demonstrates that certain neurons in the cortex change their phase response curve based on exposure to a very prevalent neurotransmitter, acetylcholine. Demonstrating this sensitivity implicates that neurotransmitters may be involved in more than just the elicitation of an individual action potential; they may, in fact, influence the behavior of entire neural networks, and therefore exert influence over large-scale cognitive function. This is an incredible finding with significant implications on our models for brain states, mind states, and the relationship between the two.

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