A Gamma-phase model of Receptive Field Formation
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1
University of Texas, United States
For the most part, cortical neurons exhibit predictably random spiking behavior that can be modeled as a Poisson process with a baseline rate that has been shown to be a correlate of experimental parameters in hundreds of experiments. Because of this extensive data set it, has been almost taken for granted that a neuron communicates a scalar parameter by the spike rate even though this strategy has proven very difficult to realize in widespread circuit simulations. One of the reasons that it has been difficult to find an alternate interpretation of cortical spikes may be that they are used for a number of different purposes simultaneously, each having different requirements. To focus on two of the important ones, the cells must learn their receptive fields and at the same time communicate stimulus information. These two tasks have radically different information processing requirements. The first task is slow and incremental, occurring prominently during development, but also in the lifetime of the animal, and uses aggregates of inputs. The second task occurs vary rapidly and uses just a few spikes over a very fast, 100-300 millisecond timescale.
Our primary result suggests that the membrane potentials of cells with overlapping receptive fields are representing components of probability distributions such that each spike generated is a data point from the combined distribution. Thus if the receptive fields overlap only one cell in the overlap can send it and the overlapping cells compete probabilistically to be the sender. Each spike communicates numerical information is by using relative timing where in a wave of spikes the earlier spikes represent higher values. This strategy can be used in general circuitry including feedback circuitry if such waves are references to the gamma oscillatory signal Spikes coincident with zero phase in the gamma signal can signal high numbers and spikes lagging by a few milliseconds can signal lower numbers. The reason a neuron's spike train appears random is that, in any specific computation, the information is randomly routed in a neural circuit from moment to moment. It is this random routing that causes the spike train to appear almost Poisson in distribution.
Learning incorporates sparse coding directly in that the input is only approximated to a certain error, resulting in a very small number of cells at each cycle that are required to send spikes. Furthermore, learning uses the spike timing phase directly to modify each synapse according to a Hebb rule. The gamma phase timing is also critical for fitting the data rapidly. By using lateral inhibition from successive components, the input data can be coded in a single gamma phase cycle. To illustrate these points, we simulate the learning of receptive fields in striate cortex, making use of a model of the LGN to striate cortex feedback circuitry. The simulation suggests the possibility that the rate code interpretation of cortical cells may be a correlate of a more fundamental process and makes testable predictions given timing information.
Conference:
Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009.
Presentation Type:
Oral Presentation
Topic:
Neural encoding and decoding
Citation:
Ballard
DH
(2009). A Gamma-phase model of Receptive Field Formation.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Conference on Computational Neuroscience.
doi: 10.3389/conf.neuro.10.2009.14.091
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Received:
27 Aug 2009;
Published Online:
27 Aug 2009.
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Correspondence:
Dana H Ballard, University of Texas, Austin, United States, dana@cs.utexas.edu