A Gamma-phase Model of Receptive Field Formation

Abstract

Event Abstract Back to Event A Gamma-phase Model of Receptive Field Formation Rate coding is a ubiquitous model of cortical signaling but is it the cause of computation or a correlate? Criticisms of the causal model cite difficulties in achieving the precision and the speed of computation, but we argue that there is an additional constraint that arises from Bayesian models of RF formation3. Think of the problem representing a distribution of inputs amongst cells with overlapping RFs. When a spike sent, if it is to be included in a pool that causes the synaptic input to be modified, then it should be so classified probabilistically according to a competition between its neighbors. This is a standard feature of the Expectation Maximization algorithm. If this is not done, errors in the RF structure will result. However the accumulation of statistics in this process is slow. It might be suitable for RF formation but it is unsuitable for communicating the values of variables used in the overarching algorithm quickly. Thus it would be beneficial to somehow have two processes, one a slow one that accumulates statistics and another a fast one that signals values. This can be done if the cortex uses gamma-phase coding to signal analog values1 and spike selection to modify the RF. The gamma signal is used as a timing reference and timing delays represent numerical values, with spikes closest in time to the timing reference being the highest value. This strategy has the additional virtue of being compatible with spike-timing dependent modification of synaptic strength. However gamma-phase coding seems incompatible with a large amount of single cell recording data. If the cortex is using a timing reference2, why does the output of cells appear Poission and mimic receptive field organization? Our claim is that the probabilistic structure used to update receptive fields can be thought of as the routing used in the overlying computation. Simply, the computation being done can take alternate routes that are chosen probabilistically, but for each neuron on the route that is chosen to send a spike, the value sent can be signaled quickly and accurately using gamma-phase coding. The cells appear Poisson since they are chosen randomly and they reflect RF statistics since those statistics govern their selection. We illustrate these points using adapting the classical sparse-coding model of RF formation between LGN and striate cortex3 to use gamma phase coding. The stunning demonstration is that the representation of an image is stable even though from moment to moment alternate choices of RFs are being used to represent it. We further demonstrate that the probabilistic routing that results has the consequence that a neuron?s receptive field, when measured in the classical way with the peristimulus histogram, recapitulates the RF statistics. Finally we show that gamma-phase coding may be more ubiquitous than currently believed as it can be difficult to detect if probabilistic routing is used. Supported by NIH Grant R01RR009283