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Dynamic Alignment Models for Neural Coding

Figure 2

Two minimal MPHs for flexible timing and context dependent coding.

(A) Architecture of the minimal MPH that allows for neural codes with varying latencies, i.e. flexible timing. This MPH has 3 hidden states, one X-state that models only the stimulus, one R-state that models only the neural response, and one M-state that jointly models stimulus and response. The probability distributions over stimuli (bottom) are illustrated as low dimensional projections (stimulus dimension 2 coincides with the receptive field of the M-state). (B) Hidden state sequences in that model correspond to paths in the alignment matrix: a diagonal step leading into position implies that stimulus and response at times and are jointly modeled by an M-state, a horizontal step implies modeling of only the stimulus at time , and a vertical step implies modeling of only the response at time (deviations from the diagonal reflect jittered spikes detected by the model). Depicted stimulus and spiking responses are from figure 1A. (C) The minimal MPH for modeling state-dependent neural codes. The MPH can switch between several M-states, each of which represents a different RF. The (projected) stimulus distributions given a spike (spike triggered stimulus ensemble) are centered on the respective RFs (indicated by black arrows). (D) Adding states to the model turns the alignment matrix into an alignment tensor composed of several planes (strictly speaking, B depicts a tensor as well; we just projected all the states onto one plane). The switch from state 1 to State 2 is indicated (green arrow).

doi: https://doi.org/10.1371/journal.pcbi.1003508.g002