Encoding whisker deflection velocity within the rodent barrel cortex using phase-delayed inhibition
The primary sensory feature represented within the rodent barrel cortex is the velocity with which a whisker has been deflected. Whisker deflection velocity is encoded within the thalamus via population synchrony (higher deflection velocities entail greater synchrony among the corresponding thalamic population). Thalamic (TC) cells project to regular spiking (RS) cells within the barrel cortex, as well as to inhibitory cortical fast-spiking (FS) neurons, which in turn project to RS cells. Thus, TC spikes result in EPSPs followed, with a small time lag, by IPSPs within an RS cell, and hence the RS cell decodes TC population synchrony by employing a phase-delayed inhibition synchrony detection scheme. As whisker deflection velocity is increased, the probability that an RS cell spikes rises, while jitter in the timing of RS cell spikes remains constant. Furthermore, repeated whisker deflections with fixed velocity lead to system adaptation  TC ?RS, TC ?FS, and FS ?RS synapses all weaken substantially, leading to a smaller probability of spiking of the RS cell and increased jitter in the timing of RS cell spikes. Interestingly, RS cell activity is better able to distinguish among different whisker deflection velocities after adaptation. In this work, we construct a biophysical model of a basic building block of barrel cortex  the feedforward circuit consisting of TC cells, FS cells, and a single RS cell  and we examine the ability of the purely feedforward circuit to explain the experimental data on RS cell spiking probability, jitter, adaptation, and deflection velocity discrimination. Moreover, we study the contribution of the phase-delayed inhibition network structure to the ability of an RS cell to decode whisker deflection velocity encoded via TC population synchrony.
whisker deflection velocity barrel cortex phase-delayed inhibition synchrony detection adaptation feedforward inhibition neuronal networks computational neuroscience network structure and function