Abstract
Neurons in the primary somatosensory cortex (S1) generate synchronised high-frequency (∼600 Hz) bursts in response to peripheral stimulation, and single-cell activity is locked to the macroscopic 600 Hz EEG wavelets. The mechanism of burst generation and synchronisation in S1 is not yet understood. Using a Poisson model with refractoriness that was fitted to to unit recordings from macaque monkeys, we can explain the synchronisation of neurons as the consequence of coincident synaptic inputs, while their high firing precision stems from the large input amplitude combined with a refractory mechanism. This model reproduced also the distribution of temporal spike patterns over repeated presentation of the same stimulus. In addition, the fine temporal details of the spike patterns are representative of the trial-to-trial variations in population excitability and bear upon the mean population activity. The findings are confirmed in a more detailed computational model of a neuron receiving cortical and thalamic inputs through depressing synapses. Our findings show that a simple feedforward processing of peripheral in-puts could give rise to neuronal responses with non-trivial temporal and population statistics. We conclude that neural systems could use refractoriness to encode variable cortical states into stereotypical short-term spike patterns amenable to processing at neuronal time scales.
Synopsis Neurons in the primary somatosensory cortex (S1, hand area) respond to repeated presentation of the same stimulus with variable sequences of spikes. It is commonly assumed that this variability reflects neuronal “noise”, which is filtered out by temporal or population averaging. Indeed, responses of some cortical neurons are variable when collected over all stimulus repetition, but they form distinct spike patterns when single-trial responses are grouped with respect to the arrangement of spikes and intervening silences. To account for the spike pattern statistics, we developed a simplified model of S1 neurons, which combines the effects of synaptic inputs and intrinsic neural properties. We show that single-neuron and population responses are best reproduced when a private variability in each neuron is combined with a multiplicative gain shared over whole population [Okun et al., 2015, Goris et al., 2014, Schölvinck et al., 2015]. This model is capable of transforming slow modulatory inputs into a set of temporal spike patterns, which might inform about dynamical state of the early stages of sensory processing. This phenomenon exemplifies a general mechanism of transforming the ensemble cortical states into precise temporal spike patterns at the level of single neurons.
