Neural coding during active whisker sensation

A major goal in studies of sensory coding is to understand how neural activity represents stimuli in the external world. Rats actively palpate objects with their whiskers to discriminate tactile features of their environment. Although neural responses have been characterized in the whisker system in anesthetized animals for artificially applied whisker stimuli, circuit mechanisms underlying neural response properties and neural coding of sensory information in behaving animals are not well understood.;Precise timing of spikes is thought to be important for many aspects of neural coding in the whisker system. Chapter 2 of this thesis elucidates the cellular mechanisms underlying precise spike timing in primary somatosensory cortex (S1). Feed-forward thalamocortical inhibition is shown to dynamically regulate the integration time window of cortical neurons, thus enforcing temporal fidelity of spiking.;How surface properties are encoded by neural activity in awake and active animals is unknown. In Chapter 3, we describe an experiment to identify the fundamental features of whisker motion that are represented in S1 during natural surface exploration. We simultaneously measured whisker motion and spiking responses of neurons in S1 in awake, behaving rats whisking across textured surfaces. We show that transient slip-stick events are encoded by a majority of S1 neurons with precisely timed spikes, leading to an increase in firing rate. The timing and amplitude of these events is encoded by S1 neurons. Slip-stick responses occurred with low probability, but led to a transient increase in synchronous activity of neurons, resulting in a sparse probabilistic population code. A simulation of the experimental data showed that slip-stick events can be efficiently decoded by synchronous spiking activity on a ∼20 ms time scale across small (∼100 neuron) populations within a single S1 cortical column. These results demonstrate that slip-stick events are primary stimulus features encoded in S1 by a sparse ensemble representation during active surface whisking. Synchronous activity of a small subset of neurons efficiently represents slip-stick events, resulting in a population temporal code for surface properties.