Millisecond-Timescale Local Network Coding in the Rat Primary Somatosensory Cortex

Correlation among neocortical neurons is thought to play an indispensable role in mediating sensory processing of external stimuli. The role of temporal precision in this correlation has been hypothesized to enhance information flow along sensory pathways. Its role in mediating the integration of information at the output of these pathways, however, remains poorly understood. Here, we examined spike timing correlation between simultaneously recorded layer V neurons within and across columns of the primary somatosensory cortex of anesthetized rats during unilateral whisker stimulation. We used Bayesian statistics and information theory to quantify the causal influence between the recorded cells with millisecond precision. For each stimulated whisker, we inferred stable, whisker-specific, dynamic Bayesian networks over many repeated trials, with network similarity of 83.3±6% within whisker, compared to only 50.3±18% across whiskers. These networks further provided information about whisker identity that was approximately 6 times higher than what was provided by the latency to first spike and 13 times higher than what was provided by the spike count of individual neurons examined separately. Furthermore, prediction of individual neurons' precise firing conditioned on knowledge of putative pre-synaptic cell firing was 3 times higher than predictions conditioned on stimulus onset alone. Taken together, these results suggest the presence of a temporally precise network coding mechanism that integrates information across neighboring columns within layer V about vibrissa position and whisking kinetics to mediate whisker movement by motor areas innervated by layer V

Temporal precision in population - but not individual neuron - dynamics reveals rapid experience-dependent plasticity in the rat barrel cortex

Cortical reorganization following sensory deprivation is characterized by alterations in the connectivity between neurons encoding spared and deprived cortical inputs. The extent to which this alteration depends on Spike Timing Dependent Plasticity (STDP), however, is largely unknown. We quantified changes in the functional connectivity between layer V neurons in the vibrissal primary somatosensory cortex (vSI) (barrel cortex) of rats following sensory deprivation. One week after chronic implantation of a microelectrode array in vSI, sensory-evoked activity resulting from mechanical deflections of individual whiskers was recorded (control data) after which two whiskers on the contralateral side were paired by sparing them while trimming all other whiskers on the rat’s mystacial pad. The rats’ environment was then enriched by placing novel objects in the cages to encourage exploratory behavior with the spared whiskers. Sensory-evoked activity in response to individual stimulation of spared whiskers and adjacent re-grown whiskers was then recorded under anesthesia 1 to 2 days and 6 to 7 days post-trimming (plasticity data). We analyzed spike trains within 100 ms of stimulus onset and confirmed previously published reports documenting changes in receptive field sizes in the spared whisker barrels. We analyzed the same data using Dynamic Bayesian Networks (DBNs) to infer the functional connectivity between the recorded neurons. We found that DBNs inferred from population responses to stimulation of each of the spared whiskers exhibited graded increase in similarity that was proportional to the pairing duration. A significant early increase in network similarity in the spared-whisker barrels was detected 1-2 days post pairing, but not when single neuron responses were examined during the same period. These results suggest that rapid reorganization of cortical neurons following sensory deprivation may be mediated by an SDTP mechanism

Tracking Signal Subspace Invariance for Blind Separation and Classification of Nonorthogonal Sources in Correlated Noise

We investigate a new approach for the problem of source separation in correlated multichannel signal and noise environments. The framework targets the specific case when nonstationary correlated signal sources contaminated by additive correlated noise impinge on an array of sensors. Existing techniques targeting this problem usually assume signal sources to be independent, and the contaminating noise to be spatially and temporally white, thus enabling orthogonal signal and noise subspaces to be separated using conventional eigendecomposition. In our context, we propose a solution to the problem when the sources are nonorthogonal, and the noise is correlated with an unknown temporal and spatial covariance. The approach is based on projecting the observations onto a nested set of multiresolution spaces prior to eigendecomposition. An inherent invariance property of the signal subspace is observed in a subset of the multiresolution spaces that depends on the degree of approximation expressed by the orthogonal basis. This feature, among others revealed by the algorithm, is eventually used to separate the signal sources in the context of "best basis" selection. The technique shows robustness to source nonstationarities as well as anisotropic properties of the unknown signal propagation medium under no constraints on the array design, and with minimal assumptions about the underlying signal and noise processes. We illustrate the high performance of the technique on simulated and experimental multichannel neurophysiological data measurements.</p