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

Multiresolution analysis of multichannel neural recordings in the context of signal detection, estimation, classification and noise suppression.

The development of multichannel microprobe fabrication technology for recording neural activity in the brain has recently achieved a significant milestone towards integrating microimplanted device technology with research and clinical applications in neurophysiology. Recent probe designs have been able to integrate large number of sites on a single probe to provide neuroscientists with tools to record from large populations of cells. Advances in probe design are always governed by the feasibility of the associated communication and signal processing technology. Surprisingly, existing signal processing techniques are considerably behind the overwhelming advances in probe fabrication technology. We envision the problem of optimizing the information transfer from the microdevice as three fold: Noise suppression, Signal detection, and Blind Source Separation. We demonstrate that all three goals can be achieved by merging multiresolution analysis theory with array processing theory into a novel unified framework. In the noise suppression context, we show that we can near-optimally suppress the additive correlated noise by introducing a spatio-temporal decorrelation mechanism using eigendecomposition of a discrete wavelet transform representation of the array data followed by universal thresholding, a unique property of the multiresolution analysis. In the detection context, when no apriori knowledge is given about the signal and/or the noise processes, we formulate a transform domain Generalized Likelihood Ratio Test in the array case that overcomes the problem of estimating unknown noise parameters. The Blind Source identification problem is approached within the same context using an inherent invariance property of the signal subspace across multiresolution levels that enables characterization of each neural source. Results demonstrate that this framework provides the basis for simple and practical implementation in the structure of today's biosensor array technology without compromising issues of bandwidth, detection and classification. We show that the framework is capable of achieving substantial improvement in detection performance in severe noise conditions, and robustness to source nonstationarities and nonisotropic properties of the unknown medium under no constraints on the array design or prior knowledge of the signal parameters.Ph.D.Applied SciencesBiological SciencesBiomedical engineeringElectrical engineeringNeurosciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/130311/2/3042147.pd