Identification of Spike Patterns in Cortical Networks

Salk Institute For Biological Studies, La Jolla CA

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Abstract

DESCRIPTION (provided by applicant): The goals of this project focus on understanding the origins and consequences of the stereotyped patterns of spike times observed in response to repeated injections of the same stimulus. These patterns have been observed in vivo in the sensory periphery in mammals and invertebrates alike, and even in a given cell type across different animals. The existence of stereotyped patterns leads to novel statistical structure of spike trains that in turn may have profound effects on the encoding and decoding of stimulus-related information (information about the world) by subsequent neurons in a given pathway. Patterns would not occur in neurons if they simply responded at random intervals according to their average firing rate. However, patterns are seen in biophysically realistic neurons in which synaptic currents, nonlinear spike-initiation and refractory mechanisms combine with the dynamics of input currents to create complex, highly structured behavior. Experiments using in vitro slices of rat frontal cortex and computer simulations of neural models will be used to study input-output relationships to a wide variety of input stimuli, to examine the abilities of neurons to code information in patterns of spikes and also to decode patterns. The conditions that occur in vivo will also be recreated experimentally in vitro using a dynamic clamp to inject background synaptic input conductances into a cell. Inputs that will be examined include constant (current step) stimuli, sinusoidal stimuli, complex quasi-periodic (multiple band-pass) stimuli similar to the local field potential or EEG patterns, observed in vivo and aperiodic stimuli. In order to quantify the reliability of patterns in spike trains, a new measure based on the correlations between spike trains will be used. A new clustering algorithm will also be used to identify and extract spike patterns from raw data. As preliminary results, we show that these new tools can extract previously unknown spike patterns from published data obtained in vivo. A better understanding of how the spike trains encode temporal information in spike timing is important in a variety of neurological diseases such as dyslexia, autism and schizophrenia, where there is evidence for a breakdown in the processing of precise timing information in the brain.