NEURAL NETWORKS: FROM BIOLOGY TO HARDWARE IMPLEMENTATIONS, The development of reliable, scalable, multiple single-unit recording technology is an important goal for the advancement of experimental systems neuroscience. Besides allowing data to be gathered more rapidly, such techniques will enable us to study the temporal structure of spike trains from many simultaneously recorded cells. Temporal patterns of spiking may be significant to downstream cells, may carry information within the nervous system, and may reveal connections between cells. Reproducible temporal structure is most likely to be found in the spike trains of neighbouring cells which share functional roles and anatomical connections.
CHIA (CAGLIARI), ITALY, SEPTEMBER 23-27, 1996.EXTRACELLULAR RECORDING FROM MULTIPLE NEIGHBORING CELLS IN PRIMATE CORTEX
M. Sahani, J. S. Pezaris, R. A. AndersenDivision of Biology
Computation and Neural Systems
Caltech, Pasadena, CA 91125, USA
Abstract
To record
in vivo from multiple cells within a single cortical column or closer, it is necessary to distinguish extracellular action potentials from different cells gathered by a single electrode. The problem is made significantly easier by using a multi-core electrode, such as a tetrode, that provides several slightly different electrical view points. We have recently adapted the tetrode technology, introduced by Recce and O'Keefe for chronic recording in rat hippocampus, for use in semi-chronic primate experiments.In this poster we will discuss a solution to the problem of separating signals from multiple cells on a tetrode recording. The problems addressed are: first, the identification of the underlying spike shapes present in the recording; second, the determination of the times of spikes from different cells; third, the resolution of simultaneous spikes from more than one neuron; fourth, the tracking of modifications in apparent spike shape due to bursting and electrode drift; and fifth the automated, scalable implementation of the algorithm to allow large numbers of cells to be discriminated on-line. A reliable, stable, and quantifiable system that addresses all of these issues is critical to the next generation of behavioural neurophysiological experiments.
Maneesh Sahani, 216-76 Caltech, Pasadena, CA 91125, USA, maneesh@caltech.edu, 5 October 1996