The possible role of spike patterns in cortical information processing.
When the same visual stimulus is presented across many trials, neurons in the visual cortex receive stimulus-related synaptic inputs that are reproducible across trials (S) and inputs that are not (N). The variability of spike trains recorded in the visual cortex and their apparent lack of spike-to-spike correlations beyond that implied by firing rate fluctuations, has been taken as evidence for a low S/N ratio. A recent re-analysis of in vivo cortical data revealed evidence for spike-to-spike correlations in the form of spike patterns. We examine neural dynamics at a higher S/N in order to determine what possible role spike patterns could play in cortical information processing. In vivo-like spike patterns were obtained in model simulations. Superpositions of multiple sinusoidal driving currents were especially effective in producing stable long-lasting patterns. By applying current pulses that were either short and strong or long and weak, neurons could be made to switch from one pattern to another. Cortical neurons with similar stimulus preferences are located near each other, have similar biophysical properties and receive a large number of common synaptic inputs. Hence, recordings of a single neuron across multiple trials are usually interpreted as the response of an ensemble of these neurons during one trial. In the presence of distinct spike patterns across trials there is ambiguity in what would be the corresponding ensemble, it could consist of the same spike pattern for each neuron or a set of patterns across neurons. We found that the spiking response of a neuron receiving these ensemble inputs was determined by the spike-pattern composition, which, in turn, could be modulated dynamically as a means for cortical information processing.
Tiesinga
Paul H E
PH
Physics & Astronomy, University of North Carolina at Chapel Hill, Campus Box 3255, Chapel Hill, North Carolina 27599-3255, USA. tiesinga@physics.unc.edu
Toups
J Vincent
JV
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001330:Electronic Data Processing; D006801:Humans; D016571:Neural Networks, Computer; D009474:Neurons / Q000145:classification / Q000502:physiology*; D010507:Periodicity; D014793:Visual Cortex / Q000166:cytology* / Q000502:physiology