Stimulus-dependent correlations in threshold-crossing spiking neurons
| Autor: | Sam Lewallen, Haim Sompolinsky, Yoram Burak |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Cognitive Neuroscience
Models Neurological Statistics as Topic Population Theta model Action Potentials Differential Threshold Stimulus (physiology) Retinal ganglion Bursting Models of neural computation Arts and Humanities (miscellaneous) medicine Animals Humans education Mathematics Neurons education.field_of_study Artificial neural network Quantitative Biology::Neurons and Cognition medicine.anatomical_structure Nonlinear Dynamics Quantitative Biology - Neurons and Cognition FOS: Biological sciences Neurons and Cognition (q-bio.NC) Neuron Biological system Neuroscience |
| DOI: | 10.48550/arxiv.0902.2037 |
| Popis: | We consider a threshold-crossing spiking process as a simple model for the activity within a population of neurons. Assuming that these neurons are driven by a common fluctuating input with Gaussian statistics, we evaluate the cross-correlation of spike trains in pairs of model neurons with different thresholds. This correlation function tends to be asymmetric in time, indicating a preference for the neuron with the lower threshold to fire before the one with the higher threshold, even if their inputs are identical. The relationship between these results and spike statistics in other models of neural activity are explored. In particular, we compare our model with an integrate-and-fire model in which the membrane voltage resets following each spike. The qualitative properties of spike cross correlations, emerging from the threshold-crossing model, are similar to those of bursting events in the integrate-and-fire model. This is particularly true for generalized integrate-and-fire models in which spikes tend to occur in bursts as observed, for example, in retinal ganglion cells driven by a rapidly fluctuating visual stimulus. The threshold crossing model thus provides a simple, analytically tractable description of event onsets in these neurons. Comment: In press in Neural Computation. 43 pages including 5 appendices and 9 figures |
| Databáze: | OpenAIRE |
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