- Poster presentation
- Open Access
Method for analyzing spike patterns with Markov transition matrices and Kullback-Leibler divergence
© Sabottke; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Simulated Data
- Primary Visual Cortex
- Interspike Interval
- Spike Time
- Distribution Tail
We describe a novel method for analyzing neural data that uses a combination of Markov transition matrices and Kullback-Leibler divergence to characterize spike history and spike patterns. For this method, the interspike intervals (ISIs) are divided into bins by quantiles, and a Markov transition matrix is computed for the ISI sequence. This Markov transition matrix is then compared to another Markov transition matrix constructed under the assumption of independent spiking. We then compute the Kullback-Leibler divergence between the distributions of ISI transition probabilities for each bin. The purpose of this method is to quantify the effects of spike history on neuron output and to help better characterize the flaws associated with assuming independent spiking.
These results emphasize that the main shortcoming of simple spiking models, like the Poisson model, is a failure to account for spike history and spike patterns related to bursting and long periods of silence. This also suggests that neural systems can gain an advantage in computational efficiency by accounting for spike timing aspects of bursting and extended periods of silence within the neural code.
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