Neuroscience 2010, the 40th annual meeting of the Society for Neuroscience
会期: 2010.11.13-17
会場: The San Diego Convention Center

発表日:2010.11.15

Program#/Poster#: 372.12/NN13

Title: Pairwise maximum entropy models explain correlated activity of neural populations in the inferior temporal cortex of macaque monkeys

Authors: *H. SHIOZAKI1, T. MOTONAGA2, H. TAMURA1, I. FUJITA1;
1Grad. Sch. of Frontier Biosci., 2Sch. of Engin. Sci., Osaka Univ., Toyonaka, Japan

Abstract: Correlations between the spike activities of different neurons are ubiquitous in the cerebral cortex, meaning that understanding the neural codes requires a description of the specific neuronal population activity. Recent studies have shown that spatial patterns describing the activity of tens of retinal ganglion cells and neurons in the primary visual cortex, a lower order visual area, can be explained by a model that considers the firing rates of individual cells and pairwise correlations between neurons without the need to incorporate higher-order correlations. However, it remains unclear whether pairwise interactions account for the concerted activity of neurons in higher-order visual areas where the dendritic morphology and local anatomical connections differ from those in early visual processing stages. To address this issue, we simultaneously recorded the spike activity of a population of neurons in the inferior temporal cortex of two monkeys under analgesia. We obtained data from 9 populations consisting of 23 to 45 neurons over 15 to 60 minutes. The probability of the number of neurons simultaneously active in the same 5-ms time bin systematically deviated from the predictions given by a model that assumes statistical independence between the activity of different neurons. As an alternative, we derived a maximum entropy model that takes into account pairwise correlations and the firing rates of individual neurons. Our resulting model captured 94% of the departures seen in the statistical independence model. We further tested our model by examining how well it predicts the probability distribution of instantaneous firing patterns in a 5-ms time bin. We randomly selected subpopulations consisting of 10 neurons and then derived the pairwise maximum entropy model for each. The pairwise maximum entropy models predicted 86% of the departures that occurred in the statistical independence models. From these results, we conclude that pairwise interactions account for the correlated activity of neurons in the inferior temporal cortex, similar to early visual areas, and speculate that synchronized firing in different visual areas have common characteristics despite their heterogeneous anatomical structures.