Hideaki Shimazaki (RIKEN Brain Science Institute, Japan)

Simultaneous silence explains structured higher-order interactions of neural population

Begin: Tue, Apr 28, 2015 17:00 - 18:00
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BCCN Berlin
Lecture hall
Philippstr. 13, Haus 6
10115 Berlin



Collective spiking activity of neurons is the basis of information processing in the brain. However, characterizing population activity is non-trivial because the number of activity patterns combinatorially increases with the number of neurons. To infer the statistical structure of neural activity from limited amount of data, the maximum entropy principle has been successfully applied. Under this principle, the probability distribution of activity patterns is estimated to be the least structured distribution that is consistent with a set of observed activity statistics. The maximum entropy distribution is characterized by interaction parameters of different orders, where the orders refer to the numbers of subset neurons that these parameters constrain. Interactions beyond the 2nd order are collectively termed higher-order interactions (HOIs). Although earlier studies demonstrated that the maximum entropy model that includes up to the 2nd order interactions explains a major variability of population activity [Schneidman et al. Nature 2006; Shlens et al. J Neurosci 2006], recent studies reported that neural populations express statistically significant HOIs, and they are relevant for information coding [Ohiorhenuan et. al. Nature 2010; Ganmor et al. PNAS 2011; Shimazaki et al. PLOS CB 2012; Tkacik et al. PLOS CB 2014]. However, the previous studies have not identified a key feature in HOIs that summarizes a principal role of seemingly diverse HOIs. 

Here we examined HOIs in population activity of the hippocampal CA3 networks in cultured slices [Shimazaki et al. Sci Rep in press]. To investigate the structure of HOIs, we propose a maximum entropy model that adds a single HOI parameter that accounts for the level of simultaneous silence (SS) of all neurons to the previously proposed pairwise maximum entropy model. This single parameter introduces structured HOIs with alternating signs with respect to the order of interactions, namely, positive pairwise interactions followed by negative triple-wise interactions, and then positive quadruple-wise interactions and so on. Using this model, we found that most groups of neurons that expressed HOIs exhibited significantly longer periods of SS than predicted by the pairwise maximum entropy model. Indeed, about ~20% of the entropy due to HOIs in these groups was explained by the single SS term of HOIs. We then directly confirmed presence of the specific oscillatory structure of HOIs predicted from the SS in the population activity of the hippocampal neurons. Through a modeling approach, we also demonstrate that population activity caused by correlated inputs and nonlinear thresholding reproduces the same structure of HOIs, and that this structure conveys information of input. The ubiquitous structure of HOI observed in the activities of both experimental and model neural populations suggests that neurons are operating in a unique regime where they are constrained to be silent simultaneously.

Organized by

Klaus Obermayerr

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