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| Paper IPM / Cognitive / 15923 |
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Recognizing network architecture exploiting the recorded activity of neurons only, is a challenge in neuroscience. In general, this is a hard task; but an analytic tool can help us to narrow down possible scenarios and approach the architecture behind the activity. Recently, researchers have found a statistical pre-post relation for a Leaky-Integrate-and-Fire (LIF) neuron when the neuron receives signaling input on top of noisy background inputs near the threshold regime [1]. We use this analytic relation in mixture models to investigate the effect of shared inputs on network architecture. It connects synaptic inputs and statistics of population activity (pairwise and higher-order correlations) to network architecture in basic symmetric and asymmetric motifs; this lets us identify the underlying network architecture. We use it to address the architecture behind sparse population activity, reported for monkey's V1 neurons [2]. Comparing the theoretical graphs with the experimental data [2,3], we determine whether the underlying architecture is symmetric or asymmetric, whether synapses are excitatory or inhibitory, and more. We also consider the possibility of recurrent activities among neurons; which we categorize in 16 main motifs. The study on shared inputs and recurrent connections shows the main structure in which one excitatory common input gives synapses to a pair of neurons is responsible for the observed strong negative triple-wise correlations. This is in contrast with the intuitive expectation that shared inhibition causes the observed sparse activity. Finally, we ask why excitatory to pairs, and not inhibitory based architectures, induce such strong correlations in the aforementioned experiment. We attribute it to the sparseness of neuronal activity: When neurons are predominantly silent; an excitatory input causes a more significant change compared to any inhibitory one.
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