Uncovering Network Architecture Using an Exact Statistical Input-Output Relation of a Neuron Model
Rashid Shomali, S.; Nili Ahmadabadi, M.; Rasuli, S. N.; Shimazaki, H.
Show abstract
Using observed neuronal activity, we try to unveil hidden microcircuits. A key requirement is the knowledge of statistical input-output relation of single neurons in vivo. We use a recent exact solution of spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near threshold, and construct a framework that links synaptic type/strength, and spiking nonlinearity, with statistics of neuronal activity. The framework explains structured higher-order interactions of neurons receiving common inputs under different architectures. Comparing models prediction with an empirical dataset of monkey V1 neurons, we find that excitatory inputs to pairs explain the observed sparse activity characterized by negative triple-wise interactions, ruling out the intuitive shared inhibition. We show that the strong interactions are in general the signature of excitatory rather than inhibitory inputs whenever spontaneous activity is low. Finally, we present a guide map that can be used to reveal the hidden motifs underlying observed interactions found in empirical data.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Correlations in population dynamics in multi-component networks 98%
- Postsynaptic frequency filters shaped by the interplay of synaptic short-term plasticity and cellular time scales 97%
- Intersegmental coordination of the central pattern generator via interleaved electrical and chemical synapses in zebrafish spinal cord 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.