Extended Data Fig. 8: Characterisation of the recurrent network dynamics before and after learning. | Nature Neuroscience

Extended Data Fig. 8: Characterisation of the recurrent network dynamics before and after learning.

From: Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks

Extended Data Fig. 8

a, Histogram of the membrane potential of all excitatory neurons. b, Histogram of the inter-spike-interval (ISI) of all excitatory neurons. c, Histogram of the coefficient of variation (standard deviation divided by the mean) of the inter-spike-intervals (from panel b) for all excitatory neurons. d, Histogram of the average effective membrane time constant for all excitatory neurons. Effective membrane time constant of a neuron is defined as the neuron’s membrane time constant divided by the neuron’s total conductance. e, Pearson correlation between excitatory and inhibitory inputs onto an example excitatory neuron of the network.

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