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Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Fig 2

Learning a sequential dynamics stably under plasticity.

(A) The excitatory neurons receive sequential clustered inputs. Excitatory neurons are grouped in 30 disjoint clusters of 80 neurons each. (7 clusters shown in the cartoon for simplicity) (B) The weight matrix after training (only the first five clusters shown) exhibits the learned connectivity structure, e.g., neurons within cluster 1 are highly interconnected and also project to neurons in cluster 2, same for cluster 2 to cluster 3, etc. The spectrum of the full weight matrix after training shows most eigenvalues in a circle in the complex plane (as in a random graph) with two other eigenvalues signifying the balancing of the network, and a series of dominant eigenvalues in pairs that encode the feedforward embedding. (C) Raster plot of the total network consisting of 2400 excitatory (in red) and 600 inhibitory (in blue) neurons. After learning, the spontaneous dynamics exhibits a stable periodic trajectory ‘going around the clock’. The excitatory clusters discretize time (see zoom) and the network has an overall period of about 450 ms.

Fig 2

doi: https://doi.org/10.1371/journal.pcbi.1007606.g002