Fig. 7: Training result with a memory implanted in the network.
a Random recurrent E/I network with a neural ensemble (size = 32, green neurons). b Convergence of both excitatory (Pyr) and inhibitory (PV) populations to their respective set-points. A subset of the weights is manually changed to instantiate a new ensemble at iteration 250, well after the network has converged to its set-point. A jump in activity is observed in both Pyr and PV cells due to the presence of the newly implanted memory. c Schematic illustration of the process of re-balancing a weight matrix after implanting a new memory, instantiated as a network of six neurons containing an ensemble of three neurons. This is only indicative, as in the current chip it is not possible to read the weight matrix for visualization. d The nominal weight value for 32 neurons (wee) is increased by 0.0088 μA at the time point highlighted with the dotted line. All weights undergo alteration again to compensate for the presence of the new ensemble, bringing the network back to the set-point after the memory is implanted. However, the memory remains intact, as evidenced by higher recurrent weight (wmemory) compared to the baseline excitatory connectivity (wee).
