Fig. 1
From: Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training
Network protocol overview and training results. (a) The spiking networks consist of three unique fully connected layers. The input layer consists of excitatory spike generators. The hidden layer consists of LIF neurons that have either excitatory or inhibitory weight connections to the output layer of leaky-integrators. (b) Sample Fashion-MNIST activity of the network starting with the input layer. Spikes of the hidden layer are indicated with a raster plot inset. Hidden layer activity is then propagated to the output where the highest voltage trace corresponds to the networks chosen class (drawn in black). (c) Network ability to train on the Fashion-MNIST dataset relative to their initial hidden layer firing rate. The different activity levels correspond to the different initial weight distributions. Every trial corresponds to an individual point on its respective graph. Training consisted of 4 different E:I ratios (left to right 50:50, 80:20, 95:5, 100:0). The black line at 86% accuracy shows the best single trial accuracy of previous training of a network of the same size and training but with unbounded weights14 (d) Network ability to train on the SHD dataset across a range of initial hidden layer firing rates. From left to right 4 E:I ratios: 50:50, 80:20, 95:5, 100:0. The black line at 48% accuracy represents a network of the same size but with unbounded weights.
