Imposing Connectome-Derived Topology on an Echo State Network
This work addresses a problem for researchers in computational neuroscience and machine learning by exploring how biological brain connectivity can enhance reservoir computing, though it appears incremental as it applies an existing method to new data.
The study tackled the problem of whether connectome-derived constraints can improve computational performance by replacing the random reservoir layer of an Echo State Network with a fruit fly connectome-derived matrix, resulting in the Fruit Fly ESN either significantly outperforming or having lower variance than control ESNs in chaotic time series prediction tasks.
Can connectome-derived constraints inform computation? In this paper we investigate the contribution of a fruit fly connectome's topology on the performance of an Echo State Network (ESN) -- a subset of Reservoir Computing which is state of the art in chaotic time series prediction. Specifically, we replace the reservoir layer of a classical ESN -- normally a fixed, random graph represented as a 2-d matrix -- with a particular (female) fruit fly connectome-derived connectivity matrix. We refer to this experimental class of models (with connectome-derived reservoirs) as "Fruit Fly ESNs" (FFESNs). We train and validate the FFESN on a chaotic time series prediction task; here we consider four sets of trials with different training input sizes (small, large) and train-validate splits (two variants). We compare the validation performance (Mean-Squared Error) of all of the best FFESN models to a class of control model ESNs (simply referred to as "ESNs"). Overall, for all four sets of trials we find that the FFESN either significantly outperforms (and has lower variance than) the ESN; or simply has lower variance than the ESN.