Year: 2021
Type: article
Abstract: "Sparse" neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. While, in machine learning, "sparsity" is related to a penalty term that leads to some connecting weights becoming small or zero, in biological brains, sparsity is often created when high spiking thresholds prevent neuronal activity. Here, we introduce sparsity into a reservoir computing network via neuron-specific learnable thresholds of activity, allowing neurons with low thresholds to contribute to decision-making but suppressing information from neurons with high thresholds. This approach, which we term "SpaRCe," optimizes the sparsity level of the reservoir without affecting the reservoir dynamics. The read-out weights and the thresholds are learned by an online gradient rule that minimizes an error function on the outputs of the network. Threshold learning occurs by the balance of two opposing forces: reducing interneuronal correlations in the reservoir by deactivating redundant neurons, while increasing the activity of neurons participating in correct decisions. We test SpaRCe on classification problems and find that threshold learning improves performance compared to standard reservoir computing. SpaRCe alleviates the problem of catastrophic forgetting, a problem most evident in standard echo state networks (ESNs) and recurrent neural networks in general, due to increasing the number of task-specialized neurons that are included in the network decisions.
Authors: Luca Manneschi, Andrew C. Lin, Eleni Vasilaki
Institution: University of Sheffield
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,045
Awards: G1305261330, Magnetic Architectures for Reservoir Computing Hardware (MARCH), G259231907, From Stochasticity to Functionality: Probabilistic Computation with Magnetic Nanowires, Brains on Board: Neuromorphic Control of Flying Robots, Anti-memories through compartmentalised activity in a single neuron in a Drosophila memory centre, Homeostatic balancing of excitation and inhibition in vivo, ActiveAI - active learning and selective attention for robust, transparent and efficient AI
OpenAlex ID: W3119055764