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Learning Long Sequences in Spiking Neural Networks
Spiking neural networks (SNNs) take inspiration from the brain to enable energy-efficient computations.
Ensembles of Spiking Neural Networks
The work demonstrates how ensembling can overcome the challenges of producing individual SNN models which can compete with traditional deep neural networks, and creates systems with fewer trainable parameters and smaller memory footprints, opening the door to low-power edge applications, e. g. implemented on neuromorphic hardware.
Spiking Associative Memory for Spatio-Temporal Patterns
The encoding and learning rules are demonstrated in the design of a single-layer associative memory (an input layer consisting of 3, 200 spiking neurons fully-connected to a similar sized population of memory neurons), which we simulate and characterise.
SpiNNTools: The Execution Engine for the SpiNNaker Platform
Distributed systems are becoming more common place, as computers typically contain multiple computation processors.
