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Synapse-Centric mapping of cortical models to the spiNNaker neuromorphic architecture
journal contribution
posted on 2023-06-09, 07:05 authored by James KnightJames Knight, Steve B FurberWhile the adult human brain has approximately 8.8 × 1010 neurons, this number is dwarfed by its 1 × 1015 synapses. From the point of view of neuromorphic engineering and neural simulation in general this makes the simulation of these synapses a particularly complex problem. SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Current solutions for simulating spiking neural networks on SpiNNaker are heavily inspired by work on distributed high-performance computing. However, while SpiNNaker shares many characteristics with such distributed systems, its component nodes have much more limited resources and, as the system lacks global synchronization, the computation performed on each node must complete within a fixed time step. We first analyze the performance of the current SpiNNaker neural simulation software and identify several problems that occur when it is used to simulate networks of the type often used to model the cortex which contain large numbers of sparsely connected synapses. We then present a new, more flexible approach for mapping the simulation of such networks to SpiNNaker which solves many of these problems. Finally we analyze the performance of our new approach using both benchmarks, designed to represent cortical connectivity, and larger, functional cortical models. In a benchmark network where neurons receive input from 8000 STDP synapses, our new approach allows 4× more neurons to be simulated on each SpiNNaker core than has been previously possible. We also demonstrate that the largest plastic neural network previously simulated on neuromorphic hardware can be run in real time using our new approach: double the speed that was previously achieved. Additionally this network contains two types of plastic synapse which previously had to be trained separately but, using our new approach, can be trained simultaneously.
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Publication status
- Published
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- Published version
Journal
Frontiers in NeuroscienceISSN
1662-453XPublisher
Frontiers MediaExternal DOI
Volume
10Article number
a420Department affiliated with
- Informatics Publications
Research groups affiliated with
- Sussex Neuroscience Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-07-18First Open Access (FOA) Date
2017-07-18First Compliant Deposit (FCD) Date
2017-07-18Usage metrics
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