Combining various types of spike timing-dependent plasticity to learn efficient neural codes
Authors: Antony W. N’dri, Céline Teulière and Jochen Triesch
Presentation type: Short talk at SNUFA 2023 online workshop (7-8 Nov 2023)
Biological spiking neural networks learn efficient representations of sensory inputs with local learning rules. Mimicking such abilities in brain-inspired computing approaches is still a grand challenge. Here we present a learning framework for hierarchical spiking neural networks called predictive coding light (PCL). PCL uses both excitatory and inhibitory spike timing-dependent plasticity (STDP) rules in a recurrent hierarchical network architecture to learn an efficient redundancy-reducing representation of sensory inputs. When exposed to naturalistic visual input, our PCL network learns simple and complex cell-like receptive fields and exhibits surround suppression effects as observed biologically. We systematically compare different STDP-rules (causal, anti-causal, symmetric) for different connection types within the PCL network. Our results indicate that the variety of biologically observed STDP rules enhances the learning abilities of hierarchical spiking neural networks.
Watch it on Youtube