Emergent rate-based dynamics in duplicate-free populations of spiking neurons
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917829944541184 |
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| author | Schmutz, Valentin Brea, Johanni Gerstner, Wulfram |
| author_facet | Schmutz, Valentin Brea, Johanni Gerstner, Wulfram |
| contents | Can Spiking Neural Networks (SNNs) approximate the dynamics of Recurrent Neural Networks (RNNs)? Arguments in classical mean-field theory based on laws of large numbers provide a positive answer when each neuron in the network has many "duplicates", i.e. other neurons with almost perfectly correlated inputs. Using a disordered network model that guarantees the absence of duplicates, we show that duplicate-free SNNs can converge to RNNs, thanks to the concentration of measure phenomenon. This result reveals a general mechanism underlying the emergence of rate-based dynamics in large SNNs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_05174 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Emergent rate-based dynamics in duplicate-free populations of spiking neurons Schmutz, Valentin Brea, Johanni Gerstner, Wulfram Neurons and Cognition Can Spiking Neural Networks (SNNs) approximate the dynamics of Recurrent Neural Networks (RNNs)? Arguments in classical mean-field theory based on laws of large numbers provide a positive answer when each neuron in the network has many "duplicates", i.e. other neurons with almost perfectly correlated inputs. Using a disordered network model that guarantees the absence of duplicates, we show that duplicate-free SNNs can converge to RNNs, thanks to the concentration of measure phenomenon. This result reveals a general mechanism underlying the emergence of rate-based dynamics in large SNNs. |
| title | Emergent rate-based dynamics in duplicate-free populations of spiking neurons |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2303.05174 |