Bayesian spiking neurons II: learning
Abstract
In the companion letter in this issue (“Bayesian Spiking Neurons I: Inference”), we showed that the dynamics of spiking neurons can be interpreted as a form of Bayesian integration, accumulating evidence over time about events in the external world or the body. We proceed to develop a theory of Bayesian learning in spiking neural networks, where the neurons learn to recognize temporal dynamics of their synaptic inputs. Meanwhile, successive layers of neurons learn hierarchical causal models for the sensory input. The corresponding learning rule is local, spike-time dependent, and highly nonlinear. This approach provides a principled description of spiking and plasticity rules maximizing information transfer, while limiting the number of costly spikes, between successive layers of neurons.
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Many examples exist in which Bayesian models can account for neural data.
“There have been many examples in which Bayesian models can account for the neural data.27”
From Computational psychiatry: a Rosetta Stone linking the brain to mental illness (2014) · cites this paper as [27]
Such examples are subtly different from proof of how the system works.
“However, such examples are subtly different from proof of how the system works.”
From Computational psychiatry: a Rosetta Stone linking the brain to mental illness (2014) · cites this paper as [27]
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- Authors
- Deneve, S.
- Affiliations
- Group for Neural Theory, Département d'Etudes Cognitives, Ecole Normale Supérieure, College de France 75005 Paris, France
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 2008-01
- Volume
- 20
- Issue
- 1
- Pages
- 118-145
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2144540603
- Cited by
- 71
- References
- 33
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 6 · 2021: 4 · 2018: 1 · 2017: 3 · 2016: 11 · 2015: 3 · 2014: 6 · 2013: 7 · 2012: 7
- FWCI
- 2.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Gatsby Charitable Foundation
- Ministry of Education, Culture, Sports, Science and Technology
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- spiking neurons, Bayesian learning, spike-timing-dependent plasticity, spiking neural networks, information transmission, learning rules, Bayesian inference
- MeSH terms
- Action Potentials, Algorithms, Animals, Bayes Theorem, Central Nervous System, Computer Simulation, Humans, Learning, Markov Chains, Nerve Net, Synaptic Transmission, Neuronal Plasticity, Neurons, Time Perception, Poisson Distribution, Neural Networks, Computer, Nonlinear Dynamics
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