publication . Article . Other literature type . 2013

Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.

Nessler, B; Pfeiffer, M; Büsing, L; Maass, W;
Closed Access
  • Published: 25 Apr 2013 Journal: PLoS Computational Biology (eissn: 1553-7358, Copyright policy)
  • Country: Switzerland
Abstract
Author Summary How do neurons learn to extract information from their inputs, and perform meaningful computations? Neurons receive inputs as continuous streams of action potentials or “spikes” that arrive at thousands of synapses. The strength of these synapses - the synaptic weight - undergoes constant modification. It has been demonstrated in numerous experiments that this modification depends on the temporal order of spikes in the pre- and postsynaptic neuron, a rule known as STDP, but it has remained unclear, how this contributes to higher level functions in neural network architectures. In this paper we show that STDP induces in a commonly found connectivit...
Subjects
arXiv: Quantitative Biology::Neurons and Cognition
free text keywords: Institute of Neuroinformatics, 570 Life sciences; biology, Ecology, Modelling and Simulation, Computational Theory and Mathematics, Genetics, Ecology, Evolution, Behavior and Systematics, Molecular Biology, Cellular and Molecular Neuroscience, Spike-timing-dependent plasticity, Bayesian probability, Information processing, Neuroscience, Nerve net, medicine.anatomical_structure, medicine, Bayes' theorem, Artificial intelligence, business.industry, business, Prior probability, Artificial neural network, Computer science, Computation, Research Article, Biology, Computational Biology, Computational Neuroscience, Circuit Models, Developmental Neuroscience, Synaptic Plasticity
Funded by
EC| SECO
Project
SECO
Self-Constructing Computing Systems
  • Funder: European Commission (EC)
  • Project Code: 216593
  • Funding stream: FP7 | SP1 | ICT
,
EC| BRAINSCALES
Project
BRAINSCALES
Brain-inspired multiscale computation in neuromorphic hybrid systems
  • Funder: European Commission (EC)
  • Project Code: 269921
  • Funding stream: FP7 | SP1 | ICT
,
EC| BRAIN-I-NETS
Project
BRAIN-I-NETS
Novel Brain-Inspired Learning Paradigms for Large-Scale Neuronal Networks
  • Funder: European Commission (EC)
  • Project Code: 243914
  • Funding stream: FP7 | SP1 | ICT
Communities
FET FP7FET Proactive: FET proactive 8: Brain Inspired ICT
FET FP7FET Proactive: Brain-inspired multiscale computation in neuromorphic hybrid systems
FET FP7FET Proactive: FET proactive: Bio-ICT convergence
FET FP7FET Proactive: Self-Constructing Computing Systems
113 references, page 1 of 8

1 Rao RPN, Olshausen BA, Lewicki MS (2002) Probabilistic Models of the Brain. MIT Press.

2 Doya K, Ishii S, Pouget A, Rao RPN (2007) Bayesian Brain: Probabilistic Approaches to Neural Coding. MIT-Press.

3 Griffiths TL, Tenenbaum JB (2006) Optimal predictions in everyday cognition. Psychological Science 17: 767–773.16984293 [PubMed]

4 Griffiths TL, Kemp C, Tenenbaum JB (2008) Bayesian models of cognition. In: Sun R, editor. Handbook of Computational Cognitive Modeling. Cambridge Univ. Press. chapter 3. p. 59100.

5 Körding KP, Wolpert DM (2004) Bayesian integration in sensorimotor learning. Nature 427: 244–247.14724638 [OpenAIRE] [PubMed]

6 Fiser J, Berkes P, Orban G, Lengyel M (2010) Statistically optimal perception and learning: from behavior to neural representation. Trends in Cogn Sciences 14: 119–130. [OpenAIRE]

7 Ma WJ, Beck JM, Latham PE, Pouget A (2006) Bayesian inference with probabilistic population codes. Nature Neuroscience 9: 1432–1438.17057707 [PubMed]

8 Dan Y, Poo M (2004) Spike timing-dependent plasticity of neural circuits. Neuron 44: 23–30.15450157 [OpenAIRE] [PubMed]

9 Feldman D (2012) The spike-timing dependence of plasticity. Neuron 75: 556–571.22920249 [OpenAIRE] [PubMed]

10 Song S, Abbott LF (2001) Cortical developing and remapping through spike timing-dependent plasticity. Neuron 32: 339–350.11684002 [PubMed]

11 Kempter R, Gerstner W, van Hemmen JL (1999) Hebbian learning and spiking neurons. Phys Rev E 59: 4498–4514. [OpenAIRE]

12 Kempter R, Gerstner W, van Hemmen JL (2001) Intrinsic stabilization of output rates by spikebased Hebbian learning. Neural Computation 13: 2709–2741.11705408 [PubMed]

13 Abbott LF, Nelson SB (2000) Synaptic plasticity: taming the beast. Nature Neuroscience 3: 1178–1183.11127835 [PubMed]

14 Daoudal G, Debanne D (2003) Long-term plasticity of intrinsic excitability: learning rules and mechanisms. Learn Mem 10: 456–465.14657257 [OpenAIRE] [PubMed]

15 Grillner S, Graybiel A (2006) Microcircuits: The Interface between Neurons and Global Brain Function. MIT-Press.

113 references, page 1 of 8
Abstract
Author Summary How do neurons learn to extract information from their inputs, and perform meaningful computations? Neurons receive inputs as continuous streams of action potentials or “spikes” that arrive at thousands of synapses. The strength of these synapses - the synaptic weight - undergoes constant modification. It has been demonstrated in numerous experiments that this modification depends on the temporal order of spikes in the pre- and postsynaptic neuron, a rule known as STDP, but it has remained unclear, how this contributes to higher level functions in neural network architectures. In this paper we show that STDP induces in a commonly found connectivit...
Subjects
arXiv: Quantitative Biology::Neurons and Cognition
free text keywords: Institute of Neuroinformatics, 570 Life sciences; biology, Ecology, Modelling and Simulation, Computational Theory and Mathematics, Genetics, Ecology, Evolution, Behavior and Systematics, Molecular Biology, Cellular and Molecular Neuroscience, Spike-timing-dependent plasticity, Bayesian probability, Information processing, Neuroscience, Nerve net, medicine.anatomical_structure, medicine, Bayes' theorem, Artificial intelligence, business.industry, business, Prior probability, Artificial neural network, Computer science, Computation, Research Article, Biology, Computational Biology, Computational Neuroscience, Circuit Models, Developmental Neuroscience, Synaptic Plasticity
Funded by
EC| SECO
Project
SECO
Self-Constructing Computing Systems
  • Funder: European Commission (EC)
  • Project Code: 216593
  • Funding stream: FP7 | SP1 | ICT
,
EC| BRAINSCALES
Project
BRAINSCALES
Brain-inspired multiscale computation in neuromorphic hybrid systems
  • Funder: European Commission (EC)
  • Project Code: 269921
  • Funding stream: FP7 | SP1 | ICT
,
EC| BRAIN-I-NETS
Project
BRAIN-I-NETS
Novel Brain-Inspired Learning Paradigms for Large-Scale Neuronal Networks
  • Funder: European Commission (EC)
  • Project Code: 243914
  • Funding stream: FP7 | SP1 | ICT
Communities
FET FP7FET Proactive: FET proactive 8: Brain Inspired ICT
FET FP7FET Proactive: Brain-inspired multiscale computation in neuromorphic hybrid systems
FET FP7FET Proactive: FET proactive: Bio-ICT convergence
FET FP7FET Proactive: Self-Constructing Computing Systems
113 references, page 1 of 8

1 Rao RPN, Olshausen BA, Lewicki MS (2002) Probabilistic Models of the Brain. MIT Press.

2 Doya K, Ishii S, Pouget A, Rao RPN (2007) Bayesian Brain: Probabilistic Approaches to Neural Coding. MIT-Press.

3 Griffiths TL, Tenenbaum JB (2006) Optimal predictions in everyday cognition. Psychological Science 17: 767–773.16984293 [PubMed]

4 Griffiths TL, Kemp C, Tenenbaum JB (2008) Bayesian models of cognition. In: Sun R, editor. Handbook of Computational Cognitive Modeling. Cambridge Univ. Press. chapter 3. p. 59100.

5 Körding KP, Wolpert DM (2004) Bayesian integration in sensorimotor learning. Nature 427: 244–247.14724638 [OpenAIRE] [PubMed]

6 Fiser J, Berkes P, Orban G, Lengyel M (2010) Statistically optimal perception and learning: from behavior to neural representation. Trends in Cogn Sciences 14: 119–130. [OpenAIRE]

7 Ma WJ, Beck JM, Latham PE, Pouget A (2006) Bayesian inference with probabilistic population codes. Nature Neuroscience 9: 1432–1438.17057707 [PubMed]

8 Dan Y, Poo M (2004) Spike timing-dependent plasticity of neural circuits. Neuron 44: 23–30.15450157 [OpenAIRE] [PubMed]

9 Feldman D (2012) The spike-timing dependence of plasticity. Neuron 75: 556–571.22920249 [OpenAIRE] [PubMed]

10 Song S, Abbott LF (2001) Cortical developing and remapping through spike timing-dependent plasticity. Neuron 32: 339–350.11684002 [PubMed]

11 Kempter R, Gerstner W, van Hemmen JL (1999) Hebbian learning and spiking neurons. Phys Rev E 59: 4498–4514. [OpenAIRE]

12 Kempter R, Gerstner W, van Hemmen JL (2001) Intrinsic stabilization of output rates by spikebased Hebbian learning. Neural Computation 13: 2709–2741.11705408 [PubMed]

13 Abbott LF, Nelson SB (2000) Synaptic plasticity: taming the beast. Nature Neuroscience 3: 1178–1183.11127835 [PubMed]

14 Daoudal G, Debanne D (2003) Long-term plasticity of intrinsic excitability: learning rules and mechanisms. Learn Mem 10: 456–465.14657257 [OpenAIRE] [PubMed]

15 Grillner S, Graybiel A (2006) Microcircuits: The Interface between Neurons and Global Brain Function. MIT-Press.

113 references, page 1 of 8
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publication . Article . Other literature type . 2013

Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.

Nessler, B; Pfeiffer, M; Büsing, L; Maass, W;