A gradient learning rule for the tempotron

Neural Computation

Volumn 21, Issue 2, 2009, Pages 340-352

  Urbanczik, Robert ^a   Senn, Walter ^a  

^a UNIVERSITY OF BERN   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ANIMAL; ARTICLE; BIOLOGICAL MODEL; HUMAN; LEARNING; NERVE CELL; NERVE CELL NETWORK; PHYSIOLOGY; TIME;

ACTION POTENTIALS; ANIMALS; HUMANS; LEARNING; MODELS, NEUROLOGICAL; NERVE NET; NEURONS; TIME FACTORS;

EID: 67650286597     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2008.09-07-605     Document Type: Article

References (7)

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3. Florian, R. (2007). Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity. Neural Computation, 19, 1468-1502.
4. Gütig, R., & Sompolinsky, H. (2006). The tempotron: A neuron that learns spike timing-based decision. Nature Neuroscience, 9, 420-428.
5. Herz, A., Gollisch, T., Machens, C., & Jaeger, D. (2006). Modeling single-neuron dynamics and computations: A balance of detail and abstraction. Science, 314, 80-85.
6. Pfister, J., Toyoizumi, T., Barber, D., & Gerstner, W. (2006). Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. Neural Computation, 18, 1318-1348.
7. Seung, H. (2003). Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron, 40, 1063-1073.