Name
Abstract | ||
|---|---|---|
The speed and reliability of mammalian perception indicate that cortical computations can rely on very few action potentials per involved neuron. Together with the stochasticity of single-spike events in cortex, this appears to imply that large populations of redundant neurons are needed for rapid computations with action potentials. Here we demonstrate that very fast and precise computations can be realized also in small networks of stochastically spiking neurons. We present a generative network model for which we derive biologically plausible algorithms that perform spike-by-spike updates of the neuron's internal states and adaptation of its synaptic weights from maximizing the likelihood of the observed spike patterns. Paradigmatic computational tasks demonstrate the online performance and learning efficiency of our framework. The potential relevance of our approach as a model for cortical computation is discussed. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1162/neco.2007.19.5.1313 | Neural Computation |
Keywords | Field | DocType |
stochastically spiking neuron,stochastic spikes,cortical computation,redundant neuron,efficient computation,internal state,mammalian perception,biologically plausible algorithm,large population,involved neuron,generative network model,action potential | Population,Models of neural computation,Artificial intelligence,Artificial neural network,Neuron,Machine learning,Network model,Mathematics,Computation | Journal |
Volume | Issue | ISSN |
19 | 5 | 0899-7667 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Udo Ernst | 1 | 13 | 3.99 |
| David Rotermund | 2 | 44 | 6.05 |
| Klaus Pawelzik | 3 | 509 | 107.71 |