Name
Abstract | ||
|---|---|---|
. Most of current neural network architectures are not suited to recognize a pattern at various displaced positions. This lack
seems due to the prevailing neuron model which reduces a neuron's information transmission to its firing rate. With this information
code, a neuronal assembly cannot distinguish between different combinations of its entities and therefore fails to represent
the fine structure within a pattern. In our approach, the main idea of the correlation theory is accepted that spatial relationships
in a pattern should be coded by temporal relations in the timing of action potentials. However, we do not assume that synchronized
spikes are a sign for strong synapses between the neurons concerned. Instead, the synchronization of Synfire chains can be
exploited to produce the relevant timing relationships between the neuronal signals. Therefore, we do not require fast synaptic
plasticity to account for the precise timing of action potentials. In order to illustrate this claim, we propose a model for
translation-invariant pattern recognition which does not depend on any changes in synaptic efficacies. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1007/s004220050537 | Biological Cybernetics |
Keywords | Field | DocType |
Firing Rate,Synaptic Plasticity,Spatial Relationship,Network Architecture,Information Transmission | Synapse,Synchronization,Biological neuron model,Network architecture,Information transmission,Synaptic plasticity,Artificial intelligence,Artificial neural network,Neuron,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
80 | 6 | 0340-1200 |
Citations | PageRank | References |
5 | 0.56 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hans-Martin R. Arnoldi | 1 | 15 | 3.69 |
| Karl-hans Englmeier | 2 | 49 | 14.79 |
| Wilfried Brauer | 3 | 969 | 299.36 |