Name
Title | ||
|---|---|---|
Optimal Information Decoding from Neuronal Populations with Specific Stimulus Selectivity |
Abstract | ||
|---|---|---|
A typical neuron in visual cortex receives most inputs from other cortical neurons with a roughly similar stimulus preference. Does this arrange- ment of inputs allow efficient readout of sensory information by the tar- get cortical neuron? We address this issue by using simple modelling of neuronal population activity and information theoretic tools. We find that efficient synaptic information transmission requires that the tuning curve of the afferent neurons is approximately as wide as the spread of stim- ulus preferences of the afferent neurons reaching the target neuron. By meta analysis of neurophysiological data we found that this is the case for cortico-cortical inputs to neurons in visual cortex. We suggest that the organization of V1 cortico-cortical synaptic inputs allows optimal in- formation transmission. |
Year | Venue | Keywords |
|---|---|---|
2004 | NIPS | meta analysis |
Field | DocType | Citations |
Population,Surround suppression,Visual cortex,Neurophysiology,Computer science,Artificial intelligence,Decoding methods,Stimulus (physiology),Sensory system,Neuron,Machine learning | Conference | 2 |
PageRank | References | Authors |
0.45 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcelo A. Montemurro | 1 | 182 | 19.95 |
| Stefano Panzeri | 2 | 404 | 62.09 |