Name
Abstract | ||
|---|---|---|
Sensory stimuli are usually composed of different features (the what) appearing at irregular times (the when). Neural responses often use spike patterns to represent sensory information. The what is hypothesized to be encoded in the identity of the elicited patterns (the pattern categories), and the when, in the time positions of patterns (the pattern timing). However, this standard view is oversimplified. In the real world, the what and the when might not be separable concepts, for instance, if they are correlated in the stimulus. In addition, neuronal dynamics can condition the pattern timing to be correlated with the pattern categories. Hence, timing and categories of patterns may not constitute independent channels of information. In this paper, we assess the role of spike patterns in the neural code, irrespective of the nature of the patterns. We first define information-theoretical quantities that allow us to quantify the information encoded by different aspects of the neural response. We also introduce the notion of synergy/redundancy between time positions and categories of patterns. We subsequently establish the relation between the what and the when in the stimulus with the timing and the categories of patterns. To that aim, we quantify the mutual information between different aspects of the stimulus and different aspects of the response. This formal framework allows us to determine the precise conditions under which the standard view holds, as well as the departures from this simple case. Finally, we study the capability of different response aspects to represent the what and the when in the neural response. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3389/fncom.2010.00145 | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE |
Keywords | Field | DocType |
patterns,neural code,sensory encoding,information theory,redundancy,synergy,stimulus features,feature extractor | Information theory,Computer science,Neural coding,Redundancy (engineering),Mutual information,Artificial intelligence,Stimulus (physiology),Sensory system,Machine learning,Independent channels | Journal |
Volume | ISSN | Citations |
4 | Frontiers in Computational Neuroscience 4:145 (2010) | 5 |
PageRank | References | Authors |
0.47 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hugo Gabriel Eyherabide | 1 | 5 | 0.81 |
| Inés Samengo | 2 | 45 | 8.37 |