Name
Abstract | ||
|---|---|---|
It is well known that some neurons tend to fire packets of action potentials followed by periods of quiescence (bursts) while others within the same stage of sensory processing fire in a tonic manner. However, the respective computational advantages of bursting and tonic neurons for encoding time varying signals largely remain a mystery. Weakly electric fish use cutaneous electroreceptors to convey information about sensory stimuli and it has been shown that some electroreceptors exhibit bursting dynamics while others do not. In this study, we compare the neural coding capabilities of tonically firing and bursting electroreceptor model neurons using information theoretic measures. We find that both bursting and tonically firing model neurons efficiently transmit information about the stimulus. However, the decoding mechanisms that must be used for each differ greatly: a non-linear decoder would be required to extract all the available information transmitted by the bursting model neuron whereas a linear one might suffice for the tonically firing model neuron. Further investigations using stimulus reconstruction techniques reveal that, unlike the tonically firing model neuron, the bursting model neuron does not encode the detailed time course of the stimulus. A novel measure of feature detection reveals that the bursting neuron signals certain stimulus features. Finally, we show that feature extraction and stimulus estimation are mutually exclusive computations occurring in bursting and tonically firing model neurons, respectively. Our results therefore suggest that stimulus estimation and feature extraction might be parallel computations in certain sensory systems rather than being sequential as has been previously proposed. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1023/B:JCNS.0000037677.58916.6b | Journal of Computational Neuroscience |
Keywords | Field | DocType |
electroreceptor,burst,information theory,feature detection,neuron | Bursting,Neuroscience,Tonic (music),Neural coding,Artificial intelligence,Theta model,Stimulus (physiology),Neuron,Sensory system,Mathematics,Machine learning,Sensory processing | Journal |
Volume | Issue | ISSN |
17 | 2 | 0929-5313 |
Citations | PageRank | References |
8 | 0.71 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maurice J. Chacron | 1 | 67 | 7.19 |
| André Longtin | 2 | 260 | 47.87 |
| Leonard Maler | 3 | 78 | 11.44 |