Name
Abstract | ||
|---|---|---|
Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1371/journal.pcbi.1004984 | PLOS COMPUTATIONAL BIOLOGY |
Field | DocType | Volume |
Population,Biological system,Computer science,Models of neural computation,Artificial intelligence,Subthreshold conduction,Stimulus (physiology),Sensory system,Artificial neural network,Synaptic potential,Neural decoding,Genetics,Machine learning | Journal | 12 |
Issue | ISSN | Citations |
6 | 1553-734X | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huang Chao | 1 | 68 | 17.03 |
| Andrey Resnik | 2 | 1 | 0.36 |
| Tansu Celikel | 3 | 1 | 0.36 |
| Bernhard Englitz | 4 | 12 | 2.27 |