The stochastic properties of input spike trains control neuronal arithmetic
Jazyk angličtina Země Německo Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
- MeSH
- akční potenciály fyziologie MeSH
- lidé MeSH
- modely neurologické * MeSH
- nervová síť fyziologie MeSH
- nervový útlum fyziologie MeSH
- neurony fyziologie MeSH
- počítačová simulace MeSH
- stochastické procesy * MeSH
- teorie pravděpodobnosti MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
In the nervous system, the representation of signals is based predominantly on the rate and timing of neuronal discharges. In most everyday tasks, the brain has to carry out a variety of mathematical operations on the discharge patterns. Recent findings show that even single neurons are capable of performing basic arithmetic on the sequences of spikes. However, the interaction of the two spike trains, and thus the resulting arithmetic operation may be influenced by the stochastic properties of the interacting spike trains. If we represent the individual discharges as events of a random point process, then an arithmetical operation is given by the interaction of two point processes. Employing a probabilistic model based on detection of coincidence of random events and complementary computer simulations, we show that the point process statistics control the arithmetical operation being performed and, particularly, that it is possible to switch from subtraction to division solely by changing the distribution of the inter-event intervals of the processes. Consequences of the model for evaluation of binaural information in the auditory brainstem are demonstrated. The results accentuate the importance of the stochastic properties of neuronal discharge patterns for information processing in the brain; further studies related to neuronal arithmetic should therefore consider the statistics of the interacting spike trains.
Citace poskytuje Crossref.org
Ergodicity and parameter estimates in auditory neural circuits