Sensitivity to Stimulus Irregularity Is Inherent in Neural Networks
Teun van Gils1, Paul H E Tiesinga2, Bernhard Englitz3
1Department of Neuroinformatics and Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, 6525 AJ Nijmegen, Gelderland, The Netherlands edu@teunvg.nl.
Neural Computation
|July 24, 2019
Summary
Neural networks exhibit sensitivity to stimulus irregularity (SSI), driven by short interspike intervals (ISIs), not complex patterns. This property, crucial for behavior, appears to be a general neural network characteristic.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Neural Networks
Background:
- Complex neural networks process numerous inputs to control behavior.
- Even brief spike trains in single cortical neurons can influence behavior.
- Irregular interspike intervals (ISIs) show a more reliable behavioral influence than regular patterns.
Discussion:
- Simulated spiking neural networks reveal mechanisms of sensitivity to stimulus irregularity (SSI).
- Irregular stimulation generates more detectable network bursts than regular stimulation.
- Short ISIs, prevalent in irregular stimuli, are identified as the primary drivers of SSI.
Key Insights:
- Short-term plasticity modulates SSI.
- SSI is retained even when inhibitory interneurons are removed, indicating independence from inhibition.
- SSI persists without network recurrence, attributed to individual neurons' short-term integration ('cell memory').
- SSI is maintained without single-neuron dynamics, due to recurrent network activity ('network memory').
Outlook:
- SSI is a general property, likely ubiquitous in diverse neural networks.
- The findings suggest a functional role for irregular neural activity in the cortex.
- SSI's underlying mechanism (short ISIs) is general and may apply to various neural systems.
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