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Biophysical parameters control signal transfer in spiking network.
Tomás Garnier Artiñano1,2, Vafa Andalibi3, Iiris Atula1,2
1Helsinki University Hospital (HUS) Neurocenter, Neurology, Helsinki University Hospital, Helsinki, Finland.
Frontiers in Computational Neuroscience
|February 10, 2023
Summary
This study reveals how neuron properties and synaptic delays impact information transfer in spiking neural networks. Biophysical parameters significantly influence information metrics, with classification being robust even at low firing rates.
Area of Science:
- Computational Neuroscience
- Neural Networks
- Information Theory
Background:
- Information transmission in biological and artificial networks relies on connectivity.
- Understanding how neuronal properties influence information transfer in neural populations is crucial.
Purpose of the Study:
- To investigate the impact of basic membrane properties and synaptic delays on information transfer in spiking neural networks.
- To build upon existing models of spiking neural networks that represent continuous signals.
Main Methods:
- Utilized a spiking neural network with leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units.
- Incorporated synaptic delays and concurrent action potential generation.
- Evaluated information transmission using coherence, Granger causality, transfer entropy, and reconstruction error across three parameter regimes.
Main Results:
- Biophysical parameters significantly affected information transfer metrics.
- Classification accuracy remained robust even at low firing and information rates.
- Information transmission and reconstruction error were dependent on higher firing rates in LIF units, while AdEx units showed lower firing rates and information transfer.
Conclusions:
- Findings suggest information transfer qualities may be a phenomenological property of biological cells.
- Results can inform predictive coding theories of the cerebral cortex.
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