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Neuronal excitability

 

In nonlinear systems the presence of noise often facilitates the detection and transmission of weak signals. Sensory neurons exploit noise for encoding and transmitting external signals in sequences of spikes. Although the statistical properties of the spike sequences have been extensively investigated, the way in which neurons encode information remains not fully understood. Among other mechanisms, neurons can encode information in the spike rate (“rate coding”) or in the timing of the spikes (“temporal coding”). We are interested in studying temporal correlations in the timing of neuronal spikes. We use neuronal models to analyze spike correlations when a neuron perceives a weak signal that is covered by background noise.

 

We are also interested in the comparison of the statistical properties of neuronal ISI sequences and those of the experimentally recorded optical spikes emitted by a semiconductor laser, which resemble neuronal spikes. Establishing a connection between these different dynamical systems can offer new perspectives in both, photonics and neuroscience. Laser-based photonic neurons can provide a novel, inexpensive and controllable experimental set up for improving our understanding of neuronal activity. On the other hand, laser-based photonic neurons can be building blocks of neuro-inspired, ultra-fast optical computing devices.

 

Recent publications:

 

Neuronal coupling benefits the encoding of weak periodic signals in symbolic spike patterns

M. Masoliver and C. Masoller

Commun. Nonlinear Sci. Numer. Simulat. 88, 105023 (2020). Arxiv: 1905.01933

 

Characterizing signal encoding and transmission in class I and class II neurons via ordinal time-series analysis

C. Estarellas, M. Masoliver, C. Masoller, C.R. Mirasso

Chaos 30, 013123 (2020). Arxiv:1908.01548

 

Subthreshold signal encoding in coupled FitzHugh-Nagumo neurons

M. Masoliver and C. Masoller

Scientific Reports 8, 8276 (2018).

 

Comparing the dynamics of periodically forced lasers and neurons

J. Tiana-Alsina, C. Quintero-Quiroz, C. Masoller

New J. of Phys 21, 103039 (2019).

 

 

Funding:

 

ICREA ACADEMIA

 

Ministerio de Ciencia, Innovación y Universidades, PGC2018-099443-B-I00

 

 

 

Neuronal spikes simulated with the stochastic FitzHugh-Nagumo model.

 

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