Neuronal excitability

In nonlinear systems the presence of noise often facilitates the detection and transmission of weak input signals. Sensory neurons exploit this role of noise for encoding and transmitting weak external stimuli in sequences of spikes. Although the statistical properties of the spike sequences have been extensively investigated, the way in which single neurons encode information remains unclear. Single 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 spike timing. We use simple models of spiking neurons, such as the FitzHugh-Nagumo (FHN) model or the integrate-and-fire model, and analyze inter-spike-intervals (ISIs) correlations when the neuron is under the influence of noise, a subthreshold external input, and is coupled to other neurons.

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.

Key words: neural coding, temporal coding, inter-spike-intervals (ISI) correlations, excitability, models of spiking neurons, FitzHugh-Nagumo model, coherence resonance, stochastic resonance.

Involved Researchers: C. Masoller, J. Tiana-Alsina.

PhD Students: M. Masoliver

ICREA Academia. The aim of this project is (i) to exploit nonlinear dynamics and stochastic phenomena for novel applications and (ii) to develop advanced data analysis tools for studying the output signals of complex systems. A specific research objective is aimed at exploiting the optical spikes emitted by a semiconductor laser with optical feedback or injection, for implementing photonic neurons that mimic biological ones, but operate in times scales that are several orders of magnitude faster.

Scientific coordinator: Masoller, C.

Funding Agency: Institució Catalana de Recerca i Estudis Avançats.

Project No: ICREA ACADEMIA 2015-04.

Start/Ends dates: 01/01/2016 - 31/12/2020

Sistemas dinámicos complejos y herramientas avanzadas de análisis de datos.

This project studies nonlinear and stochastic phenomena in a broad class of systems including information processing by optical networks, extreme events in complex systems, neuronal excitability and brain dynamics, among many others.

Scientific coordinator: C. Masoller.

Funding Agency: Agencia estatal de investigación.

Participants: A. J. Pons.

Project ref. No.: PGC2018-099443-B-I00.

Start/Ends dates: 01/01/2019 - 31/12/2021.