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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.

 

Recent publications:

 

Emergence of spike correlations in periodically forced excitable systems

J. A. Reinoso, M. C. Torrent, C. Masoller

Phys. Rev. E. 94, 032218 (2016).

 

Analysis of noise-induced temporal correlations in neuronal spike sequences

J. A. Reinoso, M. C. Torrent, C. Masoller

Eur. Phys. J. Special Topics 225, 2689–2696 (2016).

 

Funding:

Complex physical and biophysical systems: towards a comprehensive view of their dynamics and fluctuations (ComPhysBio, FIS2015-66503-C3-2-P) and ICREA ACADEMIA

 

  

 

Neuronal spikes simulated with the stochastic FitzHugh-Nagumo model.

 

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