P188 Neural coding of subthreshold sinusoidal inputs into symbolic temporal spike patterns
Maria Masoliver1,Cristina Masoller*1 1Departmento de Física, Universitat Politecnica de Catalunya, Terrassa, Spain *Email: cristina.masoller@upc.edu
Introduction
Neuromorphic photonics is a new paradigm for optical computing that can revolutionize the fields of signal processing and artificial intelligence.To develop photonic neurons able to process information as sensory neurons do, we need to identify excitable lasers able to emit pulses of light (optical spikes) that similar to neuronal spikes, and implement in these lasers the neural coding mechanisms used by neural systems to process information, in particular, the neural coding mechanisms used to process weak external inputs in noisy environments. Methods We use thestochastic FitzHugh Nagumo model to simulate spike sequences fired in response to weak (subthreshold) sinusoidal signals.We also use this model to simulate the activity of a population of neurons, when they all perceive the same subthreshold sinusoidal input. We use a symbolic time series analysis method, known as ordinal analysis [1], to analyze the sequences of inter-spike-intervals.
Results
In the analysis of the spikes of single neurons, we found that the probabilities of the symbols (ordinal patterns) encode information of the signal, because they depend on both, the amplitude and the frequency of the signal. In the analysis of the spike generated by a population of neurons, we have also found that the ordinal probabilities encode information of the amplitude and of the period of the signal that is perceived by the neurons. We found that neuronal coupling benefits signal encoding because groups of neurons are able to encode a small-amplitude signal that cannot be encoded when it is perceived by just one or two neurons. Interestingly, we found that for a population of neurons, just a few random links between them can significantly improve signal encoding. Discussion
We have found that the probabilities of spike patterns in spike sequences may encode information of a weak (subthreshold) input perceived by the neurons. An open question is whether this coding mechanism can be implemented in excitable lasers that emit pulses of light (optical spikes) whose statistical properties are similar to neuronal spikes.Using ordinal analysis and machine learning, we have found that the sequences of optical spikes emitted by a laser diode in response to low or high frequency signals are located in different regions of a 3D feature space, suggesting that information about the frequency of the input signal, can be recovered from the analysis of the emitted optical spikes [3].
Figure 1. Left: Optical spikes emitted by an excitable laser (nanosecond time scale); right: neuronal spikes simulated with the FitzHugh Nagumo model (millisecond time scale). Acknowledgements Ministerio de Ciencia, Innovación y Universidades (No. PID2021-123994NB-C21), Institució Catalana de Recerca i Estudis Avançats (ICREA Academia), Agencia de Gestió d’Ajuts Universitaris i de Recerca (AGAUR, No. 2021 SGR 00606). References [1] Bandt C, Pompe B. (2002). Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett., 88, 174102.https://doi.org/10.1103/PhysRevLett.88.174102
[2] Masoliver M, Masoller C (2020). Neuronal coupling benefits the encoding of weak periodic signals in symbolic spike patterns. Commun. Nonlinear Sci. Numer. Simulat. 88, 105023.https://doi.org/10.1016/j.cnsns.2019.105023
[3] Boaretto BRR, Macau EEN, Masoller C (2024). Characterizing the spike timing of a chaotic laser by using ordinal analysis and machine learning, Chaos 34, 043108.https://doi.org/10.1063/5.0193967
