Abstract
In this paper, we propose a novel approach for implementing spiking neurons through an optical system. Spiking neurons are a new approach to emulate the neural processes that occur in the brain, known as the third generation of artificial neural networks. They have been successfully used to build a new technology called neuromorphic engineering, which looks for a better performance than traditional computing in tasks usually performed by AI-based systems. Our optical system consists of a low-cost laser source, based on a microcontroller and a continuous-wave laser diode; the microcontroller allows producing synchronous or asynchronous pulses with complex time profiles. Here, through said system we have successfully reproduced most of the neural dynamics observed in biological neurons. These dynamics have been reproduced using a very simple optical array with a great potential for the development of neuromorphic systems. The optical system has been experimentally validated.
Highlights
In the last few decades, spiking neurons, one of the main branches of the computational neuroscience, have been successfully used to model some important regions of the brain [1,2,3] due to their biological plausibility
Due to the fact that NEURON is written in C, it offers a high computational efficiency, which is of particular interest for large neural networks with complex neuron models, e.g., the Hodgkin and Huxley model
We reproduced as the first result, from a laser diode in continuous wave regime, the behaviour of a conventional pulsed laser
Summary
In the last few decades, spiking neurons, one of the main branches of the computational neuroscience, have been successfully used to model some important regions of the brain [1,2,3] due to their biological plausibility This biological plausibility has different levels of complexity, from the most complex in the the Hodgkin and Huxley model [4] to the simpler approach in the Izhikevich model [5]. The validation of a neuron model is given by its capacity to reproduce as many neuronal firing patterns as possible To facilitate such validation, several software packages have been developed. BRIAN [7] is another widely used simulator, which compared with NEURON is better at simulating spiking neurons This fact is mainly due to BRIAN being written in Python, a high level description language with a large community supporting and developing new and more efficient libraries every day. We have NEST, a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual
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