Abstract
It is generally known that the computational neuroscience is usually considered as a mathematics-driven model. Computational neuroscience maps the functions of the neural populations to computer-based mathematical functions. Presently, numerous efforts have been taken to provide neural models that mimic the biological functions of neurons. This endeavor suggests the need for deploying comprehensive object-based basic neural models in the place of mathematical models that has been implemented by just using object-oriented programming languages. This paper describes the object-based model (OBM) and how it is different from the models using object-oriented languages. The consistent premise ‘Everything is not learning’ is set and is justified by the biological supports produced here. This paper also discusses about the alternate methods adopted by various researchers to prove that only a complete object-based design of neurons will have convergence toward the objective that mimic the actions of brain. This paper analyzes the need for different programming paradigms and concludes with a suggestion that the adaptive programming language ‘Go’ is a language that suits the implementation of such a model.
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Krishnan, R., Murugan, A. (2021). Object-Based Neural Model in Multicore Environments with Improved Biological Plausibility. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_2
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