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Identifier

000443669

Title

Biologically constrained spiking neural network for image classification

Alternative Title

Βιολογικά εμπνευσμένο spiking neural network για διαχωρισμό εικόνων

Author

Μαλακάσης, Νικόλαος

Thesis advisor

Ποϊράζη Παναγιώτα

Reviewer

Τοπάλης, Παντελής
Παυλίδης, Παύλος

Abstract

In the modern era of machine learning and artificial intelligence, artificial neural networks are widely used as the “brains” for smart software applications. Even though these algorithms are inspired by neurobiology, they lack features that neuronal circuits utilize to facilitate learning while employing learning rules that are not yet proven to be biologically plausible. In the presented study, we hypothesized that a biologically constrained neural network could perform image classification by utilizing network mechanisms and plasticity rules employed in the brain during learning. The network model includes an input layer and a single hidden layer that consists of multicompartmental spiking neurons with active dendrites, both excitatory pyramidals and inhibitory interneurons. Input neurons are sparsely connected to the hidden layer, and learning occurs by utilizing a class-specific learning signal and a synaptic tag-and-capture plasticity rule. We show that this biological network can perform binary image classification while maintaining the efficiency benefits of biological circuits. Moreover, this work highlights how structural plasticity improves performance by allowing the network to maximize its capacity. This model is arguably restricted due to computational inefficiency on regular hardware, which could be solved by implementing it on a neuromorphic platform. However, it can still serve as a template to test the advantages of biologically-inspired architectures when compared to deep learning algorithms, like its ability to learn continually. i

Language

English

Subject

Artificial intelligence

Brain

Dendrites

Learning

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Εγκέφαλος

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τεχνιτή νοημοσύνη

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