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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
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Μαλακάσης, Νικόλαος
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Thesis advisor
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Ποϊράζη Παναγιώτα
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Reviewer
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Τοπάλης, Παντελής
Παυλίδης, Παύλος
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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
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Language |
English |
Subject |
Artificial intelligence |
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Brain |
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Dendrites |
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Learning |
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Δενδρίτες |
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Εγκέφαλος |
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Μάθηση |
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τεχνιτή νοημοσύνη |
Issue date |
2021-12-01 |
Collection
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School/Department--School of Medicine--Department of Medicine--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/8/6/4/metadata-dlib-1637572515-134066-30441.tkl
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Views |
323 |