Abstract
Current research in neuroscience has begun to shift perspective from neurons as sole information processors to including the astrocytes as equal and cooperating units in this function. Recent evidence sheds new light on astrocytes and presents them as important regulators of neuronal activity and synaptic plasticity. In this paper, we present a multi-layer perceptron (MLP) with artificial astrocyte units which listen to and regulate hidden neurons based on their activity. We test the behavior and performance of this bio-inspired model on two classification tasks, N-parity problem and the two-spirals problem and show that proposed models outperform the standard MLP. Interestingly, we have also discovered multiple regimes of astrocyte activity depending on the complexity of the problem.
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Acknowledgments
This work was supported by grant UK/256/2018 from Comenius University in Bratislava (P.G.) and Slovak Grant Agency for Science, project VEGA 1/0796/18 (I.F.)
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Appendix: Derivation of the update formula
Appendix: Derivation of the update formula
Here we derive formula for stochastic (online) update of astrocyte weights \(\alpha _i\) in models A-MLP(\(\alpha \)) and A-MLP(\(\alpha ,\theta ,\gamma \)). The goal is to minimize the loss function \(E(w) = 1/2 (d - y(x))^2\), by moving the astrocytic weights along the negative gradient, i.e. \(\varDelta \alpha _i = - \partial E(w)/ \partial \alpha _i\). Since E is differentiable with respect to \(\alpha _i\), we can write using the chain rule,
which yields the final formula:
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Gergel’, P., Farkaŝ, I. (2018). Investigating the Role of Astrocyte Units in a Feedforward Neural Network. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_8
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DOI: https://doi.org/10.1007/978-3-030-01424-7_8
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