Plasticity and Learning Algorithms in Models of the Single Neuron - August 25, 2022

The neurons we use in today’s deep learning systems are extremely simple compared to their biological counterparts. In this meeting, guest speaker Toviah Moldwin explored the abilities of a single biological neuron with extended dendrites and synapses and introduced a potential biological model of plasticity that might enable better learning in neural networks.

Work done by: Toldvin Moldwin, Menachem Kalmenson, Li Shay Azran, and Idan Segev from the Edmond and Lily Safra Center for Brain Sciences (ELSC) at the Hebrew University of Jerusalem

Topics covered & related papers:
1/ Biophysical Perceptron
➤ “Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell”: https://www.frontiersin.org/articles/...

2/ The Gradient Clusteron (G-Clusteron)
➤ “The gradient clusteron: A model neuron that learns to solve classification tasks via dendritic nonlinearities, structural plasticity, and gradient descent”: https://journals.plos.org/ploscompbio...

3/ Fixed Point - Learning Rate (FPLR) Framework for Calcium

4/ The Calcitron

5/ Hierarchical Heterosynaptic Plasticity
➤ "Asymmetric voltage attenuation in dendrites enables hierarchical heterosynaptic plasticity”: https://www.biorxiv.org/content/10.11...
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