ArticleFrontiers in computational neuroscience2020
Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 34 citations in OpenAlex.
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- The gradient clusteron: A model neuron that learns to solve classification tasks via dendritic nonlinearities, structural plasticity, and gradient descent.PLoS computational biology · 2021Article
- The Subcortical-Allocortical- NeocorticalFrontiers in synaptic neuroscience · 2021Article
- Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell.Frontiers in computational neuroscience · 2020Article
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Authors and funding
2 authors at 1 institution in 1 country.
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Abstract
The perceptron learning algorithm and its multiple-layer extension, the backpropagation algorithm, are the foundations of the present-day machine learning revolution. However, these algorithms utilize a highly simplified mathematical abstraction of a neuron; it is not clear to what extent real biophysical neurons with morphologically-extended non-linear dendritic trees and conductance-based synapses can realize perceptron-like learning. Here we implemented the perceptron learning algorithm in a realistic biophysical model of a layer 5 cortical pyramidal cell with a full complement of non-linear dendritic channels. We tested this biophysical perceptron (BP) on a classification task, where it needed to correctly binarily classify 100, 1,000, or 2,000 patterns, and a generalization task, where it was required to discriminate between two "noisy" patterns. We show that the BP performs these tasks with an accuracy comparable to that of the original perceptron, though the classification capacity of the apical tuft is somewhat limited. We concluded that cortical pyramidal neurons can act as powerful classification devices.
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