Characterizing Learning in Spiking Neural Networks with Astrocyte-Like Units

• URL: http://arxiv.org/abs/2503.06798v1
• Date: Sun, 09 Mar 2025 22:36:58 GMT
• Title: Characterizing Learning in Spiking Neural Networks with Astrocyte-Like Units
• Authors: Christopher S. Yang, Sylvester J. Gates III, Dulara De Zoysa, Jaehoon Choe, Wolfgang Losert, Corey B. Hart,
• Abstract summary: We introduce a modified spiking neural network model with added astrocyte-like units in a neural network.<n>We show that the combination of neurons and astrocytes together, as opposed to neural- and astrocyte-only networks, are critical for driving learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional artificial neural networks take inspiration from biological networks, using layers of neuron-like nodes to pass information for processing. More realistic models include spiking in the neural network, capturing the electrical characteristics more closely. However, a large proportion of brain cells are of the glial cell type, in particular astrocytes which have been suggested to play a role in performing computations. Here, we introduce a modified spiking neural network model with added astrocyte-like units in a neural network and asses their impact on learning. We implement the network as a liquid state machine and task the network with performing a chaotic time-series prediction task. We varied the number and ratio of neuron-like and astrocyte-like units in the network to examine the latter units effect on learning. We show that the combination of neurons and astrocytes together, as opposed to neural- and astrocyte-only networks, are critical for driving learning. Interestingly, we found that the highest learning rate was achieved when the ratio between astrocyte-like and neuron-like units was roughly 2 to 1, mirroring some estimates of the ratio of biological astrocytes to neurons. Our results demonstrate that incorporating astrocyte-like units which represent information across longer timescales can alter the learning rates of neural networks, and the proportion of astrocytes to neurons should be tuned appropriately to a given task.

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