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.

The brain can rapidly adapt to new contexts and learn from limited data, a coveted characteristic that artificial intelligence algorithms have struggled to mimic. Inspired by oscillatory rhythms of the mechanical structures of neural cells, we developed a learning paradigm that is based on oscillations in link strengths and associates learning with the coordination of these oscillations. We find that this paradigm yields rapid adaptation and learning in artificial neural networks. Link oscillations can rapidly change coordination, endowing the network with the ability to sense subtle context changes in an unsupervised manner. In other words, the network generates the missing contextual tokens required to perform as a generalist AI architecture capable of predicting dynamics in multiple contexts. Oscillations also allow the network to extrapolate dynamics to never-seen-before contexts. These capabilities make our learning paradigm a powerful starting point for novel models of learning and cognition. Furthermore, learning through link coordination is agnostic to the specifics of the neural network architecture, hence our study opens the door for introducing rapid adaptation and learning capabilities into leading AI models.