  The liquid state machine (LSM) combines low training complexity and
biological plausibility, which has made it an attractive machine learning
framework for edge and neuromorphic computing paradigms. Originally proposed as
a model of brain computation, the LSM tunes its internal weights without
backpropagation of gradients, which results in lower performance compared to
multi-layer neural networks. Recent findings in neuroscience suggest that
astrocytes, a long-neglected non-neuronal brain cell, modulate synaptic
plasticity and brain dynamics, tuning brain networks to the vicinity of the
computationally optimal critical phase transition between order and chaos.
Inspired by this disruptive understanding of how brain networks self-tune, we
propose the neuron-astrocyte liquid state machine (NALSM) that addresses
under-performance through self-organized near-critical dynamics. Similar to its
biological counterpart, the astrocyte model integrates neuronal activity and
provides global feedback to spike-timing-dependent plasticity (STDP), which
self-organizes NALSM dynamics around a critical branching factor that is
associated with the edge-of-chaos. We demonstrate that NALSM achieves
state-of-the-art accuracy versus comparable LSM methods, without the need for
data-specific hand-tuning. With a top accuracy of 97.61% on MNIST, 97.51% on
N-MNIST, and 85.84% on Fashion-MNIST, NALSM achieved comparable performance to
current fully-connected multi-layer spiking neural networks trained via
backpropagation. Our findings suggest that the further development of
brain-inspired machine learning methods has the potential to reach the
performance of deep learning, with the added benefits of supporting robust and
energy-efficient neuromorphic computing on the edge.
