Neuromorphic computing systems have emerged as powerful computation tools in the field
of object recognition and control systems. However, training these systems, which are …

A class of brain-inspired recurrent algorithms known as<; i> reservoir computing (RC)
networks<;/i> reduces the computational complexity and cost of training machine-learning …

Equipping edge devices with intelligent behavior opens up new possibilities for automating
the decision making in extreme size, weight, and power-constrained application domains …

Abstract Spiking Neural Networks (SNNs) are the third generation of neural networks that
incorporate the notion of time in their model. SNNs are starting to be deployed in …

Echo state networks are computationally lightweight reservoir models inspired by the
random projections observed in cortical circuitry. As interest in reservoir computing has …

Liquid state machine (LSM) is a type of recurrent spiking network with a strong relationship
to neurophysiology and has achieved great success in time series processing. However, the …

The deep neural network is an intriguing prognostic model capable of learning meaningful
patterns that generalize to new data. The deep learning paradigm has been widely adopted …