Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors

Flexible Unipolar IGZO Transistor-Based Integrate and Fire Neurons for Spiking Neuromorphic Applications

In this article, three different implementations of an Axon-Hillock circuit are presented, one of the basic building blocks of spiking neural networks. In this work, we explored the design of such circuits using a unipolar thin-film transistor technology based on amorphous InGaZnO, often used for large-area electronics. All the designed circuits are fabricated by direct material deposition and patterning on top of a flexible polyimide substrate. Axon-Hillock circuits presented in this article consistently show great adaptability of the basic properties of a spiking neuron such as output spike frequency adaptation and output spike width adaptation. Additional degrees of adaptability are demonstrated with each of the Axon-Hillock circuit varieties: neuron circuit threshold voltage adaptation, differentiation between input signal importance, and refractory period modulation. The proposed neuron can change its firing frequency up to three orders of magnitude by varying a single voltage brought to a circuit terminal. This allows the neuron to function, and potentially learn, at vastly different timescales that coincide with the biologically meaningful timescales, going from milliseconds to seconds, relevant for circuits meant for interaction with the environment. Thanks to careful design choices, the average measured power consumption is kept in the nW range, realistically allowing upscaling towards the spiking neural networks in the future. The spiking neuron with refractory period modulation presented in this work has an area of 607.3 μm × 492.2 μm, it experimentally demonstrated firing rates as low as 11.926 mHz, and its energy consumption per spike is ≈ 700 pJ at 30 Hz.

Bio-Inspired Architectures Substantially Reduce the Memory Requirements of Neural Network Models

We propose a neural network model for the jumping escape response behavior observed in the cricket cercal sensory system. This sensory system processes low-intensity air currents in the animal's immediate environment generated by predators, competitors, and mates. Our model is inspired by decades of physiological and anatomical studies. We compare the performance of our model with a model derived through a universal approximation, or a generic deep learning, approach, and demonstrate that, to achieve the same performance, these models required between one and two orders of magnitude more parameters. Furthermore, since the architecture of the bio-inspired model is defined by a set of logical relations between neurons, we find that the model is open to interpretation and can be understood. This work demonstrates the potential of incorporating bio-inspired architectural motifs, which have evolved in animal nervous systems, into memory efficient neural network models.