Synaptic Normalisation for On-Chip Learning in Analog CMOS Spiking Neural Networks

Spiking Neural Networks (SNNs) are becoming increasingly popular for their application in Edge Artificial Intelligence (Edge-AI) due to their sparse and low-latency computation. Among these networks, analog hardware SNNs are chosen for their ability to emulate complex dynamics in neurons and synapses, especially in integrated Metal Oxide Semiconductor (MOS) technology. They can form memories of external stimuli by modulating the strength of synaptic weights. In this context, binary weights are a common hardware design choice, due to their ease to program and store. The use of binary weights in SNNs worsens the bias introduced by the coding level of input stimuli (i.e. fraction of active input nodes), where the network activity is highly correlated to the number of excited neurons. In this paper, we present a Complementary Metal Oxide Semiconductor (CMOS) solution for the coding level bias, by proposing a novel circuit that employs synaptic normalisation at the neuron level. This circuit modifies the gain of the neuron depending on its input weights, with a small footprint and therefore high scalability

Finding the Gap:Neuromorphic Motion Vision in Cluttered Environments

Many animals meander in environments and avoid collisions. How the underlying neuronal machinery can yield robust behaviour in a variety of environments remains unclear. In the fly brain, motion-sensitive neurons indicate the presence of nearby objects and directional cues are integrated within an area known as the central complex. Such neuronal machinery, in contrast with the traditional stream-based approach to signal processing, uses an event-based approach, with events occurring when changes are sensed by the animal. Contrary to von Neumann computing architectures, event-based neuromorphic hardware is designed to process information in an asynchronous and distributed manner. Inspired by the fly brain, we model, for the first time, a neuromorphic closed-loop system mimicking essential behaviours observed in flying insects, such as meandering in clutter and gap crossing, which are highly relevant for autonomous vehicles. We implemented our system both in software and on neuromorphic hardware. While moving through an environment, our agent perceives changes in its surroundings and uses this information for collision avoidance. The agent's manoeuvres result from a closed action-perception loop implementing probabilistic decision-making processes. This loop-closure is thought to have driven the development of neural circuitry in biological agents since the Cambrian explosion. In the fundamental quest to understand neural computation in artificial agents, we come closer to understanding and modelling biological intelligence by closing the loop also in neuromorphic systems. As a closed-loop system, our system deepens our understanding of processing in neural networks and computations in biological and artificial systems. With these investigations, we aim to set the foundations for neuromorphic intelligence in the future, moving towards leveraging the full potential of neuromorphic systems.Comment: 7 main pages with two figures, including appendix 26 pages with 14 figure

Robust Spiking Attractor Networks with a Hard Winner-Take-All Neuron Circuit

Attractor networks are widely understood to be a re-occurring primitive that underlies cognitive function. Stabilising activity in spiking attractor networks however remains a difficult task, especially when implemented in analog integrated circuits (aIC). We introduce here a novel circuit implementation of a hard Winner-Take-All (hWTA) mechanism, in which competing neurons' refractory circuits are coupled together, and thus their spiking is forced to be mutually exclusive. We demonstrate stable persistent-firing attractor dynamics in a small on-chip network consisting of hWTA-connected neurons and excitatory recurrent synapses. Its utility within larger networks is demonstrated in simulation, and shown to support overlapping attractors and be robust to synaptic weight mismatch. The realised hWTA mechanism is thus useful for stabilising activity in spiking networks composed of unreliable components, without the need for careful parameter tuning

Neuromorphic capacitive tactile sensors inspired by slowly adaptive mechanoreceptors

The sense of touch is essential in our everyday life as it allows us to interact with our environment. The same applies to robots and users of prostheses but requires sensing solutions that are power efficient and allow edge and embedded computation. In this paper, we present a capacitive, neuromorphic, event-driven, tactile sensor. The mixed-mode subthreshold circuit is implemented in 180nm technology and achieves a sensitivity of ≈ 30Hz/N in simulation with the SPICE simulation platform spectre

Constraints on the design of neuromorphic circuits set by the properties of neural population codes

In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain. To facilitate the cross-talk between neuromorphic engineering and neuroscience, in this review we first critically examine and summarize emerging recent findings about how population of neurons encode and transmit information. We examine the effects on encoding and readout of information for different features of neural population activity, namely the sparseness of neural representations, the heterogeneity of neural properties, the correlations among neurons, and the timescales (from short to long) at which neurons encode information and maintain it consistently over time. Finally, we critically elaborate on how these facts constrain the design of information coding in neuromorphic circuits. We focus primarily on the implications for designing neuromorphic circuits that communicate with the brain, as in this case it is essential that artificial and biological neurons use compatible neural codes. However, we also discuss implications for the design of neuromorphic systems for implementation or emulation of neural computation