A Hardware-friendly Neuromorphic Spiking Neural Network for Frequency Detection and Fine Texture Decoding

Humans can distinguish fabrics by their textures, even when they are finer than the density of tactile sensors. Evidence suggests that this ability is produced by the nervous system using an active touch strategy. When the finger slides over a texture, the nervous system converts the texture’s spatial period into an equivalent spiking frequency. Many studies focused on modeling the biological encoding part that translates the spatial frequency into a temporal spiking frequency, but few explored the decoding part. In this work, we propose a novel approach based on a spiking neural network able to detect the frequency of an input signal. Inspired by biological evidence, our architecture detects the range in which the encoded frequency dwells and could therefore decode the texture’s spatial period. The network has been designed to be composed of existing neuromorphic spiking primitives. This property enables a straightforward implementation on integrated silicon circuits, allowing the texture decoding at the edge of the sensor

Skin inspired flexible and stretchable electrospun carbon nanofiber sensors for neuromorphic sensing

During the past few decades, a significant amount of research effort has been dedicated toward developing skin-inspired sensors for real-time human motion monitoring and next-generation robotic devices. Although several flexible and wearable sensors have been developed in the past, the need of the hour is developing accurate, reliable, sophisticated, facile yet inexpensive flexible sensors coupled with neuromorphic systems or spiking neural networks to encode tactile information without the need for complex digital architectures, thus achieving true skin-like sensing with limited resources. In this work, we propose an approach entailing carbon nanofiber-polydimethylsiloxane composite-based piezoresistive sensors, coupled with spiking neural networks, to mimic skin-like sensing. The strain and pressure sensors have been combined with appropriately designed neural networks to encode analog voltages to spikes to recreate bioinspired tactile sensing and proprioception. To further validate the proprioceptive capability of the system, a gesture tracking smart glove, combined with a spiking neural network, was demonstrated. Wearable and flexible sensors with accompanying neural networks such as the ones proposed in this work will pave the way for a future generation of skin-mimetic sensors for advanced prosthetic devices, apparel integrable smart sensors for human motion monitoring, and human-machine interfaces.</p

Artificial Bio-inspired Tactile Receptive Fields for Edge Orientation Classification

Robots and users of hand prosthesis could easily manipulate objects if endowed with the sense of touch. Towards this goal, information about touched objects and surfaces has to be inferred from raw data coming from the sensors. An important cue for objects discrimination is the orientation of edges, that is used both in artificial vision and touch as pre-processing stage. We present a spiking neural network, inspired on the encoding of edges in human first order tactile afferents. The network uses three layers of Leaky Integrate and Fire neurons to distinguish different edge orientations of a bar pressed on the artificial skin of the iCub robot. The architecture is successfully able to discriminate eight different orientations (from 0o to 180o), by implementing a structured model of overlapping receptive fields. We demonstrate that the network can learn the appropriate connectivity through unsupervised spike based learning, and that the number and spatial distribution of sensitive areas within the receptive fields are important in edge orientation discrimination

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

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

Experimental validation of an analog spiking neural network with STDP learning rule in CMOS technology

We report the design in CMOS technology and the experimental characterization of an analog spiking neural network with on-chip unsupervised learning. Long-term synaptic memory is implemented using a floating-gate device in a standard 150 nm CMOS process. The neurons are operated with a voltage supply of only 0.4V, allowing an extremely low power dissipation with an energy dissipation per synaptic operation of about 55 fJ. The CMOS chip includes the circuits for implementing real-time learning of the network based on the Spike Time Dependent Plasticity algorithm. During the learning, the neurons produce pulses of ±4.5 V that change the synaptic weight by activating tunneling currents to change the charge in the floating gates