Neuro-inspired electronic skin for robots

Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin–like or peripheral nervous system–like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics

Mixed signal VLSI circuit implementation of the cortical microcircuit models

This thesis proposes a novel set of generic and compact biologically plausible VLSI (Very Large Scale Integration) neural circuits, suitable for implementing a parallel VLSI network that closely resembles the function of a small-scale neocortical network. The proposed circuits include a cortical neuron, two different long-term plastic synapses and four different short-term plastic synapses. These circuits operate in accelerated-time, where the time scale of neural responses is approximately three to four orders of magnitude faster than the biological-time scale of the neuronal activities, providing higher computational throughput in computing neural dynamics. Further, a novel biological-time cortical neuron circuit with similar dynamics as of the accelerated-time neuron is proposed to demonstrate the feasibility of migrating accelerated-time circuits into biological-time circuits. The fabricated accelerated-time VLSI neuron circuit is capable of replicating distinct firing patterns such as regular spiking, fast spiking, chattering and intrinsic bursting, by tuning two external voltages. It reproduces biologically plausible action potentials. This neuron circuit is compact and enables implementation of many neurons in a single silicon chip. The circuit consumes extremely low energy per spike (8pJ). Incorporating this neuron circuit in a neural network facilitates diverse non-linear neuron responses, which is an important aspect in neural processing. Two of the proposed long term plastic synapse circuits include spike-time dependent plasticity (STDP) synapse, and dopamine modulated STDP synapse. The short-term plastic synapses include excitatory depressing, inhibitory facilitating, inhibitory depressing, and excitatory facilitating synapses. Many neural parameters of short- and long- term synapses can be modified independently using externally controlled tuning voltages to obtain distinct synaptic properties. Having diverse synaptic dynamics in a network facilitates richer network behaviours such as learning, memory, stability and dynamic gain control, inherent in a biological neural network. To prove the concept in VLSI, different combinations of these accelerated-time neural circuits are fabricated in three integrated circuits (ICs) using a standard 0.35 µm CMOS technology. Using first two ICs, functions of cortical neuron and STDP synapses have been experimentally verified. The third IC, the Cortical Neural Layer (CNL) Chip is designed and fabricated to facilitate cortical network emulations. This IC implements neural circuits with a similar composition to the cortical layer of the neocortex. The CNL chip comprises 120 cortical neurons and 7 560 synapses. Many of these CNL chips can be combined together to form a six-layered VLSI neocortical network to validate the network dynamics and to perform neural processing of small-scale cortical networks. The proposed neuromorphic systems can be used as a simulation acceleration platform to explore the processing principles of biological brains and also move towards realising low power, real-time intelligent computing devices and control systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo