Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks

This paper presents a hardware based implementation of a biologically-faithful astrocyte-based selfrepairing mechanism for Spiking Neural Networks. Spiking Astrocyte-neuron Networks (SANNs) are a new computing paradigm which capture the key mechanisms of how the human brain performs repairs. Using SANN in hardware affords the potential for realizing computing architecture that can self-repair. This paper demonstrates that Spiking Astrocyte Neural Network (SANN) in hardware have a resilience to significant levels of faults. The key novelty of the paper resides in implementing an SANN on FPGAs using fixed-point representation and demonstrating graceful performance degradation to different levels of injected faults via its self-repair capability. A fixed-point implementation of astrocyte, neurons and tripartite synapses are presented and compared against previous hardware floating-point and Matlab software implementations of SANN. All results are obtained from the SANN FPGA implementation and show how the reduced fixedpoint representation can maintain the biologically-realistic repair capability

Self-repairing mobile robotic car using astrocyte-neuron networks

A self-repairing robot utilising a spiking astrocyte-neuron network is presented in this paper. It uses the output spike frequency of neurons to control the motor speed and robot activation. A software model of the astrocyte-neuron network previously demonstrated self-detection of faults and its self-repairing capability. In this paper the application demonstrator of mobile robotics is employed to evaluate the fault-tolerant capabilities of the astrocyte-neuron network when implemented in a hardware-based robotic car system. Results demonstrated that when 20% or less synapses associated with a neuron are faulty, the robot car can maintain system performance and complete the task of forward motion correctly. If 80% synapses are faulty, the system performance shows a marginal degradation, however this degradation is much smaller than that of conventional fault-tolerant techniques under the same levels of faults. This is the first time that astrocyte cells merged within spiking neurons demonstrates a self-repairing capabilities in the hardware system for a real application

Case Study - Spiking Neural Network Hardware System for Structural Health Monitoring

This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead

Exploring Self-Repair in a Coupled Spiking Astrocyte Neural Network

It is now known that astrocytes modulate the activity at the tripartite synapses where indirect signaling via the retrograde messengers, endocannabinoids, leads to a localized self-repairing capability. In this paper, a self-repairing spiking astrocyte neural network (SANN) is proposed to demonstrate a distributed self-repairing capability at the network level. The SANN uses a novel learning rule that combines the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules (hereafter referred to as the BSTDP rule). In this learning rule, the synaptic weight potentiation is not only driven by the temporal difference between the presynaptic and postsynaptic neuron firing times but also by the postsynaptic neuron activity. We will show in this paper that the BSTDP modulates the height of the plasticity window to establish an input-output mapping (in the learning phase) and also maintains this mapping (via self-repair) if synaptic pathways become dysfunctional. It is the functional dependence of postsynaptic neuron firing activity on the height of the plasticity window that underpins how the proposed SANN self-repairs on the fly. The SANN also uses the coupling between the tripartite synapses and γ -GABAergic interneurons. This interaction gives rise to a presynaptic neuron frequency filtering capability that serves to route information, represented as spike trains, to different neurons in the subsequent layers of the SANN. The proposed SANN follows a feedforward architecture with multiple interneuron pathways and astrocytes modulate synaptic activity at the hidden and output neuronal layers. The self-repairing capability will be demonstrated in a robotic obstacle avoidance application, and the simulation results will show that the SANN can maintain learned maneuvers at synaptic fault densities of up to 80% regardless of the fault locations