Digital Implementation of Bio-Inspired Spiking Neuronal Networks

Spiking Neural Network as the third generation of artificial neural networks offers a promising solution for future computing, prosthesis, robotic and image processing applications. This thesis introduces digital designs and implementations of building blocks of a Spiking Neural Networks (SNNs) including neurons, learning rule, and small networks of neurons in the form of a Central Pattern Generator (CPG) which can be used as a module in control part of a bio-inspired robot. The circuits have been developed using Verilog Hardware Description Language (VHDL) and simulated through Modelsim and compiled and synthesised by Altera Qurtus Prime software for FPGA devices. Astrocyte as one of the brain cells controls synaptic activity between neurons by providing feedback to neurons. A novel digital hardware is proposed for neuron-synapseastrocyte network based on the biological Adaptive Exponential (AdEx) neuron and Postnov astrocyte cell model. The network can be used for implementation of large scale spiking neural networks. Synthesis of the designed circuits shows that the designed astrocyte circuit is able to imitate its biological model and regulate the synapse transmission, successfully. In addition, synthesis results confirms that the proposed design uses less than 1% of available resources of a VIRTEX II FPGA which saves up to 4.4% of FPGA resources in comparison to other designs. Learning rule is an essential part of every neural network including SNN. In an SNN, a special type of learning called Spike Timing Dependent Plasticity (STDP) is used to modify the connection strength between the spiking neurons. A pair-based STDP (PSTDP) works on pairs of spikes while a Triplet-based STDP (TSTDP) works on triplets of spikes to modify the synaptic weights. A low cost, accurate, and configurable digital architectures are proposed for PSTDP and TSTDP learning models. The proposed circuits have been compared with the state of the art methods like Lookup Table (LUT), and Piecewise Linear approximation (PWL). The circuits can be employed in a large-scale SNN implementation due to their compactness and configurability. Most of the neuron models represented in the literature are introduced to model the behavior of a single neuron. Since there is a large number of neurons in the brain, a population-based model can be helpful in better understanding of the brain functionality, implementing cognitive tasks and studying the brain diseases. Gaussian Wilson-Cowan model as one of the population-based models represents neuronal activity in the neocortex region of the brain. A digital model is proposed for the GaussianWilson-Cowan and examined in terms of dynamical and timing behavior. The evaluation indicates that the proposed model is able to generate the dynamical behavior as the original model is capable of. Digital architectures are implemented on an Altera FPGA board. Experimental results show that the proposed circuits take maximum 2% of the resources of a Stratix Altera board. In addition, static timing analysis indicates that the circuits can work in a maximum frequency of 244 MHz

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

Astrocyte control bursting mode of spiking neuron network with memristor-implemented plasticity

A mathematical model of a spiking neuron network accompanied by astrocytes is considered. The network is composed of excitatory and inhibitory neurons with synaptic connections supplied by a memristor-based model of plasticity. Another mechanism for changing the synaptic connections involves astrocytic regulations using the concept of tripartite synapses. In the absence of memristor-based plasticity, the connections between these neurons drive the network dynamics into a burst mode, as observed in many experimental neurobiological studies when investigating living networks in neuronal cultures. The memristive plasticity implementing synaptic plasticity in inhibitory synapses results in a shift in network dynamics towards an asynchronous mode. Next,it is found that accounting for astrocytic regulation in glutamatergic excitatory synapses enable the restoration of 'normal' burst dynamics. The conditions and parameters of such astrocytic regulation's impact on burst dynamics established

Master of Science

thesisPrevious studies indicate that the brain tissue reaction to implanted silicon recording microelectrode arrays involves hyperplasia of macrophages, microglia and astrocytes, and that these reactions are accompanied by a decrease in the density of neurons immediately surrounding the implant. It is generally believed that the foreign body response is a major factor in the inconsistent recording performance observed with these devices. To gain insight into the earliest events, we used immunohistochemical methods to characterize the cellular responses adjacent to implanted microelectrodes at 1 ,3 , and 7 days after implantation using single shank planar Michigan-style silicon microelectrode arrays implanted into the cortex of adult male Sprague Dawley rats (225-300g) with N=12 per time point. Electrodes were implanted using stereotactic positioning at +0.2 mm bregma, 3 mm lateral, and 2 mm deep and were anchored with photo-cured adhesive into a polyurethane grommet. As a control, stab wounds were created in the same method, with N=6 at 3 and 7 days. Animals were sacrificed by transcardial perfusion and serial sectioned with a vibrotome. Significant variability existed in the amount of surface hemorrhage and the presence of infracted blood vessels along the implantation tract. Activated macrophages were attached to explanted probes at all postimplantation periods. Activation o f perivascular macrophages and extravasation of ED1+ cells at the site of injury was evident by 1 day postimplantation. Macrophages were found at the implant-tissue interface at all time points and were most prevalent at 3 days. At as early as 1 day, GFAP+ astrocytes were absent from the implantation site to about 50 |im, which was maintained at days 3 and 7. There was a significant decrease in neuronal soma within 0-50 jiin of the electrode track for stab wound and implanted animals. However for implanted animals, the area of neuronal loss had increased to 0150 |itn at 7 days, suggesting that secondary neuronal cell death is part of the early phase of the foreign body response. Future studies may use pharmacological approaches to understand if these early events can be modulated to improve the longterm functionality o f microelectrodes for neuroprosthetic devices