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

A Survey of Spiking Neural Network Accelerator on FPGA

Due to the ability to implement customized topology, FPGA is increasingly used to deploy SNNs in both embedded and high-performance applications. In this paper, we survey state-of-the-art SNN implementations and their applications on FPGA. We collect the recent widely-used spiking neuron models, network structures, and signal encoding formats, followed by the enumeration of related hardware design schemes for FPGA-based SNN implementations. Compared with the previous surveys, this manuscript enumerates the application instances that applied the above-mentioned technical schemes in recent research. Based on that, we discuss the actual acceleration potential of implementing SNN on FPGA. According to our above discussion, the upcoming trends are discussed in this paper and give a guideline for further advancement in related subjects

Spiking Neural Networks: Modification and Digital Implementation

Real-time large-scale simulation of biological systems is a challenging task due to nonlinear functions describing biochemical reactions in the cells. Being fast, cost and power efficient alongside of capability to work in parallel have made hardware an attractive choice for simulation platform. This thesis proposes a neuromorphic platform for online Spike Timing Dependant Plasticity (STDP) learning, based on the COordinate Rotation DIgital Computer (CORDIC) algorithms. The implemented platform comprises two main components. First, the Izhikevich neuron model is modified for implementation using the CORDIC algorithm and simulated to ensure the model accuracy. Afterwards, the model was described as hardware and implemented on Field Programmable Gate Array (FPGA). Second, the STDP learning algorithm is adapted and optimized using the CORDIC method, synthesized for hardware, and implemented to perform on-FPGA online learning on a network of CORDIC Izhikevich neurons to demonstrate competitive Hebbian learning. The implementation results are compared with the original model and state-of-the-art to verify accuracy, effectiveness, and higher speed of the system. These comparisons confirm that the proposed neuromorphic system offers better performance and higher accuracy while being straightforward to implement and suitable to scale. New findings show that astrocytes are important parts of the information processing in brain and believed to be responsible for some brain diseases such as Alzheimer and Epilepsy. Astrocytes generate Ca$^{2+}$ waves and release neuro-transmitters over a large area. To study astrcoytes, one need to simulate large number of biologically realistic models of these cells alongside neuron models. Software simulation is flexible but slow. This thesis proposes a high-speed and low-cost digital hardware to replicate biological-plausible astrocyte and glutamate release mechanism. The nonlinear terms of these models were calculated using high-precision and cost-efficient algorithms. Subsequently, the modified models were simulated to study and validate their functions. Several hardware were developed by setting different constraints to investigate trade-offs and achieve best possible design. As proof of concept, the design was implemented on a FPGA device. Hardware implementation results confirmed the ability of the design to replicate biological cells in detail with high accuracy. As for performance, the proposed design turned out to be far more faster and area efficient than previously published works that targeted digital hardware for biological-plausible astrocytes. Spiking neurons, the models that mimic the biological cells in the brain, are described using ordinary differential equations. A common method to numerically solve these equations is Euler\u27s method. An important factor that has a significant impact on the performance and cost of the hardware implementation or software simulation of spiking neural networks and yet its importance has been neglected in the published literature, is the time step in Euler\u27s method. In this thesis, first the Izhikevich neuron\u27s accuracy as a function of the time step was measured. It was uncovered that the threshold time step that Izhikevich neuron becomes unstable is an exponential function of the input current. Software simulation performance, including total computational time and memory usage were compared for different time steps. Afterwards, the model was synthesized and implemented on the FPGA. Hardware performance metrics such as speed, area and power consumption were measured for each time step. Results indicated that time step has a negative linear effect on the performance. It was concluded that by determining maximum input current to the neuron, larger time steps comparable to those used in the previous works could be employed