ANALYSIS OF THE DIGITAL SPIKING NEURONS WITH SPIKE PHASE MAP

This paper studies the transient phenomena and steady state of the digital spiking neurons. This is switched dynamical systems thet can generate various spike-trains. The neurons can have co-existing periodic spike-trains and exhibit one of them depending on the wiring pattern and the initial value. We demonstrate several typical phenomena. Using the mapping produce, such phenomena are analyzed precisely

SYNCHRONIZATION AND TIME-SERIES APPROXIMATION IN DIGITAL SPIKING NEURAL NETWORKS

This paper considers a ring-coupled digital spiking neural network and its hardware implementation. Depending on parameters, the network can realize multi-phase synchronization of periodic spike-trains. Applying a time dependent selection switching, the network outputs a variety of periodic spike-trains. Applying a dynamic WTA switching rule, a target spike-train can be approximated automatically. The network is a digital dynamical system and is suitable for FPGA based hardware implementation. A test circuit is implemented in a FPGA board by the Verilog and typical multi-phase synchronization phenomena are confirmed experimentally

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community

Neural networks-on-chip for hybrid bio-electronic systems

PhD ThesisBy modelling the brains computation we can further our understanding of its function and develop novel treatments for neurological disorders. The brain is incredibly powerful and energy e cient, but its computation does not t well with the traditional computer architecture developed over the previous 70 years. Therefore, there is growing research focus in developing alternative computing technologies to enhance our neural modelling capability, with the expectation that the technology in itself will also bene t from increased awareness of neural computational paradigms. This thesis focuses upon developing a methodology to study the design of neural computing systems, with an emphasis on studying systems suitable for biomedical experiments. The methodology allows for the design to be optimized according to the application. For example, di erent case studies highlight how to reduce energy consumption, reduce silicon area, or to increase network throughput. High performance processing cores are presented for both Hodgkin-Huxley and Izhikevich neurons incorporating novel design features. Further, a complete energy/area model for a neural-network-on-chip is derived, which is used in two exemplar case-studies: a cortical neural circuit to benchmark typical system performance, illustrating how a 65,000 neuron network could be processed in real-time within a 100mW power budget; and a scalable highperformance processing platform for a cerebellar neural prosthesis. From these case-studies, the contribution of network granularity towards optimal neural-network-on-chip performance is explored

Analysis and applications of simple digital spiking neural networks

This paper considers the various connection system of Digital Spiking Neurons (DSN) and Digital Spiking Neural Network (DSNN) for spike-train approximation．First，we consider the DSN．Repeats integrate-and-fire between a base signal and threshold，DSN can generate various PST．Second，we present a delay connection．It is consisted of cross-firing connection and two DSNs，and generate a synchronization phenomenon．we consider uni-directional ring-type DSN and bi-directional ring-type DSN．These coupling systems are constructed by applying delayed connection to multiple DSNs，and can generate the multi-phase synchronization phenomenon．We consider the synchronization phenomenon of two coupling systems and consider the application to the walking motion of a robot．Third，we consider the DSNN．DSNN can output various PST that cannot be output by a single DSN．In order to realize approximation of spike-trains，a DSN selection system is applied to the DSNN．We define the approximation error and consider the approximation accuracy．Finally，we implement FPGA and confirm typical synchronization phenomenon and spike train approximation

EVOLUTIONARY ALGORITHM BASED SYNTHESIS OF SIMPLE DIGITAL DYNAMICAL SYSTEMS

This paper considers optimization problems in synthesis of digital spike maps based evolutionary algorithm. The digital spike map is defined on a set of points and can generate various periodic spike-trains. The MOEA/D is known as one of most efficiency algorithms to search pareto front. The MOEA/D is applied to an elementary synthesis problem that requires optimization of multi-objective functions. The MOEA/D uses simple genetic operator and mutation operator of various genetic operators in reproduction of potential solutions. The MOEA/D can find out an approximated Pareto front. The MOEA/D performance is confirmed in elementary numerical experiments

Evolutionary dynamics optimization and FPGA based implementation of digital spike map

In this thesis, we consider optimization and FPGA based implementation of digital spike maps. First, the dynamics of spike-trains is visualized by a digital spike map. The map is defined on a set of points and is represented by a characteristic vector of integers. Second, we introduce a simple evolutionary algorithm for optimization of digital spike maps. We use autocorrelation function as a cost function. Third, in order to implement the digital spike map, we introduce a digital spiking neuron. Repeating integrate-and-fire behavior between a periodic base signal and constant threshold, the neuron can out put various periodic spike-trains. The digital spike maps are implemented in an FPGA board and typical spike-trains are confirmed experimentally

ANALYSIS OF PERIODIC ORBITS IN COUPLED DIGITAL RETURN MAPS

This paper considers periodic orbits in coupled digital spike maps. The digital spike map is a digital dynamical system defined by a set of points. The digital spike map can generate a variety of periodic orbits. Digital dynamical systems are suitable for precise numerical analysis, hardware implementation using FPGA and can be applied to various systems including neural networks. We can obtain the coupled digital spike map by mutual coupling of two digital spike maps. The coupled system can exhibit a variety of periodic orbits. First, we introduce typical examples generated by coupled digital spike maps. Next, we introduce two simple feature quantities for quantitative analysis of the behavior of periodic orbits. The first quantity evaluates complexity of periodic orbits. The second quantity evaluates stability of periodic orbits. We construct feature planes using two feature quantities for analysis. We focus on maximum periodic orbits because the phenomena are complex