This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. Designing neural networks through neuroevolution nature. Abstractspiking neural networks are powerful computa. The present paper proposes the automatic design of feedforward spiking neural networks by representing several inherent aspects of the neural architecture in a proposed contextfree grammar. Evolutionary multiobjective optimization of spiking neural. Evolutionary feature selection for spiking neural network.
When a neuron is activated, it produces a signal that is passed to connected neurons. Our focus is, specifically, on models of spike based information coding, synaptic plasticity and learning. Exploring adversarial attack in spiking neural networks with spike compatible gradient. In this context, we propose a framework for neuromorphic hardware design space exploration, which allows to define a suitable architecture based on. Sep 10, 2014 an example of fully recurrent spiking artificial neural network snn designed by evolutionary algorithm ea. The use of spiking neural networks makes the common backpropagation. In this paper, we focus on spiking neural networks snns with a comprehensive study of information coding methods and hardware exploration. Evolutionary multiobjective optimization of spiking neural networks. An important step in the design of these networks is the selection of parame. Spiking neural networks snn are a rapidly emerging means of information processing, drawing inspiration from brain processes. Pdf networks of spiking neurons can perform complex nonlinear computations in fast. An evolutionary algorithm for autonomous agents with. Our focus is, specifically, on models of spikebased information coding, synaptic plasticity and learning. Spiking neural network snn plays an essential role in classification problems.
Generator of random 2d signal left was used to feed the signal into the plant. In this paper we summarize basic properties of spiking neurons and spiking networks. Evolution and learning in neural networks 809 figure 5 illustrates the tuning of these learningevolution interactions, as discussed above. In an artificial spiking memory, and function approximations neural network, incoming pulses spikes stimulates a artificial evolution of neural networks has been postsynaptic potential according to a response function, and investigated by togelius and lucas for car racing 9,10. Integrative probabilistic evolving spiking neural networks.
In this paper, the next steps of snn design are described. Evolving spiking neural networks for robot control. Evolving spiking neural networks for nonlinear control. Evolutionary feature selection for spiking neural network patternclassifiers. The best convolution networks in both spiking and nonspiking versions that have achieved over 99 % accuracy on the mnist database use at least over 300,000 adjustable synapses.
The main computational cost for neuro evolution of snns is usually the numerous network simulations that are required for evaluating the networks. International conference on artificial neural networks. Introduction artificial neural networks are inspired by natural neural systems such as brain. An example of fully recurrent spiking artificial neural network snn designed by evolutionary algorithm ea. We present a new back propagation based training algorithm for discretetime spiking neural networks snn. The proposal performs the snn design by exploring the search space of threelayered feedforward topologies with configured synaptic connections weights and. A genetic algorithm for optimizing topology and weights in neural network design, iwann93. Pdf evolutionary feature selection for spiking neural. By means of a paretobased multiobjective genetic algorithm, we are able to optimize both classi. Specically, we evolved an agent controlled by an izhikevich neural network to. This paper presents a grammatical evolution gebased methodology to automatically design third generation artificial neural networks. Abstract this paper presents an application of the biologically realistic jastap neural network model to classification tasks. Anne a new algorithm for evolution of artificial neural network classifier systems, ieee congress on.
Vrahatis112 department ofmathematics, university ofpatras, gr26500patras, greece 2university ofpatras artificial intelligence research center. Nov 08, 2019 spiking neural networks seem suitable for creating such structures since they are designed to process spiking information which creates spatiotemporal data. Evolution and analysis of embodied spiking neural networks reveals taskspecific clusters of effective networks. Spiking neural network training using evolutionary. Pdf networks of spiking neurons can perform complex nonlinear computations in fast temporal coding just as well as rate coded networks. This work presents an efficient softwarehardware design framework for developing snn systems in hardware. The paper introduces a new framework of iecos called integrative probabilistic evolving spiking neural networks ipsnn that incorporate probability learning parameters. Evolving spiking neural networks for nonlinear control problems huanneng qiu.
By the 1980s, the notion of an artificial neural network was well established, and researchers. Biological neural networks seem to capable of creating and pruning dendritic spines, leading to synapses being changed, which results in higher learning capability. Hardware implementation of a bioplausible neuron model. Structure optimization of neural networks for evolutionary design optimization article pdf available in soft computing 91. An evolutionary optimization framework for neural networks and neuromorphic architectures catherine d.
The design approach consists of an evolutionary strategy based supervised training. Fpgabased poe spiking neural networks the evolution of directly mapped recurrent spiking neural networks on fpgas has been tackled by a few researchers 231237 authorized licensed use limited to. In this paper we study the basic training problem of biological neural networks using a biologically realistic model of spiking neurons. Pdf exploring the evolution of nocbased spiking neural. Pdf neuroevolution of spiking neural networks on spinnaker. Spiking neural networks seem suitable for creating such structures since they are designed to process spiking information which creates spatiotemporal data. The latter forms the basis of the present study in which a new ionic model for reservoirlike networks, consisting of spiking neurons, is introduced. Neuroevolution of spiking neural networks on spinnaker. A spiking neural network considers temporal information. A training method for neuromorphic systems neuroscienceinspired dynamic architecture nida spiking neural network embedded in 3d space. Pdf spiking neural network training using evolutionary algorithms.
The evolutionary design process exploits parameter selfadaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Evolutionary multiobjective optimization of spiking neural networks for solving classi. Unlike traditional artificial neural networks anns, which use a high abstraction of real neurons, spiking neural networks snns offer a biologically plausible model of realistic neurons. Although there are many models of snn, evolving spiking neural network esnn is widely used in many recent research works. Bioinspired evolutionary model of spiking neural networks. His research has been focused on simulation, optimization, and machine learning in generative design and lightweighting of mechanical parts in different industries such as aerospace, automotive, medical, tooling, and sports. The purpose of the paper is to set forth the general considerations that have to be made when designing an algorithm in this area and to give an overview on how. In other words, considering the fact that there is information at the exact time a spike appears, spiking neurons send out the information via spikes instead of firing rates haykin, 1998. Fuzzy spiking neural networks publish your masters thesis.
Advances in design and application of spiking neural networks. Comparing evolutionary strategy algorithms for training. Spiking neural networks for handwritten digit recognition. Evolutionary spiking neural networks for solving supervised classification problems g. Not a digital computation, not a quantum computation, not some other flavor of computation collective computation and collective computation exclusively. Neural networks special issue on spiking neural networks. Inspired by recent deep learning algorithms on binarized neural networks, binary activation with a straightthrough gradient estimator is used to model the leaky integratefire spiking neuron, overcoming the difficulty in training snns using back propagation.
The artificial creatures are composed of randomly connected izhikevich spiking reservoir neural networks using population activity rate coding. Proceedings of intemational joint conferenceonneural networks, montreal, canada,july 31august4, 2005 spiking neural networktraining using evolutionary algorithms n. Example of spiking artificial neural network designed by. An evolutionary framework for replicating neurophysiological data.
The jastap neural network model is presented as an alternative to the basic multilayer perceptron model. Exploring adversarial attack in spiking neural networks. Evolution of bilateral symmetry in agents controlled by. Evolutionary algorithms for neural network design and. Sharpneat evolution of neural networks neat is neuroevolution of augmenting topologies. We present an fpga design framework for largescale spiking neural networks, particularly the ones with a highdensity of connections or alltoall connections. By using this evolutionary approach, several scpg design specifications can be explicitly added into the spike distancebased fitness function, such as looking for spiking neural networks snns with minimal connectivity or a central pattern generator cpg able to generate different locomotion gaits only by changing the initial input stimuli. Evolutionary design of spiking neural networks new. This paper focuses on the intersection of neural networks and evolutionary computation, namely on how evolutionary algorithms can be used to assist neural network design and training. There exist reservoir computing networks that make use of evolutionary compu. In this paper, we propose an evolutionary algorithm for neurallydriven autonomous agents, each agent is controlled by a spiking neural network, and the network receives the sensory inputs and processes the motor outputs through the encoded spike information.
Bioinspired evolutionary model of spiking neural networks in. This paper gives an introduction to spiking neural networks, some biological background, and will present two models of spiking neurons that employ pulse coding. Evolution of deep convolutional neural networks using cartesian genetic programming. Neuroevolution of spiking neural networks on spinnaker neuromorphic hardware.
Bioinspired paradigms such as spiking neural networks snns offer the potential to emulate the repairing and adaptive ability of the brain. To best exploit information from these environments, organisms must evolve the ability to anticipate consequences of new stimuli, and act on these predictions. Neural networks algorithms and applications advanced neural networks many advanced algorithms have been invented since the first simple neural network. Evolutionary indirect design of feedforward spiking neural. Evolutionary multiobjective optimization of spiking neural networks for solving classification problems is studied in this paper. Spiking neural network training using evolutionary algorithms. The problem of design is the most important part of complex systems building. One statement that follows immediately from the fact that the brain is a neural network is that every computation performed by the brain is a collective computation, not some other kind of computation. Evolutionary algorithms eas eas 16 are an abstraction of the natural evolution process.
Evolution can therefore generate neural networks able to encode external information as spatiotemporal patterns. Spiking neural network snn is considered as the third generation of artificial neural networks. Sendhoff, evolutionary multiobjective optimization of spiking neural networks, proceedings of the 17th international conference on artificial neural networks 4668, lecture notes in computer science, eds. The brain was a spiking neural network simulator whose parameters were tuned by a genetic algorithm, where fitness was assessed by the closeness to target output spike trains.
The evolution of training parameters for spiking neural. Some algorithms are based on the same assumptions or learning techniques as the slp and the mlp. Networks of spiking neurons are more powerful than their non spiking predecessors as they can encode temporal. Evolutionary design of spiking neural networks article pdf available in new mathematics and natural computation 0203. Spiking neural network snn is the third generation of neural network nn mimicking the natural behavior of the brain. Spiking neural networks international journal of neural systems. Spinnaker spiking neural network architecture is a neuromorphic hardware designed to ef. Design of spiking central pattern generators for multiple. By means of a paretobased multiobjective genetic algorithm, we are able to optimize both classification performance and connectivity of spiking neural networks with. A very different approach however was taken by kohonen, in his research in selforganising. In proceedings of genetic and evolutionary computing conference, pp.
In a later stage we aim to transfer the techniques to biological neural cultures currently available on multielectrode arrays meas 16. Evolutionary spiking neural networks esnns constitute an approach to design third generation anns also known as spiking neural networks, snns. Fuzzy spiking neural networks haider raza masters thesis engineering computer engineering publish your bachelors or masters thesis, dissertation, term paper or essay. Computing with spiking neuron networks cwi amsterdam. Evolving spiking neural networks for control of artificial. An evolutionary framework for replicating neurophysiological. An evolutionary optimization framework for neural networks.
Evolving a spiking neural network controller for low gravity. Algorithm and hardware design of discretetime spiking. Algorithm and hardware design of discretetime spiking neural. Sound localization through evolutionary learning applied to. Abstract spiking neuron networks snns are often referred to as the 3rd gener. Feedforward spiking neural networks with multiple synaptic terminals between two neu rons a the feedforward architecture. Spiking neural networks snn, izhikevich model, genetic algorithm ga, artificial creature. Human brain, for instance, consists of about 10 11 neurons with over 6. Evolution of deep convolutional neural networks using. Comparing evolutionary strategy algorithms for training spiking neural networks jos e s. Snn can handle complex temporal or spatiotemporal data, in changing environments at low power and with high effectiveness and noise tolerance. Spiking neural network training evolutionary algorithms. Evolutionary indirect design of feedforward spiking.
By processing based on binary inputoutput, snns offer lower complexity, higher density and lower power consumption. An evolutionary framework for replicating neurophysiological data with spiking neural networks emily l. Pdf evolutionary spiking neural networks as racing car. This paper presents embracefpga, a scalable, configurable network on chip nocbased snn architecture. A major inspiration for the investigation of neuroevolution is the evolution of brains in nature. This paper presents new findings in the design and application of biologically plausible neural networks based on spiking neuron models, which represent a more plausible model of real biological neurons where time is considered as an important feature for information encoding and processing in the brain. Researcharticle evolutionary spiking neural networks for solving supervised classification problems g. Deep evolutionary network structured representation denser is a novel approach to automatically design artificial neural networks anns using evolutionary computation ec. Moore speech and hearing group, department of computer science, university of sheffield abstract a biologically based learning framework is established to study neural modeling with respect to sound source localization. It is important to note that much of the discussion on ratevsspike coding in neuroscience does not apply to spiking neural networks. We use an evolutionary algorithm to evolve spiking neural controllers for simulated agents performing a visual categorization task.
Evolution and analysis of embodied spiking neural networks. The convolutional neural network cnn, one of the deep learning models, has demonstrated outstanding performance in a variety of computer vision tasks. Sound localization through evolutionary learning applied to spiking neural networks thomas m. Jan 22, 2020 complex environments provide structured yet variable sensory inputs.
The evolution of training parameters for spiking neural networks with hebbian learning katarzyna kozdon1, peter bentley1,2 1university college london, gower street, london wc1e 6bt united kingdom 2braintree ltd, 7 gower street, london wc1e 6dp, united kingdom k. We show for the rst time the articial evolution and teanalysis of embodied spiking neural networks to perform a cognitivelyinteresting behavior. He is part of the autodesk ai lab and is based in london. Pdf spiking neural network training using evolutionary. Evolutionary multiobjective optimization of spiking. An alternative approach comes from the field of neuroevolution, which harnesses evolutionary algorithms to optimize neural networks, inspired by the fact that natural brains themselves are the. Evolving spiking networks with variable resistive memories. We propose an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior and from simple proactive behavior to. Evolutionary spiking neural networks for solving supervised. A spiking neuro evolutionary system is used for this purpose. This paper presents a grammatical evolution gebased methodology to automatically design third generation artificial neural networks anns, also known as spiking neural networks snns, for solving supervised classification problems. The idea is that not all neurons are activated in every iteration of propagation as is the case in a typical multilayer perceptron network, but only when its membrane potential reaches a certain value. Index termsspiking neural networks, neuroevolution, re. Computer science neural and evolutionary computing.
758 600 743 636 617 850 415 157 691 1284 65 1277 202 853 1200 256 1326 75 693 355 188 924 521 242 1484 960 1101 882 325 921 651 986 1455 652 1016 254 1454 642 864 215 67 242 126 1347 979