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ical charge reaches a specific value called the threshold as a neuron integrates its inputs over time its membrane potential increases or decreases once it reaches the threshold the neuron fires and generates a signal that travels to other neurons which in turn increase or decrease their own potentials in response a neuron model that fires at the moment of threshold crossing is also called a spiking neuron model 3 while spike rates can be considered the analogue of the variable output of a traditional ann 4 neurobiology research indicated that high speed processing cannot be performed solely through a rate based scheme for example humans can perform an image recognition task requiring no more than 10 ms of processing time per neuron through the successive layers going from the retina to the temporal lobe this time window is too short for rate based encoding the precise spike timings in a small set of spiking neurons also has a higher information coding capacity compared with a rate based approach 5 the most prominent spiking neuron model is the leaky integrate and fire model 6 in that model the momentary activation level modeled as a differential equation is normally considered to be the neuron s state with incoming spikes pushing this value higher or lower until the state eventually either decays or if the firing threshold is reached the neuron fires after firing the state variable is reset to a lower value various decoding methods exist for interpreting the outgoing spike train as a real value number relying on either the frequency of spikes rate code the time to first spike after stimulation or the interval between spikes history edit this section needs more citations please help improve this section by adding citations to reliable sources unsourced material may be challenged and removed december 2018 learn how and when to remove this message see also biological neuron model pulsed neuron model artificial synapses based on ftjs many multi layer artificial neural networks are fully connected receiving input from every neuron in the previous layer and signalling every neuron in the subsequent layer although these networks have achieved breakthroughs they do not match biological networks and do not mimic neurons citation needed the biology inspired hodgkin huxley model of a spiking neuron was proposed in 1952 this model described how action potentials are initiated and propagated communication between neurons which requires the exchange of chemical neurotransmitters in the synaptic gap is described in models such as the integrate and fire model fitzhugh nagumo model 1961 1962 and hindmarsh rose model 1984 the leaky integrate and fire model or a derivative is commonly used as it is easier to compute than hodgkin huxley 7 while the notion of an artificial spiking neural network became popular only in the twenty first century 8 9 10 studies between 1980 and 1995 supported the concept the first models of this type of ann appeared to simulate non algorithmic intelligent information processing systems 11 12 13 however the notion of the spiking neural network as a mathematical model was first worked on in the early 1970s 14 as of 2019 snns lagged behind anns in accuracy but the gap is decreasing and has vanished on some tasks 15 underpinnings edit this article needs more citations please help improve this article by adding citations to reliable sources unsourced material may be challenged and removed find sources spiking neural network news newspapers books scholar jstor november 2021 learn how and when to remove this message information in the brain is represented as action potentials neuron spikes which may group into spike trains or coordinated waves a fundamental question of neuroscience is to determine whether neurons communicate by a rate or temporal code 16 temporal coding implies that a single spiking neuron can replace hundreds of hidden units on a conventional neural net 1 snns define a neuron s current state as its potential possibly modeled as a differential equation 17 an input pulse causes the potential to rise and then gradually decline encoding schemes can interpret these pulse sequences as a number considering pulse frequency and pulse interval 18 using the precise time of pulse occurrence a neural network can consider more information and offer better computing properties 19 snns compute in the continuous domain such neurons test for activation only when their potentials reach a certain value when a neuron is activated it produces a signal that is passed to connected neurons accordingly raising or lowering their potentials the snn approach produces a continuous output instead of the binary output of traditional anns pulse trains are not easily interpretable hence the need for encoding schemes however a pulse train representation may be more suited for processing spatiotemporal data or real world sensory data classification 20 snns connect neurons only to nearby neurons so that they process input blocks separately similar to cnn using filters they consider time by encoding information as pulse trains so as not to lose information this avoids the complexity of a recurrent neural network rnn impulse neurons are more powerful computational units than traditional artificial neurons 21 snns are theoretically more powerful than so called second generation networks defined as anns based on computational units that apply activation function with a continuous set of possible output values to a weighted sum or polynomial of the inputs however snn training issues and hardware requirements limit their use although unsupervised biologically inspired learning methods are available such as hebbian learning and stdp no effective supervised training method is suitable for snns that can provide better performance than second generation networks 21 spike based activation of snns is not differentiable thus gradient descent based backpropagation bp is not available snns have much larger computational costs for simulating realistic neural models than traditional anns 22 pulse coupled neural networks pcnn are often confused with snns a pcnn can be seen as a kind of snn researchers are actively working on various topics the first concerns differentiability the expressions for both the forward and backward learning methods contain the derivative of the neural activation function which is not differentiable because a neuron s output is either 1 when it spikes and 0 otherwise this all or nothing behavior disrupts gradients and makes these neurons unsuitable for gradient based optimization approaches to resolving it include resorting to entirely biologically inspired local learning rules for the hidden units translating conventionally trained rate based nns to snns smoothing the network model to be continuously differentiable defining an sg surrogate gradient as a continuous relaxation of the real gradients the second concerns the optimization algorithm standard bp can be expensive in terms of computation memory and communication and may be poorly suited to the hardware that implements it e g a computer brain or neuromorphic device 23 incorporating additional neuron dynamics such as spike frequency adaptation sfa is a notable advance enhancing efficiency and computational power 6 24 these neurons sit between biological complexity and computational complexity 25 originating from biological insights sfa offers significant computational benefits by reducing power usage 26 especially in cases of repetitive or intense stimuli this adaptation improves signal noise clarity and introduces an elementary short term memory at the neuron level which in turn improves accuracy and efficiency 27 this was mostly achieved using compartmental neuron models the simpler versions are of neuron models with adaptive thresholds are an indirect way of achieving sfa it equips snns with improved learning capabilities even with constrained synaptic plasticity and elevates computational efficiency 28 29 this feature lessens the demand on network layers by decreasing the need for spike processing thus lowering computational load and memory access time essential aspects of neural computation moreover snns utilizing neurons capable of sfa achieve levels of accuracy that rival those of conventional anns 30 31 while also requiring fewer neurons for comparable tasks this efficiency streamlines the computational workflow and conserves space and energy while maintaining technical integrity high performance deep spiking neural networks can operate with 0 3 spikes per neuron 32 applications edit this section needs more citations please help improve this section by adding citations to reliable sources unsourced material may be challenged and removed december 2018 learn how and when to remove this message snns can in principle be applied to the same applications as traditional anns 33 in addition snns can model the central nervous system of biological organisms such as an insect seeking food without prior knowledge of the environment 34 due to their relative realism they can be used to study biological neural circuits starting with a hypothesis about the topology of a biological neuronal circuit and its function recordings of this circuit can be compared to the output of a corresponding snn evaluating the plausibility of the hypothesis snns lack effective training mechanisms which can complicate some applications including computer vision when using snns for image based data the images need to be converted into binary spike trains 35 types of encodings include 36 temporal coding generating one spike per neuron in which spike latency is inversely proportional to the pixel intensity rate coding converting pixel intensity into a spike train where the number of spikes is proportional to the pixel intensity direct coding using a trainable layer to generate a floating point value for each time step the layer converts each pixel at a certain time step into a floating point value and then a threshold is used on the generated floating point values to pick either zero or one phase coding encoding temporal information into spike patterns based on a global oscillator burst coding transmitting spikes in bursts increasing communication reliability software edit this section needs more citations please help improve this section by adding citations to reliable sources unsourced material may be challenged and removed december 2018 learn how and when to remove this message a diverse range of application software can simulate snns this software can be classified according to its uses snn simulation edit unsupervised learning with ferroelectric synapses these simulate complex neural models large networks usually require lengthy processing candidates include 37 brian developed by romain brette and dan goodman at the école normale supérieure genesis the general neural simulation system 38 developed in james bower s laboratory at caltech nest developed by the nest initiative neuron mainly developed by michael hines john w moore and ted carnevale in yale university and duke university ravsim runtime tool 39 mainly developed by sanaullah in bielefeld university of applied sciences and arts snntorch an open source python library that simplifies building spiking neural networks and implementing gradient based training using pytorch 40 hardware edit efforts to implement hardware based spiking neural networks snns began in the 1980s 41 when researchers began exploring brain inspired neuromorphic systems in the following decades advancements in semiconductor technologies enabled the development of several notable projects 42 one such project is spinnaker developed at the university of manchester which utilizes millions of processing cores for large scale simulation of spiking neurons truenorth developed by ibm is one of the first commercial neuromorphic chips designed for energy efficient and parallel processing loihi an intel research chip focuses on online learning and adaptability in neuromorphic modeling in research contexts platforms such as brainscales developed in europe integrate analog and digital circuitry to accelerate neural simulations neurogrid from stanford university was designed to efficiently simulate biological neurons and synapses dynap se 43 models developed by inilabs are a family of low power event based neuromorphic chip intended for use in robotics and internet of things iot applications in addition hardware based on memristors and other emerging memory technologies is being explored for the implementation of snns with the goal of achieving lower power consumption and improved compatibility with biological neural models 42 predicting stdp learning with ferroelectric synapses neuron to neuron mesh routing model sutton and barto proposed that future neuromorphic architectures 44 will comprise billions of nanosynapses which require a clear understanding of the accompanying physical mechanisms experimental systems based on ferroelectric tunnel junctions have been used to show that stdp can be harnessed from heterogeneous polarization switching through combined scanning probe imaging electrical transport and atomic scale molecular dynamics conductance variations can be modelled by nucleation dominated domain reversal simulations showed that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way opening the path towards unsupervised learning 45 unsupervised learning with ferroelectric synapses benchmarks edit classification capabilities of spiking networks trained according to unsupervised learning methods 46 have been tested on benchmark datasets such as iris wisconsin breast cancer or statlog landsat dataset 47 48 various approaches to information encoding and network design have been used such as a 2 layer feedforward network for data clustering and classification based on hopfield 1995 the authors implemented models of local receptive fields combining the properties of radial basis functions and spiking neurons to convert input signals having a floating point representation into a spiking representation 49 50 see also edit codi cognitive architecture cognitive map cognitive computer computational neuroscience neural coding neural correlate neural decoding neuroethology neuroinformatics models of neural computation motion perception systems neuroscience references edit 1 2 maass w 1997 networks of spiking neurons the third generation of neural network models neural networks 10 9 1659 1671 doi 10 1016 s0893 6080 97 00011 7 issn 0893 6080 auge daniel hille julian mueller etienne knoll alois 2021 12 01 a survey of encoding techniques for signal processing in spiking neural networks neural processing letters 53 6 4693 4710 doi 10 1007 s11063 021 10562 2 issn 1573 773x gerstner w kistler wm 2002 spiking neuron models single neurons populations plasticity cambridge u k cambridge university press isbn 0 511 07817 x oclc 57417395 wang xi...