A deep learning framework for neuroscience

Nature Neuroscience, Год журнала: 2019, Номер 22(11), С. 1761 - 1770

Опубликована: Окт. 28, 2019

Язык: Английский

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Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks

IEEE Signal Processing Magazine, Год журнала: 2019, Номер 36(6), С. 51 - 63

Опубликована: Ноя. 1, 2019

Spiking neural networks (SNNs) are nature's versatile solution to fault-tolerant, energy-efficient signal processing. To translate these benefits into hardware, a growing number of neuromorphic spiking NN processors have attempted emulate biological NNs. These developments created an imminent need for methods and tools that enable such systems solve real-world processing problems. Like conventional NNs, SNNs can be trained on real, domain-specific data; however, their training requires the overcoming challenges linked binary dynamical nature. This article elucidates step-by-step problems typically encountered when guides reader through key concepts synaptic plasticity data-driven learning in setting. Accordingly, it gives overview existing approaches provides introduction surrogate gradient (SG) methods, specifically, as particularly flexible efficient method overcome aforementioned challenges.

Язык: Английский

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Backpropagation and the brain

Nature reviews. Neuroscience, Год журнала: 2020, Номер 21(6), С. 335 - 346

Опубликована: Апрель 17, 2020

Язык: Английский

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Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Advanced Materials, Год журнала: 2019, Номер 31(49)

Опубликована: Сен. 24, 2019

As the research on artificial intelligence booms, there is broad interest in brain-inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for has attracted extensive efforts, leading to a large number publications. Going forward, order better emulate brain's functions, its relevant fundamentals, working mechanisms, resultant behaviors need be re-visited, understood, connected electronics. A systematic overview biological neural systems given, along with their related critical mechanisms. Recent progress reviewed and, more importantly, existing challenges are highlighted hopefully shed light future directions.

Язык: Английский

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Synaptic Plasticity Forms and Functions

Annual Review of Neuroscience, Год журнала: 2020, Номер 43(1), С. 95 - 117

Опубликована: Фев. 20, 2020

Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long been considered an important component of learning and memory. Computational engineering work corroborate power through directed adjustment weights. Here we review fundamental elements four broadly categorized forms synaptic plasticity discuss their functional capabilities limitations. Although standard, correlation-based, Hebbian primary focus neuroscientists for decades, it is inherently limited. Three-factor rules supplement with neuromodulation eligibility traces, while true supervised types go even further by adding objectives instructive signals. Finally, a recently discovered hippocampal form combines above elements, leaving behind requirement. We suggest that effort to determine neural basis adaptive behavior could benefit from renewed experimental theoretical investigation more powerful plasticity.

Язык: Английский

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Deep Learning With Spiking Neurons: Opportunities and Challenges

Frontiers in Neuroscience, Год журнала: 2018, Номер 12

Опубликована: Окт. 25, 2018

Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals communicated processed a massively parallel fashion. SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference, event-driven processing. This makes them interesting candidates for the efficient implementation of deep networks, method choice many machine learning tasks. In this review, we address opportunities that spiking offer investigate detail challenges associated with training way competitive conventional learning, but simultaneously allows mapping to hardware. A wide range methods is presented, ranging from conversion into SNNs, constrained before conversion, variants backpropagation, biologically motivated STDP. The goal our review define categorization SNN methods, summarize their advantages drawbacks. We further discuss relationships between which becoming popular digital implementation. Neuromorphic platforms have great potential enable real-world applications. compare suitability various systems been developed over past years, use cases. approaches should not be considered simply two solutions same classes problems, instead it possible identify exploit task-specific advantages. Deep work new types event-based sensors, temporal codes local on-chip so far just scratched surface realizing these practical

Язык: Английский

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SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

Neural Computation, Год журнала: 2018, Номер 30(6), С. 1514 - 1541

Опубликована: Апрель 13, 2018

A vast majority of computation in the brain is performed by spiking neural networks. Despite ubiquity such spiking, we currently lack an understanding how biological circuits learn and compute vivo, as well can instantiate capabilities artificial silico. Here revisit problem supervised learning temporally coding multilayer First, using a surrogate gradient approach, derive SuperSpike, nonlinear voltage-based three-factor rule capable training networks deterministic integrate-and-fire neurons to perform computations on spatiotemporal spike patterns. Second, inspired recent results feedback alignment, compare performance our under different credit assignment strategies for propagating output errors hidden units. Specifically, test uniform, symmetric, random feedback, finding that simpler tasks be solved with any type while more complex require symmetric feedback. In summary, open door obtaining better scientific advancing ability train them solve problems involving transformations between time

Язык: Английский

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Theories of Error Back-Propagation in the Brain

Trends in Cognitive Sciences, Год журнала: 2019, Номер 23(3), С. 235 - 250

Опубликована: Янв. 30, 2019

The error back-propagation algorithm can be approximated in networks of neurons, which plasticity only depends on the activity presynaptic and postsynaptic neurons. These biologically plausible deep learning models include both feedforward feedback connections, allowing errors made by network to propagate through layers. rules different implemented with types spike-time-dependent plasticity. dynamics described within a common framework energy minimisation. This review article summarises recently proposed theories how neural circuits brain could approximate used artificial networks. Computational implementing these achieve as efficient networks, but they use simple synaptic based have similarities, such including information about throughout network. Furthermore, incorporate experimental evidence connectivity, responses, provide insights might organised that modification weights multiple levels cortical hierarchy leads improved performance tasks. In past few years, computer programs using (see Glossary) achieved impressive results complex cognitive tasks were previously reach humans. processing natural images language [1LeCun Y. et al.Deep learning.Nature. 2015; 521: 436-444Crossref PubMed Scopus (42113) Google Scholar], or playing arcade board games [2Mnih V. al.Human-level control reinforcement 518: 529-533Crossref (13741) Scholar, 3Silver D. al.Mastering game Go tree search.Nature. 2016; 529: 484-489Crossref (8554) Scholar]. Since recent applications extended versions classic [4Rumelhart D.E. al.Learning representations back-propagating errors.Nature. 1986; 323: 533-536Crossref (15380) their success has inspired studies comparing brain. It been demonstrated when learn perform image classification navigation, neurons layers develop similar those seen areas involved tasks, receptive fields across visual grid cells entorhinal cortex [5Banino A. al.Vector-based navigation grid-like agents.Nature. 2018; 557: 429-433Crossref (289) 6Whittington, J.C.R. al. (2018) Generalisation structural knowledge hippocampal-entorhinal system. 31st Conference Neural Information Processing Systems (NIPS 2018), MontrealGoogle 7Yamins D.L. DiCarlo J.J. Using goal-driven understand sensory cortex.Nat. Neurosci. 19: 356-365Crossref (650) suggests may analogous algorithms. thanks current computational advances, now useful functions are [8Bowers J.S. Parallel distributed theory age networks.Trends Cogn. Sci. 2017; 21: 950-961Abstract Full Text PDF (22) A key question remains open is implement describes connections should modified during learning, its attractiveness, part, comes from prescribing weight changes reduce network, according theoretical analysis. Although originally brain, weights, appears unrealistic [9Crick F. excitement networks.Nature. 1989; 337: 129-132Crossref (353) 10Grossberg S. Competitive learning: interactive activation adaptive resonance.Cogn. 1987; 11: 23-63Crossref Nevertheless, [11Bengio al.STDP-Compatible approximation backpropagation an energy-based model.Neural Comput. 29: 555-577Crossref (47) 12Guerguiev J. al.Towards segregated dendrites.eLife. 6e22901Crossref (173) 13Sacramento, Dendritic microcircuits algorithm. 14Whittington Bogacz R. An predictive coding local Hebbian plasticity.Neural 1229-1262Crossref (91) theoretic important because overrule dogma, generally accepted for 30 too complicated Before discussing this new generation detail, we first brief overview train discuss why it was considered implausible. To effectively feedback, often need appropriately adjusted hierarchical simultaneously. For example, child learns name letters, incorrect pronunciation combined result speech, associative, areas. When multi-layer makes error, assigns credit individual synapses all prescribes much. How networks? trained set examples, each consisting input pattern target pattern. pair, generates prediction then minimise difference between predicted determine appropriate modification, term computed neuron change discrepancy (Box 1). Each amount determined product projects to.Box 1Artificial NetworksA conventional consists layer receiving weighted previous (Figure IA). propagating layers, Equation 1.1, where xl vector denoting l Wl−1 matrix − 1 l. function f applied allow nonlinear computations.During cost quantifying patterns (typically defined 1.2). particular, direction steepest decrease (or gradient) ID). Such 1.3, δl+1 terms associated xl+1. last L 1.4 t activity. Thus, output positive if higher than earlier 1.5 sum above strengths (and further scaled derivative function; · denotes element-wise multiplication). hidden unit sends excitatory projections units high terms, so increasing would output. Once computed, changed 1.3 proportion neuron. computations. During procedure steps take place case naming letters mentioned above, corresponds letter. After seeing image, guess at (predicted pattern) via speech On supervision his her parent correct (target pattern), along stream more likely sound will produced again. algorithmic process enough, there problems biology. Below, briefly three issues. Conventional compute forward direction, separately external Without representation, update computations downstream biological connection strength solely signals (e.g., connect), unclear afforded Historically, major criticism; thus main focus our article. back-propagated same prediction. symmetry identical exist directions connected bidirectional significantly expected chance, not always present [15Song al.Highly nonrandom features connectivity circuits.PLoS Biol. 2005; 3: 507-519Google even present, backwards forwards still correctly align themselves. Artificial send continuous (corresponding firing rate neurons), whereas real spikes. Generalising discrete spikes trivial, derivate found Away algorithm, description inside also simplified linear summation inputs. above-mentioned issues investigated studies. lack representation addressed early proposing instead driven global signal carried neuromodulators [16Mazzoni P. al.A rule networks.Proc. Natl. Acad. U. 1991; 88: 4433-4437Crossref (138) 17Williams R.J. Simple statistical gradient-following algorithms connectionist learning.Mach. Learn. 1992; 8: 229-256Crossref 18Unnikrishnan K.P. Venugopal Alopex: correlation-based recurrent networks.Neural 1994; 6: 469-490Crossref 19Seung H.S. Learning spiking stochastic transmission.Neuron. 2003; 40: 1063-1073Abstract (238) However, slow does scale size [20Werfel curves gradient descent 17: 2699-2718Crossref More promisingly, several do represent locally closely similarly standard benchmark handwritten digit classification) [12Guerguiev 21Lillicrap T.P. al.Random support learning.Nat. Commun. 713276Crossref (336) 22Scellier B. Bengio Equilibrium propagation: bridging gap backpropagation.Front. 24Crossref (146) summarise them detail following sections. criticism demonstrating random good [21Lillicrap 23Zenke Ganguli SuperSpike: supervised multilayer 30: 1514-1541Crossref (209) 24Mostafa, H. (2017) Deep errors. arXiv preprint arXiv:1711.06756Google 25Scellier, Generalization equilibrium propagation field dynamics. 1808.04873Google 26Liao, Q. (2016) backpropagation? AAAI Intelligence, pp. 1837–1844, AAAIGoogle 27Baldi Sadowski channel, optimality backpropagation.Neural Netw. 83: 51-74Crossref (39) being said, some concern regarding issue [28Bartunov, Assessing scalability biologically-motivated architectures. With regard realism shown generalised producing [29Sporea I. Grüning Supervised 2013; 25: 473-509Crossref (97) Scholar] calculating derivatives overcome [23Zenke realistic considered, themselves small dendritic structures [30Schiess M. al.Somato-dendritic error-backpropagation active dendrites.PLoS 12e1004638Crossref (43) There diversity ideas [31Balduzzi, (2015) Kickback cuts backprop's red-tape: assignment 485–491, 32Krotov, Hopfield, Unsupervised competing units. arXiv:1806.10181Google 33Kuśmierz Ł. factors: modulating errors.Curr. Opin. Neurobiol. 46: 170-177Crossref (52) 34Marblestone A.H. al.Toward integration neuroscience.Front. 10: 94Crossref (316) 35Bengio, (2014) auto-encoders propagation. arXiv:1407.7906Google 36Lee, D.-H. Difference Joint European Machine Knowledge Discovery Databases, 498–515, SpringerGoogle Scholar]; however, principles behind related 37O'Reilly R.C. Biologically error-driven differences: generalized recirculation algorithm.Neural 1996; 895-938Crossref (211) substantial data while paralleling operate minimal control, modifications depend biology, spike time-dependent plasticity, properties pyramidal microcircuits. We emphasise rely fundamentally principles. thereby without requiring program dynamics, well divide reviewed two classes differing represented, class model encodes differences time. contrastive [37O'Reilly relies observation proportional (difference decomposed into separate updates: one other provided [38Ackley D.H. Boltzmann machines.Cogn. 1985; 9: 147-169Crossref 2). twice: anti-Hebbian once converges (after propagated connections) role 'unlearn' existing association prediction, second target.Box 2Temporal-Error ModelsTemporal-error describe nodes given node summed inputs adjacent decay level IB). As recurrent, no longer possible write equation describing (such 1.1 Box 1); instead, differential 2.1 [72Pineda F.J. networks.Phys. Rev. Lett. 59: 2229-2232Crossref (594) x˙l over time (all equations figure ignore nonlinearities brevity).In model, occurring times. easiest consider connecting modified. Substituting see 2.2 required terms. O'Reilly presence backward propagates sequence approximates version Scholar].In gradually (x3|¬t) towards values (t), sample Figure ID. temporal (x˙3) (t −x3|¬t), is, (defined 1.4). Hence, simply equal (Equation 2.3). Temporal-error brevity). o

Язык: Английский

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A review of learning in biologically plausible spiking neural networks

Neural Networks, Год журнала: 2019, Номер 122, С. 253 - 272

Опубликована: Окт. 12, 2019

Язык: Английский

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Artificial Neural Networks for Neuroscientists: A Primer

Neuron, Год журнала: 2020, Номер 107(6), С. 1048 - 1070

Опубликована: Сен. 1, 2020

Язык: Английский

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