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Learning heterogeneous delays in a layer of spiking neurons for fast motion detection

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Abstract

The precise timing of spikes emitted by neurons plays a crucial role in shaping the response of efferent biological neurons. This temporal dimension of neural activity holds significant importance in understanding information processing in neurobiology, especially for the performance of neuromorphic hardware, such as event-based cameras. Nonetheless, many artificial neural models disregard this critical temporal dimension of neural activity. In this study, we present a model designed to efficiently detect temporal spiking motifs using a layer of spiking neurons equipped with heterogeneous synaptic delays. Our model capitalizes on the diverse synaptic delays present on the dendritic tree, enabling specific arrangements of temporally precise synaptic inputs to synchronize upon reaching the basal dendritic tree. We formalize this process as a time-invariant logistic regression, which can be trained using labeled data. To demonstrate its practical efficacy, we apply the model to naturalistic videos transformed into event streams, simulating the output of the biological retina or event-based cameras. To evaluate the robustness of the model in detecting visual motion, we conduct experiments by selectively pruning weights and demonstrate that the model remains efficient even under significantly reduced workloads. In conclusion, by providing a comprehensive, event-driven computational building block, the incorporation of heterogeneous delays has the potential to greatly improve the performance of future spiking neural network algorithms, particularly in the context of neuromorphic chips.

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Data Availability

Not applicable.

Code Availability

This work is made reproducible. The code reproducing the manuscript and all figures is available on https://github.com/SpikeAI/2023_GrimaldiPerrinet_HeterogeneousDelaySNNGitHub. It also contains supplementary figures and results. Find also the associated zotero group used to gather relevant literature on the subject.

References

  • Abeles M (1982) Role of the cortical neuron: integrator or coincidence detector? Isr J Med Sci 18(1):83–92

    CAS  PubMed  Google Scholar 

  • Barlow H (1989) Unsupervised Learning. Neural Comput 1(3):295–311

    Article  Google Scholar 

  • Baudot P, Levy M, Marre O, Monier C, Pananceau M, Frégnac Y (2013) Animation of natural scene by virtual eye-movements evokes high precision and low noise in V1 neurons. Front Neural Circuits 7:35

    Article  Google Scholar 

  • Benosman R (2012) Asynchronous frameless event-based optical flow. Neural Netw 27:6

    Article  Google Scholar 

  • Benvenuti G, Chemla S, Boonman A, Perrinet LU, Masson GS, Chavane F (2020) Anticipatory responses along motion trajectories in awake monkey area V1. bioRxiv : the preprint server for biology

  • Bohte SM (2004) The evidence for neural information processing with precise spike-times: a survey. Nat Comput 3(2):195–206

    Article  Google Scholar 

  • Bohte SM, Kok JN, La Poutré H (2002) Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1):17–37

    Article  Google Scholar 

  • Boutin V, Franciosini A, Chavane F, Perrinet LU (2022) Pooling strategies in V1 can account for the functional and structural diversity across species. PLoS Comput Biol 18(7):e1010270

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Boutin V, Franciosini A, Chavane FY, Ruffier F, Perrinet LU (2020) Sparse deep predictive coding captures contour integration capabilities of the early visual system. PLoS Comput Biol 5:28

    Google Scholar 

  • Boutin V, Franciosini A, Ruffier F, Perrinet LU (2020) Effect of top-down connections in Hierarchical Sparse Coding. Neural Comput 32(11):2279–2309

    Article  PubMed  Google Scholar 

  • Carr C, Konishi M (1990) A circuit for detection of interaural time differences in the brain stem of the barn owl. J Neurosci 10(10):3227–3246

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chavane F, Perrinet LU, Rankin J (2022) Revisiting horizontal connectivity rules in V1: from like-to-like towards like-to-all. Brain Struct Funct 2:568

    Google Scholar 

  • Dan Y, Atick JJ, Reid RC (1996) Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory. J Neurosci 16(10):3351–3362

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dandekar S, Privitera C, Carney T, Klein SA (2012) Neural saccadic response estimation during natural viewing. J Neurophysiol 107(6):1776–1790

    Article  PubMed  Google Scholar 

  • Dardelet L, Benosman R, Ieng S-H (2021) An event-by-event feature detection and tracking invariant to motion direction and velocity. Springer, Berlin

    Google Scholar 

  • Davis ZW, Benigno GB, Fletterman C, Desbordes T, Steward C, Sejnowski TJ, Reynolds HL, Muller L (2021) Spontaneous traveling waves naturally emerge from horizontal fiber time delays and travel through locally asynchronous-irregular states. Nat Commun 12(1):1–16

    Article  Google Scholar 

  • DeAngelis GC, Ghose GM, Ohzawa I, Freeman RD (1999) Functional micro-organization of primary visual cortex: receptive field analysis of nearby neurons. J Neurosci 19(10):4046–4064

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Delorme A, Gautrais J, van Rullen R, Thorpe S (1999) SpikeNET: a simulator for modeling large networks of integrate and fire neurons. Neurocomputing 26–27:989–996

    Article  Google Scholar 

  • DeWeese M, Zador A (2002) Binary coding in auditory cortex. Adv Neural Inform Process Syst 15:258

    Google Scholar 

  • Engbert R, Mergenthaler K, Sinn P, Pikovsky A (2011) An integrated model of fixational eye movements and microsaccades. Proc Natl Acad Sci 108(39):E765–E770

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gallego G, Delbruck T, Orchard G, Bartolozzi C, Taba B, Censi A, Leutenegger S, Davison AJ, Conradt J, Daniilidis K, Scaramuzza D (2022) Event-based vision: a survey. IEEE Trans Pattern Anal Mach Intell 44(1):154–180

    Article  PubMed  Google Scholar 

  • Ghosh R, Gupta A, Nakagawa A, Soares A, Thakor N (2019) Spatiotemporal filtering for event-based action recognition. arXiv:1903.07067 [cs]

  • Gollisch T, Meister M (2008) Rapid neural coding in the retina with relative spike latencies. Science 319(5866):1108–1111

    Article  CAS  PubMed  Google Scholar 

  • Grimaldi A, Boutin V, Ieng S-H, Benosman R, Perrinet LU (2023) A robust event-driven approach to always-on object recognition. Springer, Berlin

    Google Scholar 

  • Grimaldi A, Gruel A, Besnainou C, Jérémie J-N, Martinet J, Perrinet LU (2023) Precise spiking motifs in neurobiological and neuromorphic data. Brain Sci 13(1):68

    Article  Google Scholar 

  • Grimaldi A, Perrinet LU (2022) Learning hetero-synaptic delays for motion detection in a single layer of spiking neurons. In 2022 IEEE International Conference on Image Processing (ICIP), pp 3591–3595. ISSN: 2381-8549

  • Guise M, Knott A, Benuskova L (2014) A Bayesian model of polychronicity. Neural Comput 26(9):2052–2073

    Article  PubMed  Google Scholar 

  • Gütig R, Sompolinsky H (2006) The tempotron: a neuron that learns spike Timing-Based decisions. Nat Neurosci 9(3):420–428

    Article  PubMed  Google Scholar 

  • Haimerl C, Angulo-Garcia D, Villette V, Reichinnek S, Torcini A, Cossart R, Malvache A (2019) Internal representation of hippocampal neuronal population spans a time-distance continuum. Proc Natl Acad Sci 116(15):7477–7482

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hanuschkin A, Kunkel S, Helias M, Morrison A, Diesmann M (2010) A general and efficient method for incorporating precise spike times in globally time-driven simulations. Front Neuroinform 4:113

    Article  PubMed  PubMed Central  Google Scholar 

  • Hogendoorn H, Burkitt AN (2019) Predictive coding with neural transmission delays: a real-time temporal alignment hypothesis. eNeuro 6(2):412–182019

    Article  Google Scholar 

  • Ikegaya Y, Aaron G, Cossart R, Aronov D, Lampl I, Ferster D, Yuste R (2004) Synfire chains and cortical songs: temporal modules of cortical activity. Science 304(5670):559–564

    Article  CAS  PubMed  Google Scholar 

  • Izhikevich EM (2006) Polychronization: computation with spikes. Neural Comput 18(2):245–282

    Article  PubMed  Google Scholar 

  • Kaplan B, Lansner A, Masson GS, Perrinet LU (2013) Anisotropic connectivity implements motion-based prediction in a spiking neural network. Front Comput Neurosci 7:56

    Article  Google Scholar 

  • Khoei MA, Masson GS, Perrinet LU (2017) The flash-lag effect as a motion-based predictive shift. PLOS Comput Biol 13(1):e1005068

    Article  PubMed  PubMed Central  Google Scholar 

  • Koenderink JJ, van Doorn AJ (1987) Representation of local geometry in the visual system. Biol Cybern 55(6):367–375

    Article  CAS  PubMed  Google Scholar 

  • Kremkow J, Perrinet LU, Monier C, Alonso J-M, Aertsen A, Frégnac Y, Masson GS (2016) Push-pull receptive field organization and synaptic depression: mechanisms for reliably encoding naturalistic stimuli in V1. Front Neural Circuits 10:369

    Article  Google Scholar 

  • Lagorce X, Orchard G, Galluppi F, Shi BE, Benosman RB (2017) HOTS: a hierarchy of event-based time-surfaces for pattern recognition. IEEE Trans Pattern Anal Mach Intell 39(7):1346–1359

    Article  PubMed  Google Scholar 

  • Leon PS, Vanzetta I, Masson GS, Perrinet LU (2012) Motion Clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception. J Neurophysiol 107(11):3217–3226

    Article  PubMed  Google Scholar 

  • Leon PS, Vanzetta I, Masson GS, Perrinet LU (2012) Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception. J Neurophysiol 107(11):3217–3226

    Article  PubMed  Google Scholar 

  • Levy WB, Calvert VG (2021) Communication consumes 35 times more energy than computation in the human cortex, but both costs are needed to predict synapse number. Proc Natl Acad Sci 118(18):e2008173118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Luczak A, Barthó P, Marguet SL, Buzsáki G, Harris KD (2007) Sequential structure of neocortical spontaneous activity in vivo. Proc Natl Acad Sci 104(1):347–352

    Article  CAS  PubMed  Google Scholar 

  • Mandelbrot BB (1982) The fractal geometry of nature. W.H. Freeman, San Francisco

    Google Scholar 

  • Mansour PK, Gekas N, Mamassian P, Perrinet LU, Montagnini A, Masson GS (2018) Speed uncertainty and motion perception with naturalistic random textures. In Journal of Vision, Vol 18, pp 345, proceedings of VSS

  • Masquelier T, Guyonneau R, Thorpe SJ (2009) Competitive STDP-Based Spike Pattern Learning. Neural Comput 21(5):1259–1276

    Article  PubMed  Google Scholar 

  • Masquelier T, Thorpe SJ (2007) Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity. PLOS Comput Biol 3(2):e3100314

    Article  Google Scholar 

  • Nadafian A, Ganjtabesh M (2020) Bio-plausible unsupervised delay learning for extracting temporal features in spiking neural networks. arXiv:2011.09380 [cs, q-bio]. 00000

  • Nawrot M (2003) Eye movements provide the extra-retinal signal required for the perception of depth from motion parallax. Vision Res 43(14):1553–1562

    Article  PubMed  Google Scholar 

  • Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–609

    Article  CAS  PubMed  Google Scholar 

  • Pastalkova E, Itskov V, Amarasingham A, Buzsáki G (2008) Internally generated cell assembly sequences in the Rat Hippocampus. Science 321(5894):1322–1327

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pasturel C, Montagnini A, Perrinet LU (2020) Humans adapt their anticipatory eye movements to the volatility of visual motion properties. PLoS Comput Biol 2:45

    Google Scholar 

  • Paugam-Moisy H, Bohte SM (2012) Computing with spiking neuron networks. In Handbook of natural computing, Springer

  • Perrinet L, Samuelides M, Thorpe S (2004) Coding static natural images using spiking event times: do neurons cooperate? IEEE Trans Neural Netw 15(5):1164–1175

    Article  PubMed  Google Scholar 

  • Perrinet LU (2002) Coherence detection in a spiking neuron via Hebbian learning. Neurocomputing 5:44–46

    Google Scholar 

  • Perrinet LU (2004) Emergence of filters from natural scenes in a sparse spike coding scheme. Neurocomputing 58–60:821–826

    Article  Google Scholar 

  • Perrinet LU (2015) Sparse models for computer vision. In: Keil M, Cristóbal G, Perrinet LU (eds) Biologically inspired computer vision. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 319–346

    Chapter  Google Scholar 

  • Perrinet LU (2023) Accurate detection of spiking motifs in multi-unit raster plots. In ICANN

  • Perrinet LU, Adams RA, Friston KJ (2014) Active inference, eye movements and oculomotor delays. Biol Cybern 108(6):777–801

    Article  PubMed  PubMed Central  Google Scholar 

  • Perrinet LU, Bednar JA (2015) Edge co-occurrences can account for rapid categorization of natural versus animal images. Sci Rep 5:11400

    Article  PubMed  PubMed Central  Google Scholar 

  • Perrinet LU, Masson GS (2007) Modeling spatial integration in the ocular following response using a probabilistic framework. J Physiol 101(1–3):258

    Google Scholar 

  • Perrinet LU, Masson GS (2012) Motion-based prediction is sufficient to solve the aperture problem. Neural Comput 24(10):2726–2750

    Article  PubMed  PubMed Central  Google Scholar 

  • Poletti M, Aytekin M, Rucci M (2015) Head-eye coordination at a microscopic scale. Curr Biol 25(24):3253–3259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Priebe NJ, Lisberger SG, Movshon JA (2006) Tuning for spatiotemporal frequency and speed in directionally selective neurons of Macaque Striate Cortex. J Neurosci 26(11):2941–2950

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ravello CR, Perrinet LU, Escobar M-J, Palacios AG (2019) Speed-selectivity in retinal ganglion cells is sharpened by broad spatial frequency, naturalistic stimuli. Sci Rep 9(1):456

    Article  PubMed  PubMed Central  Google Scholar 

  • Riehle A, Grun S, Diesmann M, Aertsen A (1997) Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278(5345):1950–1953

    Article  CAS  PubMed  Google Scholar 

  • Roelfsema PR, de Lange FP (2016) Early visual cortex as a multiscale cognitive blackboard. Ann Rev Vis Sci 2:131–151

    Article  Google Scholar 

  • Rogers B, Graham M (1979) Motion parallax as an independent cue for depth perception. Perception 8(2):125–134

    Article  CAS  PubMed  Google Scholar 

  • Schrimpf M, Kubilius J, Hong H, Majaj NJ, Rajalingham R, Issa EB, Kar K, Bashivan P, Prescott-Roy J, Geiger F, Schmidt K, Yamins DLK, DiCarlo JJ (2020) Brain-score: which artificial neural network for object recognition is most brain-like? bioRxiv : the preprint server for biology. Publisher: Cold Spring Harbor Laboratory tex.elocation-id: 407007 tex.eprint: https://www.biorxiv.org/content/early/2020/01/02/407007.full.pdf

  • Sekikawa Y, Ishikawa K, Hara K, Yoshida Y, Suzuki K, Sato I, Saito H (2018) Constant velocity 3D convolution. In 2018 international conference on 3D vision (3DV), pp 343–351, Verona. IEEE

  • Simoncini C, Perrinet LU, Montagnini A, Mamassian P, Masson GS (2012) More is not always better: adaptive gain control explains dissociation between perception and action. Nat Neurosci 15(11):1596–1603

  • Vacher J, Meso AI, Perrinet LU, Peyré G (2018) Bayesian modeling of motion perception using dynamical stochastic textures. Neural Comput 5:96

    Google Scholar 

  • van Hateren JH, van der Schaaf A (1998) Independent component filters of natural images compared with simple cells in primary visual cortex. Proc R Soc B Biol Sci 265(1394):359–366

    Article  Google Scholar 

  • Villette V, Malvache A, Tressard T, Dupuy N, Cossart R (2015) Internally recurring hippocampal sequences as a population template of spatiotemporal information. Neuron 88(2):357–366

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Vinje WE, Gallant JL (2000) Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287(5456):1273–1276

    Article  CAS  PubMed  Google Scholar 

  • Yoonessi A, Baker CL Jr (2011) Contribution of motion parallax to segmentation and depth perception. J Vis 11(9):13

    Article  PubMed  Google Scholar 

  • Yu C, Gu Z, Li D, Wang G, Wang A, Li E (2022) STSC-SNN: spatio-temporal synaptic connection with temporal convolution and attention for spiking neural networks. arXiv:2210.05241 [cs, q-bio, stat]

  • Zenke F, Vogels TP (2021) The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural Comput 33(4):899–925

    Article  PubMed  Google Scholar 

  • Zhang M, Wu J, Belatreche A, Pan Z, Xie X, Chua Y, Li G, Qu H, Li H (2020) Supervised learning in spiking neural networks with synaptic delay-weight plasticity. Neurocomputing 409:103–118

    Article  Google Scholar 

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Acknowledgements

The authors thank Salvatore Giancani, Hugo Ladret, Camille Besnainou, Jean-Nicolas Jérémie, Miles Keating, and Adrien Fois for useful discussions during the elaboration of this work.

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  1. Institut de Neurosciences de la Timone, Aix Marseille Univ, CNRS, 27 boulevard Jean Moulin, 13005, Marseille, France

    Antoine Grimaldi & Laurent U. Perrinet

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  1. Antoine Grimaldi
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  2. Laurent U. Perrinet
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Both authors contributed to the conceptualization and methodology design of the study and to the project’s coordination and administration. Laurent Perrinet carried out the funding acquisition and supervision. Formal analysis and investigation were performed by both authors. Results visualization and presentation were realized by both authors. The manuscript was written by both authors. Both authors have read and approved the final manuscript.

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Correspondence to Laurent U. Perrinet.

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Communicated by Benjamin Lindner.

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Grimaldi, A., Perrinet, L.U. Learning heterogeneous delays in a layer of spiking neurons for fast motion detection. Biol Cybern 117, 373–387 (2023). https://doi.org/10.1007/s00422-023-00975-8

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