research-article

Open access

Spatiotemporal Pattern Recognition in Single Mixed-Signal VLSI Neurons with Heterogeneous Dynamic Synapses

Authors:

Mattias Nilsson,

Foteini Liwicki,

Fredrik SandinAuthors Info & Claims

ICONS '22: Proceedings of the International Conference on Neuromorphic Systems 2022

Article No.: 4, Pages 1 - 8

https://doi.org/10.1145/3546790.3546794

Published: 07 September 2022 Publication History

All formats PDF

Abstract

Mixed-signal neuromorphic processors with brain-like organization and device physics offer an ultra-low-power alternative to the unsustainable developments of conventional deep learning and computing. However, realizing the potential of such neuromorphic hardware requires efficient use of its heterogeneous, analog neurosynaptic circuitry with neurocomputational methods for sparse, spike-timing-based encoding and processing. Here, we investigate the use of balanced excitatory–inhibitory disynaptic lateral connections as a resource-efficient mechanism for implementing a thalamocortically inspired Spatiotemporal Correlator (STC) neural network without using dedicated delay mechanisms. We present hardware-in-the-loop experiments with a DYNAP-SE neuromorphic processor, in which receptive fields of heterogeneous coincidence-detection neurons in an STC network with four lateral afferent connections per column were mapped by random input-sampling. Furthermore, we demonstrate how such a neuron was tuned to detect a particular spatiotemporal feature by discrete address-reprogramming of the analog synaptic circuits. The energy dissipation of the disynaptic connections is one order of magnitude lower per lateral connection (0.65 nJ vs 9.6 nJ per spike) than in the former delay-based hardware implementation of the STC.

References

[1]

H. Agmon-Snir and I. Segev. 1993. Signal delay and input synchronization in passive dendritic structures. Journal of Neurophysiology 70, 5 (1993), 2066–2085. https://doi.org/10.1152/jn.1993.70.5.2066 8294970.

[2]

Chiara Bartolozzi and Giacomo Indiveri. 2007. Synaptic Dynamics in Analog VLSI. Neural Computation 19, 10 (2007), 2581–2603. https://doi.org/10.1162/neco.2007.19.10.2581 17716003.

Digital Library

[3]

F. C. Bauer, D. R. Muir, and G. Indiveri. 2019. Real-Time Ultra-Low Power ECG Anomaly Detection Using an Event-Driven Neuromorphic Processor. IEEE Transactions on Biomedical Circuits and Systems 13, 6 (2019), 1575–1582.

[4]

Ben Varkey Benjamin, Nicholas A Steinmetz, Nick N Oza, Jose J Aguayo, and Kwabena Boahen. 2021. Neurogrid simulates cortical cell-types, active dendrites, and top-down attention. Neuromorphic Computing and Engineering 1, 1 (jul 2021), 013001. https://doi.org/10.1088/2634-4386/ac0a5a

[5]

Romain Brette and Wulfram Gerstner. 2005. Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity. Journal of Neurophysiology 94, 5 (2005), 3637–3642. https://doi.org/10.1152/jn.00686.2005 16014787.

[6]

Julian Büchel, Fynn Firouz Faber, and Dylan Richard Muir. 2022. Network Insensitivity to Parameter Noise via Parameter Attack During Training. In International Conference on Learning Representations.

[7]

Julian Büchel, Dmitrii Zendrikov, Sergio Solinas, Giacomo Indiveri, and Dylan R. Muir. 2021. Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors. Scientific Reports 11, 1 (2021), 1–12. https://doi.org/10.1038/s41598-021-02779-x

[8]

E. Chicca, F. Stefanini, C. Bartolozzi, and G. Indiveri. 2014. Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems. Proc. IEEE 102, 9 (2014), 1367–1388.

[9]

Dennis V Christensen, Regina Dittmann, Bernabe Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, 2022. 2022 roadmap on neuromorphic computing and engineering. Neuromorphic Computing and Engineering 2, 2 (may 2022), 022501. https://doi.org/10.1088/2634-4386/ac4a83

[10]

Martin Coath, Robert Mill, Susan L. Denham, and Thomas Wennekers. 2011. Emergent Feature Sensitivity in a Model of the Auditory Thalamocortical System. In From Brains to Systems, Carlos Hernández, Ricardo Sanz, Jaime Gómez-Ramirez, Leslie S. Smith, Amir Hussain, Antonio Chella, and Igor Aleksander (Eds.). Springer New York, New York, NY, 7–17.

[11]

Martin Coath, Sadique Sheik, Elisabetta Chicca, Giacomo Indiveri, Susan Denham, and Thomas Wennekers. 2014. A robust sound perception model suitable for neuromorphic implementation. Frontiers in Neuroscience 7 (2014), 278. https://doi.org/10.3389/fnins.2013.00278

[12]

Erika Covi, Elisa Donati, Xiangpeng Liang, David Kappel, Hadi Heidari, Melika Payvand, and Wei Wang. 2021. Adaptive Extreme Edge Computing for Wearable Devices. Frontiers in Neuroscience 15 (2021), 429. https://doi.org/10.3389/fnins.2021.611300

[13]

Thomas Dalgaty, Niccolo Castellani, Clément Turck, Kamel-Eddine Harabi, Damien Querlioz, and Elisa Vianello. 2021. In situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling. Nature Electronics (2021), 1–11.

[14]

Anup Das, Paruthi Pradhapan, Willemijn Groenendaal, Prathyusha Adiraju, Raj Thilak Rajan, Francky Catthoor, Siebren Schaafsma, Jeffrey L. Krichmar, Nikil Dutt, and Chris Van Hoof. 2018. Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout. Neural Networks 99(2018), 134 – 147. https://doi.org/10.1016/j.neunet.2017.12.015

Digital Library

[15]

Simon Davidson and Steve B. Furber. 2021. Comparison of Artificial and Spiking Neural Networks on Digital Hardware. Frontiers in Neuroscience 15 (2021), 345. https://doi.org/10.3389/fnins.2021.651141

[16]

Mike Davies, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya, Gabriel A Fonseca Guerra, Prasad Joshi, Philipp Plank, and Sumedh R Risbud. 2021. Advancing neuromorphic computing with Loihi: A survey of results and outlook. Proc. IEEE 109, 5 (2021), 911–934.

[17]

Elisa Donati, Melika Payvand, Nicoletta Risi, Renate Krause, Karla Burelo, Giacomo Indiveri, Thomas Dalgaty, and Elisa Vianello. 2018. Processing EMG signals using reservoir computing on an event-based neuromorphic system. In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS). 1–4. https://doi.org/10.1109/BIOCAS.2018.8584674

[18]

Charlotte Frenkel, David Bol, and Giacomo Indiveri. 2021. Bottom-Up and Top-Down Neural Processing Systems Design: Neuromorphic Intelligence as the Convergence of Natural and Artificial Intelligence. https://doi.org/10.48550/ARXIV.2106.01288

[19]

J Göltz, L Kriener, A Baumbach, S Billaudelle, O Breitwieser, B Cramer, D Dold, AF Kungl, W Senn, J Schemmel, 2021. Fast and energy-efficient neuromorphic deep learning with first-spike times. Nature Machine Intelligence 3, 9 (2021), 823–835.

[20]

Wenzhe Guo, Mohammed E. Fouda, Ahmed M. Eltawil, and Khaled Nabil Salama. 2021. Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems. Frontiers in Neuroscience 15 (2021), 212. https://doi.org/10.3389/fnins.2021.638474

[21]

Germain Haessig, Moritz B. Milde, Pau Vilimelis Aceituno, Omar Oubari, James C. Knight, André van Schaik, Ryad B. Benosman, and Giacomo Indiveri. 2020. Event-Based Computation for Touch Localization Based on Precise Spike Timing. Frontiers in Neuroscience 14 (2020), 420. https://doi.org/10.3389/fnins.2020.00420

[22]

Giacomo Indiveri. 2021. Introducing ‘Neuromorphic Computing and Engineering’. Neuromorphic Computing and Engineering 1, 1 (jul 2021), 010401. https://doi.org/10.1088/2634-4386/ac0a5b

[23]

G. Indiveri and Y. Sandamirskaya. 2019. The Importance of Space and Time for Signal Processing in Neuromorphic Agents: The Challenge of Developing Low-Power, Autonomous Agents That Interact With the Environment. IEEE Signal Processing Magazine 36, 6 (2019), 16–28.

[24]

Jakob Kaiser, Sebastian Billaudelle, Eric Müller, Christian Tetzlaff, Johannes Schemmel, and Sebastian Schmitt. 2021. Emulating Dendritic Computing Paradigms on Analog Neuromorphic Hardware. Neuroscience (2021). https://doi.org/10.1016/j.neuroscience.2021.08.013

[25]

Eric Kauderer-Abrams, Andrew Gilbert, Aaron Voelker, Ben Benjamin, Terrence C Stewart, and Kwabena Boahen. 2017. A population-level approach to temperature robustness in neuromorphic systems. In 2017 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 1–4.

[26]

Juan A Leñero-Bardallo, Teresa Serrano-Gotarredona, and Bernabé Linares-Barranco. 2008. A calibration technique for very low current and compact tunable neuromorphic cells: Application to 5-bit 20-nA DACs. IEEE Transactions on Circuits and Systems II: Express Briefs 55, 6(2008), 522–526.

[27]

Wolfgang Maass. 1997. Networks of spiking neurons: the third generation of neural network models. Neural networks 10, 9 (1997), 1659–1671.

[28]

Carver Mead. 2020. How we created Neuromorphic Engineering. Nature Electronics 3, 7 (2020), 434–435.

[29]

A. Mehonic and A. J. Kenyon. 2022. Brain-inspired computing needs a master plan. Nature 604, 7905 (2022), 255–260. https://doi.org/10.1038/s41586-021-04362-w

[30]

M. B. Milde, O. J. N. Bertrand, H. Ramachandran, M. Egelhaaf, and E. Chicca. 2018. Spiking Elementary Motion Detector in Neuromorphic Systems. Neural Computation 30, 9 (2018), 2384–2417. https://doi.org/10.1162/neco_a_01112 30021082.

Digital Library

[31]

S. Moradi, N. Qiao, F. Stefanini, and G. Indiveri. 2018. A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs). IEEE Transactions on Biomedical Circuits and Systems 12, 1 (2018), 106–122.

[32]

Emre Neftci and Giacomo Indiveri. 2010. A device mismatch compensation method for VLSI neural networks. In 2010 Biomedical Circuits and Systems Conference (BioCAS). IEEE, 262–265.

[33]

C. Nielsen, N. Qiao, and G. Indiveri. 2017. A compact ultra low-power pulse delay and extension circuit for neuromorphic processors. In 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS). 1–4. https://doi.org/10.1109/BIOCAS.2017.8325234

[34]

Mattias Nilsson, Foteini Liwicki, and Fredrik Sandin. 2020. Synaptic Integration of Spatiotemporal Features with a Dynamic Neuromorphic Processor. In 2020 International Joint Conference on Neural Networks (IJCNN). 1–7. https://doi.org/10.1109/IJCNN48605.2020.9207210

[35]

Marcel JM Pelgrom, Aad CJ Duinmaijer, and Anton PG Welbers. 1989. Matching properties of MOS transistors. IEEE Journal of solid-state circuits 24, 5 (1989), 1433–1439.

[36]

Nicolas Perez-Nieves, Vincent CH Leung, Pier Luigi Dragotti, and Dan FM Goodman. 2021. Neural heterogeneity promotes robust learning. Nature communications 12, 1 (2021), 1–9.

[37]

Ning Qiao and Giacomo Indiveri. 2016. Scaling mixed-signal neuromorphic processors to 28 nm FD-SOI technologies. In 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 552–555.

[38]

B. Rajendran, A. Sebastian, M. Schmuker, N. Srinivasa, and E. Eleftheriou. 2019. Low-Power Neuromorphic Hardware for Signal Processing Applications: A Review of Architectural and System-Level Design Approaches. IEEE Signal Processing Magazine 36, 6 (2019), 97–110.

[39]

Ole Richter, René Felix Reinhart, Stephen Nease, Jochen Steil, and Elisabetta Chicca. 2015. Device mismatch in a neuromorphic system implements random features for regression. In 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS). 1–4. https://doi.org/10.1109/BioCAS.2015.7348416

[40]

Fredrik Sandin and Mattias Nilsson. 2020. Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors. Frontiers in Neuroscience 14 (2020), 150. https://doi.org/10.3389/fnins.2020.00150

[41]

Stefan Schöneich, Konstantinos Kostarakos, and Berthold Hedwig. 2015. An auditory feature detection circuit for sound pattern recognition. Science Advances 1, 8 (2015), e1500325. https://doi.org/10.1126/sciadv.1500325

[42]

Sadique Sheik, Elisabetta Chicca, and Giacomo Indiveri. 2012. Exploiting device mismatch in neuromorphic VLSI systems to implement axonal delays. In The 2012 International Joint Conference on Neural Networks (IJCNN). 1–6. https://doi.org/10.1109/IJCNN.2012.6252636

[43]

Sadique Sheik, Martin Coath, Giacomo Indiveri, Susan Denham, Thomas Wennekers, and Elisabetta Chicca. 2012. Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System. Frontiers in Neuroscience 6 (2012), 17. https://doi.org/10.3389/fnins.2012.00017

[44]

Sadique Sheik, Michael Pfeiffer, Fabio Stefanini, and Giacomo Indiveri. 2013. Spatio-temporal Spike Pattern Classification in Neuromorphic Systems. In Biomimetic and Biohybrid Systems, Nathan F. Lepora, Anna Mura, Holger G. Krapp, Paul F. M. J. Verschure, and Tony J. Prescott (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 262–273.

[45]

Sadique ul Ameen Sheik. 2013. Autonomous learning in neuromorphic systems for recognition of spatio-temporal spike patterns. Ph.D. Dissertation. ETH Zurich, Zürich. https://doi.org/10.3929/ethz-a-010118718 Diss., Eidgenössische Technische Hochschule ETH Zürich, Nr. 21425, 2013.

[46]

Shihao Song, Jui Hanamshet, Adarsha Balaji, Anup Das, Jeffrey L. Krichmar, Nikil D. Dutt, Nagarajan Kandasamy, and Francky Catthoor. 2021. Dynamic Reliability Management in Neuromorphic Computing. arxiv:2105.02038 [cs.NE]

[47]

Christoph Stöckl and Wolfgang Maass. 2021. Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes. Nature Machine Intelligence 3, 3 (2021), 230–238.

[48]

SynSense. 2021. Samna. SynSense AG. https://synsense-sys-int.gitlab.io/samna/

[49]

Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F. Manso. 2021. Deep Learning’s Diminishing Returns: The Cost of Improvement is Becoming Unsustainable. IEEE Spectrum 58, 10 (2021), 50–55. https://doi.org/10.1109/MSPEC.2021.9563954

[50]

Simon Thorpe, Arnaud Delorme, and Rufin Van Rullen. 2001. Spike-based strategies for rapid processing. Neural networks 14, 6-7 (2001), 715–725.

[51]

Hans Tuinhout, Nicole Wils, and Pietro Andricciola. 2010. Parametric mismatch characterization for mixed-signal technologies. IEEE journal of solid-state circuits 45, 9 (2010), 1687–1696.

[52]

Yingxue Wang and Shih-Chii Liu. 2012. Active processing of spatio-temporal input patterns in silicon dendrites. IEEE transactions on biomedical circuits and systems 7, 3 (2012), 307–318.

[53]

Le Ye, Zhixuan Wang, Ying Liu, Peiyu Chen, Heyi Li, Hao Zhang, Meng Wu, Wei He, Linxiao Shen, Yihan Zhang, 2021. The Challenges and Emerging Technologies for Low-Power Artificial Intelligence IoT Systems. IEEE Transactions on Circuits and Systems I: Regular Papers 68, 12(2021), 4821–4834. https://doi.org/10.1109/TCSI.2021.3095622

[54]

Fleur Zeldenrust, Boris Gutkin, and Sophie Denéve. 2021. Efficient and robust coding in heterogeneous recurrent networks. PLoS computational biology 17, 4 (2021), e1008673.

[55]

Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang. 2019. Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107, 8 (2019), 1738–1762.

Cited By

Nilsson MPina TKhacef LLiwicki FChicca ESandin F(2023)A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191938(1-7)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191938

Index Terms

Spatiotemporal Pattern Recognition in Single Mixed-Signal VLSI Neurons with Heterogeneous Dynamic Synapses
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Hardware
  1. Emerging technologies
    1. Biology-related information processing
      1. Neural systems

Recommendations

A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity

We present a mixed-mode analog/digital VLSI device comprising an array of leaky integrate-and-fire (I&F) neurons, adaptive synapses with spike-timing dependent plasticity, and an asynchronous event based communication infrastructure that allows the user ...
Input-Driven Oscillations in Networks with Excitatory and Inhibitory Neurons with Dynamic Synapses

Previous work has shown that networks of neurons with two coupled layers of excitatory and inhibitory neurons can reveal oscillatory activity. For example, Börgers and Kopell (2003) have shown that oscillations occur when the excitatory neurons receive ...
Self-organization of Hebbian synapses in Hippocampal neurons
NIPS'90: Proceedings of the 3rd International Conference on Neural Information Processing Systems

We are exploring the significance of biological complexity for neuronal computation. Here we demonstrate that Hebbian synapses in realistically-modeled hippocampal pyramidal cells may give rise to two novel forms of self-organization in response to ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICONS '22: Proceedings of the International Conference on Neuromorphic Systems 2022

July 2022

213 pages

ISBN:9781450397896

DOI:10.1145/3546790

Editors:
Thomas E. Potok
Oak Ridge National Laboratory
,
Catherine Schuman
University of Tennessee Knoxville
,
Melika Payvand
University of Zurich
,
Prasanna Date
Oak Ridge National Laboratory
,
Shruti Kulkarni
Oak Ridge National Laboratory
,
Yiran Chen
Duke University
,
Robinson Pino
U.S. Department of Energy
,
Brad Aimone
Sandia National Laboratories
,
Mutsumi Kimura
Ryukoku University
,
Gregory Cohen
Western Sydney University
,
David Whittaker
Elm Street Ventures
,
Gordon Hirsch Wilson
Rain Neuromorphics

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2022

Check for updates

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Conference

ICONS

ICONS: International Conference on Neuromorphic Systems

July 27 - 29, 2022

TN, Knoxville, USA

Acceptance Rates

Overall Acceptance Rate 13 of 22 submissions, 59%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
229
Total Downloads

Downloads (Last 12 months)83
Downloads (Last 6 weeks)13

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Nilsson MPina TKhacef LLiwicki FChicca ESandin F(2023)A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191938(1-7)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191938

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents