Cited By
View all- Akwaboah AEtienne-Cummings R(2023)A Current-Mode Implementation of A Nearest Neighbor STDP Synapse2023 21st IEEE Interregional NEWCAS Conference (NEWCAS)10.1109/NEWCAS57931.2023.10198113(1-5)Online publication date: 26-Jun-2023
LODeNNS: A Linearly-approximated and Optimized Dendrocentric Nearest Neighbor STDP
Article No.: 3, Pages 1 - 8
Abstract
Realizing Hebbian plasticity in large-scale neuromorphic systems is essential for reconfiguring them for recognition tasks. Spike-timing-dependent plasticity, as a tool to this effect, has received a lot of attention in recent times. This phenomenon encodes weight update information as correlations between the presynaptic and postsynaptic event times, as such, it is imperative for each synapse in a silicon neural network to somehow keep its own time. We present a biologically plausible and optimized Register Transfer Level (RTL) and algorithmic approach to the Nearest-Neighbor STDP with time management handled by the postsynaptic dendrite. We adopt a time-constant based ramp approximation for ease of RTL implementation and incorporation in large-scale digital neuromorphic systems.
References
[1]
John V Arthur and Kwabena Boahen. 2005. Learning in silicon: Timing is everything. Advances in neural information processing systems 18 (2005), 75–82.
[2]
Chiara Bartolozzi and Giacomo Indiveri. 2007. Synaptic dynamics in analog VLSI. Neural computation 19, 10 (2007), 2581–2603.
[3]
Bilel Belhadj, Jean Tomas, Yannick Bornat, Adel Daouzli, Olivia Malot, and Sylvie Renaud. 2009. Digital mapping of a realistic spike timing plasticity model for real-time neural simulations. In Proceedings of the XXIV conference on design of circuits and integrated systems. IEEE, Zaragoza, Spain, 1–6.
[4]
Andrew Cassidy, Andreas G Andreou, and Julius Georgiou. 2011. A combinational digital logic approach to STDP. In 2011 IEEE international Symposium of Circuits and Systems (ISCAS). IEEE, IEEE, Rio de Janeiro, Brazil, 673–676.
[5]
Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, 2018. Loihi: A neuromorphic manycore processor with on-chip learning. Ieee Micro 38, 1 (2018), 82–99.
[6]
Robert C Froemke and Yang Dan. 2002. Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416, 6879 (2002), 433–438.
[7]
Julijana Gjorgjieva, Claudia Clopath, Juliette Audet, and Jean-Pascal Pfister. 2011. A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations. Proceedings of the National Academy of Sciences 108, 48(2011), 19383–19388.
[8]
Neil Hardingham, James Dachtler, and Kevin Fox. 2013. The role of nitric oxide in pre-synaptic plasticity and homeostasis. Frontiers in cellular neuroscience 7 (2013), 190.
[9]
Eugene M Izhikevich and Niraj S Desai. 2003. Relating stdp to bcm. Neural computation 15, 7 (2003), 1511–1523.
[10]
Corey Lammie, Tara Julia Hamilton, André van Schaik, and Mostafa Rahimi Azghadi. 2018. Efficient FPGA implementations of pair and triplet-based STDP for neuromorphic architectures. IEEE Transactions on Circuits and Systems I: Regular Papers 66, 4(2018), 1558–1570.
[11]
Jean-Pascal Pfister and Wulfram Gerstner. 2006. Triplets of spikes in a model of spike timing-dependent plasticity. Journal of Neuroscience 26, 38 (2006), 9673–9682.
[12]
MIT Press. 2021. The future of AI Hardware: A 3D silicon brain. MIT. Retrieved March 08, 2022 from https://web.mit.edu/deblina-sarkar/talks_kwabena.html
[13]
Harel Z Shouval, Samuel S-H Wang, and Gayle M Wittenberg. 2010. Spike timing dependent plasticity: a consequence of more fundamental learning rules. Frontiers in computational neuroscience 4 (2010), 19.
[14]
Peter Sterling and Simon Laughlin. 2015. Principles of neural design. MIT press, Cambridge, Massachusetts. 170–174 pages.
[15]
Jun Wang, Gert Cauwenberghs, and Frédéric D Broccard. 2019. Neuromorphic dynamical synapses with reconfigurable voltage-gated kinetics. IEEE Transactions on Biomedical Engineering 67, 7 (2019), 1831–1840.
Recommendations
GABA inhibition modulates NMDA-R mediated spike timing dependent plasticity (STDP) in a biophysical model
Spike timing dependent plasticity (STDP) has been demonstrated in various neural systems of many animals. It has been shown that STDP depends on the target and the location of the synapse and is dynamically regulated by the activity of adjacent synapses,...
A calcium-influx-dependent plasticity model exhibiting multiple STDP curves
AbstractHebbian plasticity means that if the firing of two neurons is correlated, then their connection is strengthened. Conversely, uncorrelated firing causes a decrease in synaptic strength. Spike-timing-dependent plasticity (STDP) represents one ...
How Bursts Shape the STDP Curve in the Presence/Absence of GABAergic Inhibition
ICANN '09: Proceedings of the 19th International Conference on Artificial Neural Networks: Part IIt has been known for some time that the synapses of the CA1 pyramidal cells are surprisingly unreliable at signalling the arrival of single spikes to the postsynaptic neuron [2]. On the other hand, bursts of spikes are reliably signalled, because ...
Comments
Information & Contributors
Information
Published In
July 2022
213 pages
ISBN:9781450397896
DOI:10.1145/3546790
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 September 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
Conference
ICONS
Acceptance Rates
Overall Acceptance Rate 13 of 22 submissions, 59%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 269Total Downloads
- Downloads (Last 12 months)114
- Downloads (Last 6 weeks)16
Reflects downloads up to 26 Dec 2024
Other Metrics
Citations
Cited By
View all- Akwaboah AEtienne-Cummings R(2023)A Current-Mode Implementation of A Nearest Neighbor STDP Synapse2023 21st IEEE Interregional NEWCAS Conference (NEWCAS)10.1109/NEWCAS57931.2023.10198113(1-5)Online publication date: 26-Jun-2023
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatLogin options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in