Memristive synapses connect brain and silicon spiking neurons.
Action Potentials
/ physiology
Animals
Electronics
/ methods
Embryo, Mammalian
Excitatory Postsynaptic Potentials
/ physiology
Hippocampus
/ cytology
Long-Term Potentiation
/ physiology
Microelectrodes
Models, Neurological
Nerve Net
/ cytology
Neural Networks, Computer
Neurons
/ cytology
Primary Cell Culture
Rats
Silicon
/ chemistry
Synapses
/ physiology
Titanium
/ chemistry
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
25 02 2020
25 02 2020
Historique:
received:
22
10
2019
accepted:
21
01
2020
entrez:
27
2
2020
pubmed:
27
2
2020
medline:
13
11
2020
Statut:
epublish
Résumé
Brain function relies on circuits of spiking neurons with synapses playing the key role of merging transmission with memory storage and processing. Electronics has made important advances to emulate neurons and synapses and brain-computer interfacing concepts that interlink brain and brain-inspired devices are beginning to materialise. We report on memristive links between brain and silicon spiking neurons that emulate transmission and plasticity properties of real synapses. A memristor paired with a metal-thin film titanium oxide microelectrode connects a silicon neuron to a neuron of the rat hippocampus. Memristive plasticity accounts for modulation of connection strength, while transmission is mediated by weighted stimuli through the thin film oxide leading to responses that resemble excitatory postsynaptic potentials. The reverse brain-to-silicon link is established through a microelectrode-memristor pair. On these bases, we demonstrate a three-neuron brain-silicon network where memristive synapses undergo long-term potentiation or depression driven by neuronal firing rates.
Identifiants
pubmed: 32098971
doi: 10.1038/s41598-020-58831-9
pii: 10.1038/s41598-020-58831-9
pmc: PMC7042282
doi:
Substances chimiques
titanium dioxide
15FIX9V2JP
Titanium
D1JT611TNE
Silicon
Z4152N8IUI
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2590Commentaires et corrections
Type : ErratumIn
Références
O’Doherty, J. E. et al. Active tactile exploration using a brain–machine–brain interface. Nature 479, 228–231 (2011).
doi: 10.1038/nature10489
Hampson, R. E. et al. Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall. J. Neural Eng. 15, 036014 (2018).
doi: 10.1088/1741-2552/aaaed7
Thakor, N. V. Translating the Brain-Machine Interface. Sci. Transl. Med. 5, 210ps17–210ps17 (2013).
doi: 10.1126/scitranslmed.3007303
Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990).
doi: 10.1109/5.58356
Vassanelli, S. & Mahmud, M. Trends and Challenges in Neuroengineering: Toward “Intelligent” Neuroprostheses through Brain-“Brain Inspired Systems” Communication. Front. Neurosci. 10 (2016).
Boi, F. et al. A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder. Front. Neurosci. 10 (2016).
Wei, S. L. et al. Emulating long-term synaptic dynamics with memristive devices. ArXiV. 1509, 01998 (2015).
Berdan, R. et al. Emulating short-term synaptic dynamics with memristive devices. Scientific reports. 6 (2015).
Burr, G. W. et al. Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element. IEEE Trans. Electron Devices 62, 3498–3507 (2015).
doi: 10.1109/TED.2015.2439635
Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).
doi: 10.1038/nnano.2012.240
Gupta, I. et al. Real-time encoding and compression of neuronal spikes by metal-oxide memristors. Nat. Commun. 7, 12805 (2016).
doi: 10.1038/ncomms12805
Birmingham, K. et al. Bioelectronic medicines: a research roadmap. Nat. Rev. Drug Discov. 13, 399–400 (2014).
doi: 10.1038/nrd4351
Schoen, I. & Fromherz, P. Extracellular Stimulation of Mammalian Neurons Through Repetitive Activation of Na+ Channels by Weak Capacitive Currents on a Silicon Chip. J. Neurophysiol. 100, 346–357 (2008).
doi: 10.1152/jn.90287.2008
George, R., Mayr, C., Indiveri, G. & Vassanelli, S. Event-based softcore processor in a biohybrid setup applied to structural plasticity. In 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP) 1–4, https://doi.org/10.1109/EBCCSP.2015.7300664 (IEEE, 2015).
Rast, A. D. et al. A location-independent direct link neuromorphic interface. In The 2013 International Joint Conference on Neural Networks (IJCNN) 1–8, https://doi.org/10.1109/IJCNN.2013.6706887 (IEEE, 2013).
Keren, H., Partzsch, J., Marom, S. & Mayr, C. G. A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks. Front. Neurosci. 13 (2019).
Dudek, S. M. & Bear, M. F. Homosynaptic long-term depression in area CA1 of hippocampus and effects of N-methyl-D-aspartate receptor blockade. Proc. Natl. Acad. Sci. USA 89, 4363–4367 (1992).
doi: 10.1073/pnas.89.10.4363
Cooper, L. N. & Bear, M. F. The BCM theory of synapse modification at 30: interaction of theory with experiment. Nat. Rev. Neurosci. 13, 798–810 (2012).
doi: 10.1038/nrn3353
Vassanelli, S., Mahmud, M., Girardi, S. & Maschietto, M. On the Way to Large-Scale and High-Resolution Brain-Chip Interfacing. Cogn. Comput. 4, 71–81 (2012).
doi: 10.1007/s12559-011-9121-4
Giacomello, M. et al. Stimulation of Ca2+ signals in neurons by electrically coupled electrolyte-oxide-semiconductor capacitors. J. Neurosci. Methods 198, 1–7 (2011).
doi: 10.1016/j.jneumeth.2011.02.009
Spira, M. E. & Hai, A. Multi-electrode array technologies for neuroscience and cardiology. Nat. Nanotechnol. 8, 83 (2013).
doi: 10.1038/nnano.2012.265
Alivisatos, A. P. et al. Nanotools for Neuroscience and Brain Activity Mapping. ACS Nano 7, 1850–1866 (2013).
doi: 10.1021/nn4012847
Angle, M. R., Cui, B. & Melosh, N. A. Nanotechnology and neurophysiology. Curr. Opin. Neurobiol. 32, 132–140 (2015).
doi: 10.1016/j.conb.2015.03.014
Duan, X. & Lieber, C. M. Nanoscience and the nano-bioelectronics frontier. Nano Res. 8, 1–22 (2015).
doi: 10.1007/s12274-014-0692-8
Brivio, S. et al. Experimental study of gradual/abrupt dynamics of HfO2-based memristive devices. Appl. Phys. Lett. 109, 133504 (2016).
doi: 10.1063/1.4963675
Serrano-Gotarredona, T., Masquelier, T., Prodromakis, T., Indiveri, G. & Linares-Barranco, B. STDP and STDP variations with memristors for spiking neuromorphic learning systems. Front. Neurosci. 7 (2013).
Serb, A. et al. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nat. Commun. 7 (2016).
Qiao, N. et al. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses. Front. Neurosci. 9, 141 (2015).
doi: 10.3389/fnins.2015.00141
Boegerhausen, M., Suter, P. & Liu, S.-C. Modeling Short-Term Synaptic Depression in Silicon. Neural Comput. 15, 331–348 (2003).
doi: 10.1162/089976603762552942
Mitra, S., Fusi, S. & Indiveri, G. Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI. IEEE Trans. Biomed. Circuits Syst. 3, 32–42 (2009).
doi: 10.1109/TBCAS.2008.2005781
Livi, P. & Indiveri, G. A current-mode conductance-based silicon neuron for address-event neuromorphic systems. In 2009 IEEE International Symposium on Circuits and Systems 2898–2901 https://doi.org/10.1109/ISCAS.2009.5118408 (IEEE, 2009).
Deiss, S., Douglas, R. & Whatley, A. A pulse-coded communications infrastructure for neuromorphic systems. Pulsed Neural Netw. 157–178 (1999).
Berdan, R. et al. A u-Controller-Based System for Interfacing Selectorless RRAM Crossbar Arrays. IEEE Trans. Electron Devices 62, 2190–2196 (2015).
doi: 10.1109/TED.2015.2433676
Antonucci, D. E., Lim, S. T., Vassanelli, S. & Trimmer, J. S. Dynamic localization and clustering of dendritic Kv2.1 voltage-dependent potassium channels in developing hippocampal neurons. Neuroscience 108, 69–81 (2001).
doi: 10.1016/S0306-4522(01)00476-6
Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 73 (2011).
pubmed: 21747754
pmcid: 3130465
Stathopoulos, S. et al. Multibit memory operation of metal-oxide bi-layer memristors. Sci. Rep. 7 (2017).