Skip to main content

Neuromorphic Cognition

  • Living reference work entry
  • First Online:

Synonyms

Neuromorphic cognitive systems; Neuromorphic electronic systems; Neuromorphic real-time behaving systems

Definition

The hallmark of cognitive behavior is the ability of an agent to select an economically advantageous action based on immediate external stimuli as well as on their longer-term context. Neuromorphic cognition refers to the cognitive abilities of systems or agents implemented in neuromorphic electronic VLSI technology whose processing architecture is similar to the distributed, asynchronous one of biological brains. Neuromorphic agents are typically real-time behaving systems composed of multiple asynchronous event-based VLSI chips that integrate networks of silicon neurons and dynamic synapses together and that are interfaced to event-based neuromorphic sensors and robotic actuators. In order to express cognitive performance, these agents require a hardware infrastructure that supports local learning and decision making, for distributed communication and for the...

This is a preview of subscription content, log in via an institution.

References

  • Abbott L, Nelson S (2000) Synaptic plasticity: taming the beast. Nat Neurosci 3:1178–1183

    Article  CAS  PubMed  Google Scholar 

  • Abrahamsen J, Hafliger P, Lande T (2004) A time domain winner-take-all network of integrate-and-fire neurons. In: International symposium on circuits and systems, (ISCAS), 2004, IEEE, vol 5, pp V-361–V-364

    Google Scholar 

  • Amari S, Arbib M (1977) Competition and cooperation in neural nets. In: Metzler J (ed) Systems neuroscience. Academic, New York, pp 119–165

    Google Scholar 

  • Arthur J, Boahen K (2006) Learning in silicon: timing is everything. In: Weiss Y, Schölkopf B, Platt J (eds) Advances in neural information processing systems 18. MIT Press, Cambridge, MA

    Google Scholar 

  • Bartolozzi C, Indiveri G (2007) Synaptic dynamics in analog VLSI. Neural computation 19(10):2581–2603. doi 10.1162/neco.2007.19.10.2581. http://ncs.ethz.ch/pubs/pdf/Bartolozzi_Indiveri07b.pdf

  • Bartolozzi C, Mitra S, Indiveri G (2006) An ultra low power current–mode filter for neuromorphic systems and biomedical signal processing. In: Biomedical circuits and systems conference, (BioCAS), 2006, IEEE, pp 130–133. doi 10.1109/BIOCAS.2006.4600325. http://ncs.ethz.ch/pubs/pdf/Bartolozzi_etal06.pdf

  • Bennett A (1990) Large competitive networks. Network 1:449–462

    Article  Google Scholar 

  • Ben-Yishai R, Lev Bar-Or R, Sompolinsky H (1995) Theory of orientation tuning in visual cortex. Proc Natl Acad Sci U S A 92(9):3844–3848

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Binzegger T, Douglas R, Martin K (2004) A quantitative map of the circuit of cat primary visual cortex. J Neurosci 24(39):8441–8853

    Article  CAS  PubMed  Google Scholar 

  • Bofill-i Petit A, Murray A (2004) Synchrony detection and amplification by silicon neurons with STDP synapses. IEEE Trans Neural Netw 15(5):1296–1304

    Article  PubMed  Google Scholar 

  • Brader J, Senn W, Fusi S (2007) Learning real world stimuli in a neural network with spike-driven synaptic dynamics. Neural Comput 19:2881–2912

    Article  PubMed  Google Scholar 

  • Brüderle D, Petrovici M, Vogginger B, Ehrlich M, Pfeil T, Millner S, Grübl A, Wendt K, Müller E, Schwartz MO, de Oliveira D, Jeltsch S, Fieres J, Schilling M, Müller P, Breitwieser O, Petkov V, Muller L, Davison A, Krishnamurthy P, Kremkow J, Lundqvist M, Muller E, Partzsch J, Scholze S, Zühl L, Mayr C, Destexhe A, Diesmann M, Potjans T, Lansner A, Schüffny R, Schemmel J, Meier K (2011) A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol Cybern 104(4):263–296

    Article  PubMed  Google Scholar 

  • Chicca E (2006) A neuromorphic VLSI system for modeling spike–based cooperative competitive neural networks. PhD thesis, ETH Zürich, Zürich. doi 10.3929/ethz-a-005275753

    Google Scholar 

  • Chicca E, Indiveri G, Douglas R (2004) An event based VLSI network of integrate-and-fire neurons. In: International symposium on circuits and systems, (ISCAS), 2004, IEEE, pp V-357–V-360. doi 10.1109/ISCAS.2004.1329536. http://ncs.ethz.ch/pubs/pdf/Chicca_etal04.pdf

  • Chicca E, Indiveri G, Douglas R (2007) Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons. In: Schölkopf B, Platt J, Hofmann T (eds) Advances in neural information processing systems 19, Neural Information Processing Systems Foundation, MIT Press, Cambridge, MA, pp 257–264. http://ncs.ethz.ch/pubs/pdf/Chicca_etal07.pdf

  • Chicca E, Whatley A, Lichtsteiner P, Dante V, Delbruck T, Del Giudice P, Douglas R, Indiveri G (2007) A multi-chip pulse-based neuromorphic infrastructure and its application to a model of orientation selectivity. IEEE Trans Circuits Syst I 5(54):981–993.doi 10.1109/TCSI.2007.893509. http://ncs.ethz.ch/pubs/pdf/Chicca_etal07b.pdf

  • Chicca E, Stefanini F, Indiveri G (2013) Neuromorphic electronic circuits for building autonomous cognitive systems. In: Proceedings of IEEE (Submitted, under review), Los Alamitos, CA

    Google Scholar 

  • Dayan P, Abbott L (2001) Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT Press, Cambridge, MA

    Google Scholar 

  • Destexhe A, Mainen Z, Sejnowski T (1998) Kinetic models of synaptic transmission. In: Koch C, Segev I (eds) Methods in neuronal modelling, from ions to networks. MIT Press, Cambridge, MA, pp 1–25

    Google Scholar 

  • DeYong M, Findley R, Fields C (1992) The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element. IEEE Trans Neural Netw 3(3):363–374

    Article  CAS  PubMed  Google Scholar 

  • Douglas R, Martin K (2004) Neural circuits of the neocortex. Annu Rev Neurosci 27:419–451

    Article  CAS  PubMed  Google Scholar 

  • Douglas R, Martin K (2007) Recurrent neuronal circuits in the neocortex. Curr Biol 17(13):R496–R500

    Article  CAS  PubMed  Google Scholar 

  • Douglas R, Koch C, Mahowald M, Martin K, Suarez H (1995) Recurrent excitation in neocortical circuits. Science 269:981–985

    Article  CAS  PubMed  Google Scholar 

  • Drakakis E, Payne A, Toumazou C (1999) “Log-domain state-space”: a systematic transistor-level approach for log-domain filtering. IEEE Trans Circuits Syst II 46(3):290–305

    Article  Google Scholar 

  • Eliasmith C, Stewart T, Choo X, Bekolay T, DeWolf T, Tang Y, Rasmussen D (2012) A large-scale model of the functioning brain. Science 338(6111):1202–1205. doi 10.1126/science.1225266. http://www.sciencemag.org/content/338/6111/1202.abstract, http://www.sciencemag.org/content/338/6111/1202.full.pdf

  • Frey D (1993) Log-domain filtering: an approach to current-mode filtering. IEEE Proc G Circuits Devices Syst 140(6):406–416

    Article  Google Scholar 

  • Fusi S (2002) Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates. Biol Cybern 87:459–470

    Article  PubMed  Google Scholar 

  • Fusi S, Annunziato M, Badoni D, Salamon A, Amit D (2000) Spike–driven synaptic plasticity: theory, simulation, VLSI implementation. Neural Comput 12:2227–2259

    Article  CAS  PubMed  Google Scholar 

  • Giulioni M, Pannunzi M, Badoni D, Dante V, Del Giudice P (2009) Classification of overlapping patterns with a configurable analog VLSI neural network of spiking neurons and self-regulating plastic synapses. Neural Comput 21(11):3106–3129. doi:10.1162/neco.2009.08-07-599

    Article  PubMed  Google Scholar 

  • Giulioni M, Camilleri P, Mattia M, Dante V, Braun J, Giudice PD (2011) Robust working memory in an asynchronously spiking neural network realized in neuromorphic VLSI. Front Neurosci 5. doi 10.3389/fnins.2011.00149, http://www.frontiersin.org/Journal/Abstract.aspx?s=755&name=neuromorphic_engineering&ART_DOI=10.3389/fnins.2011.00149

  • Gütig R, Sompolinsky H (2006) The tempotron: a neuron that learns spike timing–based decisions. Nat Neurosci 9:420–428. doi:10.1038/nn1643 6

    Article  PubMed  CAS  Google Scholar 

  • Häfliger P, Mahowald M, Watts L (1997) A spike based learning neuron in analog VLSI. In: Mozer M, Jordan M, Petsche T (eds) Advances in neural information processing systems, vol 9. MIT Press, Cambridge, MA, pp 692–698

    Google Scholar 

  • Hahnloser R, Sarpeshkar R, Mahowald M, Douglas R, Seung S (2000) Digital selection and analog amplification co-exist in an electronic circuit inspired by neocortex. Nature 405(6789):947–951

    Article  CAS  PubMed  Google Scholar 

  • Hansel D, Sompolinsky H (1998) Modeling feature selectivity in local cortical circuits. In: Koch C, Segev I (eds) Methods in neuronal modeling. MIT Press, Cambridge, MA, pp 499–567

    Google Scholar 

  • Hylander P, Meador J, Frie E (1993) VLSI implementation of pulse coded winner take all networks. In: 36th midwest symposium on circuits and systems. Piscataway, NJ, vol 1, pp 758–761

    Google Scholar 

  • Indiveri G, Horiuchi TK (2011) Frontiers in neuromorphic engineering. Front Neurosci 5(118).doi 10.3389/fnins.2011.00118, http://www.frontiersin.org/neuromorphic_engineering/10.3389/fnins.2011.00118/full

  • Indiveri G, Horiuchi T, Niebur E, Douglas R (2001) A competitive network of spiking VLSI neurons. In: Rattay F (ed) World congress on neuroinformatics, ARGESIM/ASIM – Verlag, Vienna, ARGESIM Report no. 20, pp 443–455. http://ncs.ethz.ch/pubs/pdf/Indiveri_etal01.pdf

  • Indiveri G, Chicca E, Douglas R (2006) A VLSI array of low-power spiking neurons and bistable synapses with spike–timing dependent plasticity. IEEE Trans Neural Netw 17(1):211–221.doi 10.1109/TNN.2005.860850, http://ncs.ethz.ch/pubs/pdf/Indiveri_etal06.pdf

  • Indiveri G, Chicca E, Douglas R (2009) Artificial cognitive systems: from VLSI networks of spiking neurons to neuromorphic cognition. Cogn Comput 1:119–127. doi 10.1007/s12559-008-9003-6, http://ncs.ethz.ch/pubs/pdf/Indiveri_etal09.pdf

  • Indiveri G, Linares-Barranco B, Hamilton T, van Schaik A, Etienne-Cummings R, Delbruck T, Liu SC, Dudek P, Häfliger P, Renaud S, Schemmel J, Cauwenberghs G, Arthur J, Hynna K, Folowosele F, Saighi S, Serrano-Gotarredona T, Wijekoon J, Wang Y, Boahen K (2011) Neuromorphic silicon neuron circuits. Front Neurosci 5:1–23. doi 10.3389/fnins.2011.00073, http://www.frontiersin.org/Neuromorphic_Engineering/10.3389/fnins.2011.00073/abstract

  • Liu SC, Delbruck T (2010) Neuromorphic sensory systems. Curr Opin Neurobiol 20(3):288–295. doi:10.1016/j.conb.2010.03.007 5

    Article  PubMed  CAS  Google Scholar 

  • Liu SC, Kramer J, Indiveri G, Delbruck T, Douglas R (2002) Analog VLSI: circuits and principles. MIT Press. http://ncs.ethz.ch/pubs/pdf/Liu_etal02b.pdf

  • Markram H, Lübke J, Frotscher M, Sakmann B (1997) Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275:213–215

    Article  CAS  PubMed  Google Scholar 

  • Mead C (1990) Neuromorphic electronic systems. Proc IEEE 78(10):1629–1636

    Article  Google Scholar 

  • Merolla P, Arthur J, Shi B, Boahen K (2007) Expandable networks for neuromorphic chips. IEEE Trans Circuits Syst I 54(2):301–311

    Article  Google Scholar 

  • Mitra S, Indiveri G (2009) Spike-based synaptic plasticity and classification on VLSI. The Neuromorphic Engineer. doi 10.2417/1200904.1636, http://ncs.ethz.ch/pubs/pdf/Mitra_Indiveri09.pdf

  • Mitra S, Indiveri G, Fusi S (2008) Learning to classify complex patterns using a VLSI network of spiking neurons. In: Platt J, Koller D, Singer Y, Roweis S (eds) Advances in neural information processing systems 20, MIT Press, Cambridge, MA, pp 1009–1016. http://ncs.ethz.ch/pubs/pdf/Mitra_etal08.pdf

  • Mitra S, Fusi S, Indiveri G (2009) Real-time classification of complex patterns using spike-based learning in neuromorphic VLSI. IEEE Trans Biomed Circuits Syst 3(1):32–42, doi 10.1109/TBCAS.2008.2005781, http://ncs.ethz.ch/pubs/pdf/Mitra_etal09.pdf

  • Neftci E, Binas J, Rutishauser U, Chicca E, Indiveri G, Douglas RJ (2013). Synthesizing cognition in neuromorphic electronic systems. In: Proceedings of the national academy of sciences. Washington, USA, 110(37):E3468–E3476

    Google Scholar 

  • Oster M, Liu SC (2004) A winner-take-all spiking network with spiking inputs. In: 11th IEEE international conference on electronics, circuits and systems (ICECS 2004), Piscataway, NJ

    Google Scholar 

  • Pfeiffer M, Nessler B, Douglas RJ, Maass W (2010) Reward-modulated hebbian learning of decision making. Neural Comput 22(6):1399–1444. doi 10.1162/neco.2010.03-09-980

    Google Scholar 

  • Rutishauser U, Douglas R (2009) State-dependent computation using coupled recurrent networks. Neural Comput 21:478–509

    Article  PubMed  Google Scholar 

  • Sarpeshkar R (1998) Analog versus digital: extrapolating from electronics to neurobiology. Neural Comput 10(7):1601–1638

    Article  CAS  PubMed  Google Scholar 

  • Schemmel J, Brüderle D, Meier K, Ostendorf B (2007) Modeling synaptic plasticity within networks of highly accelerated I&F neurons. In: International symposium on circuits and systems, (ISCAS), 2007, IEEE, Piscataway, NJ, pp 3367–3370

    Google Scholar 

  • Serrano-Gotarredona T, Masquelier T, Prodromakis T, Indiveri G, Linares-Barranco B (2013) STDP and STDP variations with memristors for spiking neuromorphic learning systems. Front Neurosci 7(2). doi 10.3389/fnins.2013.00002, http://www.frontiersin.org/neuroscience/10.3389/fnins.2013.00002/full

  • Somers D, Nelson S, Sur M (1995) An emergent model of orientation selectivity in cat visual cortical simple cells. J Neurosci 15:5448–5465

    CAS  PubMed  Google Scholar 

  • Sze S (1981) Physics of semiconductor devices, 2nd edn. Wiley, New York

    Google Scholar 

  • Tomazou C, Lidgey F, Haigh D (eds) (1990) Analogue IC design: the current-mode approach. Peregrinus, Stevenage

    Google Scholar 

  • von Neumann J (1958) The computer and the brain. Yale University Press, New Haven

    Google Scholar 

  • Wijekoon J, Dudek P (2008) Compact silicon neuron circuit with spiking and bursting behaviour. Neural Netw 21(2–3):524–534

    Article  PubMed  Google Scholar 

  • Wilimzig C, Schneider S, Schöner G (2006) The time course of saccadic decision making: dynamic field theory. Neural Netw 19:1059–1074

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the EU ERC Grant “neuroP” (257219). Part of this work, including the one on soft WTA networks, was done in collaboration with Elisabetta Chicca. We acknowledge also the CapoCaccia and Telluride workshops on neuromorphic engineering (The CapoCaccia Cognitive Neuromorphic Engineering Workshop (http://capocaccia.ethz.ch/) and the Telluride neuromorphic cognition engineering workshop (http://www.ine-web.org)) for fruitful discussions on neuromorphic cognition.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Giacomo Indiveri .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Indiveri, G., Douglas, D.R. (2014). Neuromorphic Cognition. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_113-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_113-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7320-6

  • eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences

Publish with us

Policies and ethics