Neuromorphic Computing Based on Organic Memristive Systems

  • Victor ErokhinEmail author
Reference work entry
Part of the Encyclopedia of Complexity and Systems Science Series book series (ECSSS)


Artificial Neuron Networks (ANN)

Computing systems inspired by the biological neural networks.

Logic with memory

Logic gates with integrated memory: output depends not only on the current configurations of inputs but also on the history of the gate function.


Hypothetical element, introduced by L. Chua in 1971; originally, the resistance of the memristor must be a function of the passed charge.

Memristive device

Modern understanding of ideal Chua’s memristor, including devices, varying their resistance, capacity, or inductance.


Hypothetical element introduced by V. Braitenberg in 1984 for explaining learning capabilities.


Perceptron is an algorithm or device for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not).

Polyaniline (PANI)

Conducting polymer whose conductivity depends strongly on the doping level and redox state.



  1. Asamitsu A, Tomioka Y, Kuwahara H, Tokura Y (1997) Current switching of resistive states in magnetoresistive manganites. Nature 388:50–52CrossRefGoogle Scholar
  2. Baldi G, Battistoni S, Attolini G, Bosi M, Collini C, Iannotta S, Lorenzelli L, Mosca R, Ponraj JS, Verucchi R, Erokhin V (2014) Logic with memory: AND gates made of organic and inorganic memristive devices. Semicond Sci Technol 29:104009CrossRefGoogle Scholar
  3. Battistoni S, Dimonte A, Erokhin V (2016) Spectrophotometric characterization of organic memristive devices. Org Electron 38:79–83CrossRefGoogle Scholar
  4. Berzina T, Erokhin V, Fontana MP (2007) Spectroscopic investigation of an electrochemically controlled conducting polymer – solid electrolyte junction. J Appl Phys 101:024501CrossRefGoogle Scholar
  5. Berzina T, Erokhina S, Camorani P, Konovalov O, Erokhin V, Fontana MP (2009) Electrochemical control of the conductivity in an organic memristor: a time-resolved X-ray fluorescence study of ionic drift as a function of the applied voltage. ACS Appl Mater Interfaces 1:2115–2118CrossRefGoogle Scholar
  6. Braitenberg V (1984) Vehicles. Experiments in synthetic psychology. MIT Press, Cambridge, MAGoogle Scholar
  7. Chua LO (1971) Memristor – the missing circuit element. IEEE Trans Circuit Theory 18:507–519CrossRefGoogle Scholar
  8. Corinto F, Civalleri PP, Chua LO (2015) A theoretical approach to memristor devices. IEEE J Emerg Sel Top Circuits Syst 5:123–132CrossRefGoogle Scholar
  9. Demin VA, Erokhin VV, Kashkarov PK, Kovalchuk MV (2014) Electrochemical model of the polyaniline based organic memristive device. J Appl Phys 116:064507CrossRefGoogle Scholar
  10. Demin VA, Erokhin V, Emelyanov AV, Battistoni S, Baldi G, Iannotta S, Kashkarov PK, Kovalchuk MV (2015) Hardware elementary perceptron based on polyaniline memristive devices. Org Electron 25:16–20CrossRefGoogle Scholar
  11. Dimonte A, Fermi F, Berzina T, Erokhin V (2015) Spectral imaging method for studying Physarum polycephalum growth on polyaniline surface. Mater Sci Eng C 53:11–14CrossRefGoogle Scholar
  12. Emelyanov AV, Lapkin DA, Demin VA, Erokhin VV, Battistoni S, Baldi G, Dimonte A, Korovin AN, Iannotta S, Kashkarov PK, Kovalchuk MV (2016) First step towards the realization of a double layer perceptron based on organic memristive device. AIP Adv 6:111301CrossRefGoogle Scholar
  13. Erokhin V (2007) Polymer-based adaptive networks. In: Erokhin V, Ram MK, Yavuz O (eds) The new frontiers of organic and composite nanotechnologies. Elsevier, Oxford, Amsterdam, pp 287–353Google Scholar
  14. Erokhin V (2013) On the learning of stochastic networks of organic memristive devices. Int J Unconv Comput 9:303–310Google Scholar
  15. Erokhin V (2015) Bioelectronics brain using memristive polymer statistical systems. In: Carrara S, Iniewski K (eds) Handbook of bioelectronics. Cambridge University Press, Cambridge. pp 256–265Google Scholar
  16. Erokhin V, Fontana MP (2011) Thin film electrochemical memristive systems for bio-inspired computation. J Comput Theor Nanosci 8:313–330CrossRefGoogle Scholar
  17. Erokhin V, Berzina T, Fontana MP (2005) Hybrid electronic device based on polyaniline-polyethylene oxide junction. J Appl Phys 97:064501CrossRefGoogle Scholar
  18. Erokhin V, Berzina T, Camorani P, Fontana MP (2006) Conducting polymer – solid electrolyte fibrillar composite material for adaptive networks. Soft Matter 2:870–874CrossRefGoogle Scholar
  19. Erokhin V, Berzina T, Fontana MP (2007a) Polymeric elements for adaptive networks. Crystallogr Rep 52:159–166CrossRefGoogle Scholar
  20. Erokhin V, Berzina T, Camorani P, Fontana MP (2007b) Non-equilibrium electrical behavior of polymeric electrochemical junctions. J Phys Condens Matter 19:205111CrossRefGoogle Scholar
  21. Erokhin V, Berzina T, Camorani P, Fontana MP (2008) On the stability of polymeric electrochemical elements for adaptive networks. Colloids Surf A 321:218–221CrossRefGoogle Scholar
  22. Erokhin V, Berzina T, Camorani P, Smerieri A, Vavoulis D, Feng J, Fontana MP (2012a) Material memristive device circuit with synaptic plasticity: learning and memory. BioNanoScience 1:24–30CrossRefGoogle Scholar
  23. Erokhin V, Howard GD, Adamatsky A (2012b) Organic memristor devices for logic elements with memory. Int J Bifurcat Chaos 22:1250283MathSciNetCrossRefGoogle Scholar
  24. Erokhin V, Berzina T, Gorshkov K, Camorani P, Pucci A, Ricci L, Ruggeri G, Sigala R, Schuz A (2012c) Stochastic hybrid 3D matrix: learning and adaptation of electrical properties. J Mater Chem 22:22881–22887CrossRefGoogle Scholar
  25. Erokhina S, Sorokin V, Erokhin V (2015) Skeleton-supported stochastic networks of organic memristive devices: adaptation and learning. AIP Adv 5:027129CrossRefGoogle Scholar
  26. Gupta I, Serb A, Khiat A, Zeitler R, Vassanelli S, Prodromakis T (2016) Real-time encoding and compression of neuronal spikes by metal-oxide memristors. Nat Commun 7:12805CrossRefGoogle Scholar
  27. Hebb DO (1961) The organization of behavior: a neuropsychological theory, 2nd edn. Wiley, New YorkGoogle Scholar
  28. Kang ET, Neoh KG, Tan KL (1998) Polyaniline: a polymer with many interesting intrinsic redox states. Prog Polym Sci 23:277–324CrossRefGoogle Scholar
  29. Kim TH, Jang EY, Lee NJ, Choi DJ, Lee KJ, Jang J, Choi J, Moon SH, Cheong J (2009) Nanoparticle assemblies as memristors. Nano Lett 9:2229–2233CrossRefGoogle Scholar
  30. Klemenes I, Straub VA, Nikitin ES, Staras K, O’Shea M, Kemenes G, Benjamin PR (2006) Role of delayed nonsynaptic neuronal plasticity in long-term associative memory. Curr Biol 16:1269–1279CrossRefGoogle Scholar
  31. Minsky M, Papert S (1969) Perceptrons. MIT Press, CambridgezbMATHGoogle Scholar
  32. Pershin YV, Di Ventra M (2008) Spin memristive systems: spin memory effects in semiconductor spintronics. Phys Rev V 78:11309Google Scholar
  33. Pershin YV, Di Ventra M (2011) Memory effects in complex materials and nanoscale systems. Adv Phys 60:145–227CrossRefGoogle Scholar
  34. Prezioso M, Merrikh Bayat F, Hoskins BD, Adam GC, Likharev KK, Strukov DB (2015) Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521:61–64CrossRefGoogle Scholar
  35. Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65:386–408CrossRefGoogle Scholar
  36. Rosenblatt F (1961) Principles of neurodynamics: perceptions and the theory of brain mechanism. Spartan Books, Washington, DCCrossRefGoogle Scholar
  37. Schrodinger E (1944) What is life? Physical aspects of the living cells. Cambridge University Press, CambridgeGoogle Scholar
  38. Sigala R, Smerieri A, Schuz A, Camorani P, Erokhin V (2013) Modeling and simulating the adaptive electrical properties of stochastic polymeric 3D networks. Model Simul Mater Sci Eng 21:075007CrossRefGoogle Scholar
  39. Smerieri A, Berzina T, Erokhin V, Fontana MP (2008a) Polymeric electrochemical element for adaptive networks: pulse mode. J Appl Phys 104:114513CrossRefGoogle Scholar
  40. Smerieri A, Erokhin V, Fontana MP (2008b) Origin of current oscillations in a polymeric electrochemically controlled element. J Appl Phys 103:094517CrossRefGoogle Scholar
  41. Smerieri A, Berzina T, Erokhin V, Fontana MP (2008c) A functional polymeric material based on hybrid electrochemically controlled junctions. Mater Sci Eng C 28:18–22CrossRefGoogle Scholar
  42. Son DI, Park DH, Kim JB, Choi JW, Kim TW, Angadi B, Yi Y, Choi WK (2011) Bistable organic memory device with gold nanoparticles embedded in a conducting poly(N-vinylcarbazole) colloids hybrid. J Phys Chem C 115:2341–2348CrossRefGoogle Scholar
  43. Straub VA, Benjamin PR (2001) Extrinsic modulation and motor pattern generation in a feeding network: a cellular study. J Neurosci 21:1767–1778CrossRefGoogle Scholar
  44. Strukov DB, Snider GS, Steward DR, Williams RS (2008) The missing memristor found. Nature 453:80–83CrossRefGoogle Scholar
  45. Wang L, Duan M, Duan S (2013) Memristive perceptron for combinational logic classification. Math Probl Eng 2013:625790MathSciNetzbMATHGoogle Scholar
  46. Waser R, Aono M (2007) Nanoionic-based resistive switching memories. Nat Mater 6:833–840CrossRefGoogle Scholar
  47. Wasserman PD (1989) Neural computing: theory and practice. Van Nostrand Reinhold, New YorkGoogle Scholar
  48. Ziegler M, Soni R, Patelczyk T, Ignatov M, Bartsch T, Meuffels Kohlstedt PH (2012) An electronic version of Pavlov’s dog. Adv Funct Mater 22:2744–2749CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Materials for Electronics and Magnetism, Italian National Council of Research (CNR-IMEM)ParmaItaly

Personalised recommendations