A minimal dynamical system and analog circuit for non-associative learning (2405.05770v1)
Abstract: Learning in living organisms is typically associated with networks of neurons. The use of large numbers of adjustable units has also been a crucial factor in the continued success of artificial neural networks. In light of the complexity of both living and artificial neural networks, it is surprising to see that very simple organisms -- even unicellular organisms that do not possess a nervous system -- are capable of certain forms of learning. Since in these cases learning may be implemented with much simpler structures than neural networks, it is natural to ask how simple the building blocks required for basic forms of learning may be. The purpose of this study is to discuss the simplest dynamical systems that model a fundamental form of non-associative learning, habituation, and to elucidate technical implementations of such systems, which may be used to implement non-associative learning in neuromorphic computing and related applications.
- U. Alon. An Introduction to Systems Biology: Design Principles of Biological Circuits. Taylor & Francis, 2006.
- Mechanical vibration patterns elicit behavioral transitions and habituation in crawling Drosophila larvae. eLife, 12:e69205, 2023.
- Habituation in non-neural organisms: evidence from slime moulds. Proc. R. Soc. B, 283:20160446, 2016.
- Memory inception and preservation in slime moulds: the quest for a common mechanism. Phil. Trans. R. Soc. B, 374:20180368, 2019.
- L. Eckert. Learning in single cells - computational models of habituation. Master’s thesis, ETH Zurich, 2022.
- Habituation and sensitization in an aneural cell - some comparative and theoretical considerations. Neurosci. Biobehav. Rev., 6(2):183–194, 1982.
- Experience teaches plants to learn faster and forget slower in environments where it matters. Oecologia, 175:63–72, 2014.
- A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. Nat. Electron., 6(9):680–693, 2023.
- D. J. Patterson. Habituation in a protozoan vorticella convallaria. Behaviour, 45:304–311, 1973.
- Introduction to mathematical systems theory: a behavioral approach. Springer, Berlin, Heidelberg, 1998.
- Single-cell analysis of habituation in stentor coeruleus. Curr. Biol., 33(2):241–251.e4, 2023.
- Habituation revisited: An updated and revised description of the behavioral characteristics of habituation. Neurobiol. Learn. Mem., 92(2):135–138, 2009.
- E. D. Sontag. Mathematical Control Theory. Springer-Verlag, Berlin, Heidelberg, 2nd edition, 1998.
- J. E. Staddon. On rate-sensitive habituation. Adapt. Behav., 1(4):421–436, 1993.
- Multiple time scales in simple habituation. Psychol. Rev., 103(4):720–733, 1996.
- M. Stern and A. Murugan. Learning without neurons in physical systems. Annu. Rev. Condens. Matter Phys., 14:417–441, 2023.
- Habituation: A model phenomenon for the study of neuronal substrates of behavior. Psychol. Rev., 73(1):16–43, 1966.
- Neuro-inspired computing chips. Nat. Electron., 3(7):371–382, 2020.
- Neuromorphic learning with mott insulator nio. Proc. Natl. Acad. Sci. USA, 118(39):e2017239118, 2021.
- Habituation based synaptic plasticity and organismic learning in a quantum perovskite. Nat. Comm., 8, 240, 2017.
Sponsored by Paperpile, the PDF & BibTeX manager trusted by top AI labs.
Get 30 days freePaper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.