Abstract
Associative memory is a central cognitive task. However, the actual biological architecture that supports this memory is not currently known, so simulating with biologically plausible neurons and topologies is an ideal mechanism to improve understanding of associative memory. Simulations of spiking networks that perform associative memory tasks lay the groundwork for utilizing biological neurons in cognitive tasks. Specifically, this paper explores simulations of spiking networks that perform associative memory tasks using Hebbian cell assemblies of neurons to represent nodes and synapses to represent associations. The first tasks use binary cell assemblies to perform two well-known cognitive tasks. Then the paper examines different topologies of excitatory neurons for basic assemblies and their performance as short-term memory. Lastly, larger assemblies are associated in 2/3 sets, where two active elements can retrieve the third. Future research is proposed to explore the potential use of these assemblies and associations in cognitive tasks. By investigating biologically and cognitively plausible topologies, learning, and neurons, simulations will lead to an improved understanding of neuro-cognition, and potentially to systems that surpass the brittleness and domain specificity of current AI systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hebb, D.: The Organization of Behavior: A Neuropsychological Theory. Wiley (1949)
Huyck, C., Passmore, P.: A review of cell assemblies. Biol. Cybern. 107, 263–288 (2013)
Sakurai, Y., Tanisumi, Y., Ishihara, E., Hirokawa, J., Matanabe, H.: Multiple approaches to the investigation of cell assembly in memory research—present and future. Front. Syst. Neurosci. 12, 21 (2018)
Gerstner, W., Kistler, W., Naud, R., Paninski, L.: From Single Neurons to Networks and Models of Cognition. Cambridge University Press (2014)
Ji, Y., Gamez, D., Huyck, C.: A brain-inspired cognitive system that mimics the dynamics of human thought. In: 38th SGAI International Conference on Artificial Intelligence, Cambridge, UK (2018)
Stroop, J.: Studies of interference in serial verbal reactions. J. Exp. Psychol. 643–662 (1935)
Collins, A., Quillian, M.: Retrieval time from semantic memory. J. Verbal Learn. Verbal Behav. 8(2), 240–247 (1969)
Huyck, C., Ji, Y.: Two simple neurocognitive associative memory models. In: 16th International Conference on Cognitive Modelling (2018)
Buzsaki, G.: Neural syntax: cell assemblies, synapsembles, and readers. Neuron 68(3), 362–385 (2010)
Markram, H., Muller, E., Ramaswamy, S., Reimann, M.W., Abdellah, M., Sanchez, C.A., Ailamaki, A., et al.: Reconstruction and simulation of neocortical microcircuitry. Cell 163(2), 456–492 (2015)
Mengiste, S.A., Aertsen, A., Kumar, A.: Relevance of network topology for the dynamics of biological neuronal networks. bioRxiv 02 (2021)
Senk, J., Kriener, B., Djurfeldt, M., Voges, N., Jiang, H.-J., Schüttler, L., Gramelsberger, G., Diesmann, M., Plesser, H.E., van Albada, S.J.: Connectivity concepts in neuronal network modeling. PLoS Comput. Biol. 18(9), e1010086 (2022)
Davison, A.P., Brüderle, D., Eppler, J.M., Kremkow, J., Muller, E., Pecevski, D., Perrinet, L., Yger, P.: PyNN: a common interface for neuronal network simulators. Front. Neuroinform. 2, 388 (2009)
Erdos, P., Renyi, A.: On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 17–60 (1960)
Watts, D., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)
Albert, R., Barabasi, L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 47 (2002)
Brette, R., Gerstner, W.: Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94, 3637–3642 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ji, Y., Gamez, D., Huyck, C. (2024). Associative Memory with Biologically-Inspired Cell Assemblies. In: Samsonovich, A.V., Liu, T. (eds) Biologically Inspired Cognitive Architectures 2023. BICA 2023. Studies in Computational Intelligence, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-031-50381-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-50381-8_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50380-1
Online ISBN: 978-3-031-50381-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)