Abstract
By introducing a physiological constraint in the auto-correlation matrix memory, the system is found to acquire an ability in cognition i.e. the ability to identify and input pattern by its proximity to any one of the stored memories. The physiological constraint here is that the attribute of a given synapse (i.e. excitatory or inhibitory) is uniquely determined by the neuron it belongs. Thus the synaptic coupling is generally not symmetric. Analytical and numerical analyses revealed that the present model retrieves a memory if an input pattern is close to the pattern of the stored memories; if not, it gives a clear response by going into a special mode where almost all neurons are in the same state in each time step. This uniform mode may be stationary or periodic, depending on whether or not the number of the excitatory neurons exceeds the number of inhibitory neurons.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Amari S (1971) Characteristics of randomly connected threshold-element networks and network systems. Proc IEEE 59:35–47
Amari S (1977) Neural theory of association and concept formation. Biol Cybern 26:175–185
Amari S (1982) Competitive and cooperative aspects in dynamics of neural excitation and self-organization. In: Amari S, Arbib MA (eds) Competition and cooperation in neural nets. Springer, Berlin Heidelberg New York, pp 1–28
Amit DJ, Gutfreund H, Sompolinsky H (1985) Storing infinite number of patterns in a spin-glass model of neural networks. Phys Rev Lett 55:1530–1533
Anderson JA (1972) A simple neural networks generating iterative memory. Math Biosci 14:197–220
Buhmann J, Schulten K (1986) Assiciative recognition and storage in a model network of physiological neurons. Biol Cybern 54:319–335
Caianiello ER (1961) Outline of a theory of thought-process and thinking machines. J Theor Biol 2:204–235
Cooper LN (1973) A possible organization of animal memory and learning. In: Lundqvist B, Lundqvist S (eds) Proceeding of the Nobel symposium on collective of physical systems. Academic Press, New York, pp 252–264
Dotsenko VS (1985) Ordered spin glass: a hierarchical memory machine. J Phys C18:L1017-L1022
Eccles JC (1977) The understanding of the brain, 2nd edn. McGraw-Hill, New York
Fukushima K (1984) A hierarchical neural network model for associative memory. Biol Cybern 50:105–113
Fukushima K (1986) A neural network model for selective attention in visual pattern recognition. Biol Cybern 55:5–15
Hopfield JJ (1982) Neural networks and physical systems with emergent collective abilities. Proc Natl Acad Sci USA 79:2554–2558
Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci USA 81:3088–3092
Hopfield JJ, Feinstein DI, Palmer RG (1983) Unlearning has a stabilizing effect in collective memories. Nature 304:158–159
Kinzel W (1985) Learning and pattern recognition in spin glass models. Z Phys B—Condensed Matter 60:205–213
Kohonen T (1972) Correlation matrix memories. IEEE Trans. Computers C-21:353–359
Kohonen T (1984) Self-organization and associative memory. Springer, Berlin Heidelberg New York
Kohonen T, Reuhkala E, Mäkisara K, Vainio L (1976) Associative recall of images. Biol Cybern 22:159–168
Kuffler SW, Nicholls JG, Martin AR (1984) From neuron to brain. Sinauer, Massachusetts
Little WA (1974) The existence of persistent states in the brain. Math Biosci 19:101–120
Little WA, Shaw GL (1978) Analytic study of the memory strage capacity of a neural network. Math Biosci 39:281–290
Nakano K (1972) Associatron: a model of associative memory. IEEE Trans Syst Man Cybern SMC-2:380–388
Parga N, Virasoro MA (1986) The ultrametric organization of memories in a neural network. J Phys (Paris) 47:1857–1864
Parisi G (1986) Asymmetric neural networks and the process of learning. J Phys A 19:L675-L680
Peretto P, Niez JJ (1986) Stochastic dynamics of neural networks. IEEE Trans SMC-16:73–83
Personnaz L, Guyon I, Dreyfus G, Toulouse G (1986) A biologically constrained learning mechanism in networks of formal neurons. J Stat Phys 43:411–422
Shinomoto S (1986a) Statistical properties of neural networks. Prog Theor Phys 75:1313–1318
Shinomoto S (1986b) Talk at the 41st annual conference of the Physical Society of Japan
Toulouse G, Dehaene S, Changeux JP (1986) Spin glass model of learning by selection. Proc Natl Acad Sci USA, 83:1695–1698
Tsuda I, Koerner E, Shimizu H (1987) Memory dynamics in asynchronous neural networks. Prog Theor Phys (to be published)
Wilson HR, Cowan JD (1972) Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J 12:1–24
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Shinomoto, S. A cognitive and associative memory. Biol. Cybern. 57, 197–206 (1987). https://doi.org/10.1007/BF00364151
Received:
Issue Date:
DOI: https://doi.org/10.1007/BF00364151