Abstract
A conditioned response not only reflects knowledge of an association between two events, a CS and a US, it also reflects knowledge about the timing of these events. A neural network and set of learning rules that generates appropriately timed conditioned response waveforms is presented. The model is capable of simulating some of the basic temporal properties of conditioned responses exhibited in biological systems, including (1) decreasing onset latency during acquisition training, (2) peak amplitude accurring at the temporal locus of the US, (3) inhibition of delay, and (4) trace conditioning. The model is also capable of simulating complex CR waveforms under certain conditions, and these simulations are compared with the results of behavioral experiments. The temporally adaptive responses are achieved by virtue of stimulus trace processes that are built into the network architecture.
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References
Barto AG, Sutton RS (1982) Simulation of anticipatory responses in classical conditioning by a neuron-like adaptive element. Behav Brain Res 4:221–235
Blazis DEJ, Desmond JE, Moore JW, Berthier NE (1986) Simulation of the classically conditioned nictitating membrane response by a neuron-like adaptive element: a real-time variant of the Sutton-Barto model. In: Program of the Eighth Annual Conference of the Cognitive Science Society. Erlbaum, Hillsdale, NJ, pp 176–186
Braitenberg V (1967) Is the cerebellar cortex a biological clock in the millisecond range? In: Fox CA, Snider RS (eds) Progress in Brain Research, vol. 25. The Cerebellum, Elsevier, New York, pp 334–346
Coleman SR, Gormezano I (1971) Classical conditioning of the rabbit's (Oryctolagus cuniculus) nictitating membrane response under symmetrical CS-US interval shifts. J Comp Physiol Psychol 77:447–455
Desmond JE, Blazis DEJ, Moore JW, Berthier NE (1986) Computer simulations of a classically conditioned response using neuron-like adaptive elements: response topography. Soc Neurosci 12:516 (abstr)
Freeman JA (1969) The cerebellum as a timing device: An experimental study in the frog. In: Llinas R (ed) Neurobiology of cerebellar evolution and development. American Medical Association. Chicago, pp 397–420
Gelperin A, Hopfield JJ, Tank DW (1985) The logic of Limax learning. In: Selverston A (ed) Model neural networks and behavior. Plenum Press, New York
Gingrich KJ, Byrne JH (1987) Single-cell neuronal model for associative learning. J Neurophys 57:1705–1715
Gluck MA, Thompson RF (1987) Modeling the neural substrates of associative learning and memory: a computational approach. Psychol Rev 94:176–191
Gormezano I (1972) Investigations of defense and reward conditioning in the rabbit. In: Black AH, Prokasy WF (eds) Classical conditioning. II. Current research and theory. Appleton-Century-Crofts, New York, pp 151–181
Gormezano I, Moore JW (1969) Classical conditioning. In: Marx MH (ed) Learning: processes. Collier-Macmillan, London
Gormezano I, Kehoe EJ, Marshall BS (1983) Twenty years of classical conditioning with the rabbit. Prog Psychobiol Physiol Psychol 10:197–275
Grossberg S, Schmajuk NA (1987) Neural dynamics of attentionally modulated Pavlovian conditioning: conditioned reinforcement, inhibition, and opponent processing. Psychobiology 15:195–240
Hawkins RD, Kandel ER (1984) Is there a cell-biological alphabet for simple forms of learning? Psychol Rev 91:375–391
Hebb DO (1949) The organization of behavior. Wiley, New York
Hull CL (1943) Principles of behavior. Appleton-Century-Crofts, New York
Kamin LJ (1986) Attention-like processes in classical conditioning. In: Prokasy WF (ed) Classical conditioning: a symposium. Appleton, New York, pp 118–147
Kelso SR, Ganong AH, Brown TH (1986) Hebbian synapses in hippocampus. Proc Natl Acad Sci USA 83:5326–5330
Kent EW (1981) The brains of men and machines. BYTE/McGraw Hill, Peterborough, NH
Klopf AH (1986) A drive reinforcement model of single neuron function: an alternative to the Hebbian neural model. In: Denker JS (ed) Neural networks for computing. AIP Conference Proceedings 151. American Institute of Physics, New York
Levine DS (1986) A neural network model of temporal order effects in classical conditioning. In: Eisenfeld J, Witten M (eds) Modelling of biomedical systems. Elsevier, North-Holland
Liu SS, Moore JW (1969) Differential conditioning of the rabbit nictating membrane response: IV. Training based on stimulus offset and the effect of an intertrial tone. Psychonom Sci 15:128–129
Logan FA (1956) A micromolar approach to behavior theory. Psychol Rev 65:63–73
Marchant HG, III, Moore JW (1973) Blocking of the rabbit's conditioned nictitating membrane response in Kamin's two-stage paradigm. J Exp Psychol 101:155–158
Marchant HG, III, Mis FW, Moore JW (1972) Conditioned inhibition of the rabbit's nictitating membrane response. J Exp Psychol 95:408–411
Millenson JR, Kehoe EJ, Gormezano I (1977) Classical conditioning of the rabbit's nictitating membrane response under fixed and mixed CS-US intervals. Learn Motiv 8:351–366
Moore JW, Desmond JE, Berthier NE, Blazis DEJ, Sutton RS, Barto AG (1986) Simulation of the classically conditioned nictitating membrane response by a neuron-like adaptive element: response topography, neuronal firing, and interstimulus intervals. Behav Brain Res 21:143–154
Pavlov IP (1927) Conditioned reflexes. Dover, New York
Rescorla RA (1969) Pavlovian conditioned inhibition. Psych Bull 72:77–94
Scheibel ME, Scheibel AB (1958) Structural substrates for integrative patterns in the brain stem reticular core. In: Jasper H, Proctor LD, Knighton RS, Noshay WS, Costello RT (eds) Reticular formation of the brain. Little Brown, Boston, pp 31–55
Scheibel ME, Scheibel AB (1967) Anatomical basis of attention mechanisms in vertebrate brains. In: Quarton GC, Melnechuk T, Schmitt FO (eds) The neurosciences. A study program. The Rockefeller University Press, New York, pp 577–602
Schmajuk NA, Moore JW (1985) Real-time attentional models for classical conditioning and the hippocampus. Physiol Psychol 13:278–290
Schneiderman N (1966) Interstimulus interval function of the nictitating membrane response in the rabbit under delay versus trace conditioning. J Comp Physiol Psychol 62:397–402
Schneiderman N (1972) Response system divergencies in aversive classical conditioning. In: Black AH, Prokasy WF (eds) Classical conditioning. II. Current research and theory. Appleton-Century Crofts, New York, pp 341–376
Schneiderman N, Gormezano I (1964) Conditioning of the nictitating membrane of the rabbit as a function of CS-US interval. J Comp Physiol Psychol 57:188–195
Smith MC (1968) CS-US interval and US intensity in classical conditioning of the rabbit's nictitating membrane response. J Comp Physiol Psychol 66:679–687
Sutton RS, Barto AG (1981) Toward a modern theory of adaptive networks: expectation and prediction. Psychol Rev 88:135–170
Sutton RS, Barto AG (1987) A temporal-difference model of classical conditioning. Technical report TR 87-509.2, GTE Lab, Waltham, MA
Tesauro G (1986) Simple neural models of classical conditioning. Biol Cybern 55:187–200
Wagner AR (1981) SOP: A model of automatic memory processing in animal behavior. In: Spear NE, Miller RR (eds) Information processing in animals: memory mechanisms. Erlbaum, Hillsdale, NJ
Widrow B, Stearns SD (1985) Adaptive signal processing. Prentice-Hall, Englewood Cliffs, NJ
Wigström H, Gustafsson B, Huang Y-Y, Abraham WC (1986) Hippocampal long-term potentiation is induced by pairing single afferent volleys with intracellularly injected depolarizing current pulses. Acta Physiol Scand 126:317–319
Zipser D (1986) A model of hippocampal learning during classical conditioning. Behav Neurosci 100:764–776
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Desmond, J.E., Moore, J.W. Adaptive timing in neural networks: The conditioned response. Biol. Cybern. 58, 405–415 (1988). https://doi.org/10.1007/BF00361347
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DOI: https://doi.org/10.1007/BF00361347