Neural dynamics of adaptive timing and temporal discrimination during associative learning

N E U R A L D Y N A M I C S OF A D A P T I V E T I M I N G A N D T E M P O R A L D I S C R I M I N A T I O N D U R I N G A S S O C I A T I V E L E A R N I N G . S. Grossber~ and N. Schn~uk. Center for Adaptive Systems, Boston University, 111 Cumm|ngton Street, Boston, MA 0~15 USA. Humans and animals are capable of learning under real-time training conditions to generate appropriately timed responses to sensory stimuli. An extensive experimental literature exists describing the properties of such timed behavior during classical conditioning and instrumental learning experiments. The present work develops a neural network model that has been used to quantitatively simulate key properties of this data base on the computer. The model further develops a theory of cognitive-emotional interactions that is described in Grossberg (1987), Grossberg and Levine (1987), and Grossberg and Sclun~uk (1987). This theory includes a design for an opponent processing neural circuit,called a gated dipole, whereby sensory cues, called conditioned stimuli (CS), learn to control the affective reactions---such as fear, relief,hunger, and sexual arousalmthat are generated by rewards or punishments, called unconditioned stimuli (US). These a~ctive reactions, in turn, motivate motor responses. The present work describes a neural network that learns the types of timed reactions that are found in data about learned t|m|ng. The model consists of three layers of neural elements. A step function Its(t), activated by the CS presentation, excites the first layer (z~) according to a spectrum of rates ai: d =

+

(B

-

cx,)Ics(t)].

The output of each unit in the first layer is a sigmoid function of z~, f(z~), that activates a second layer of habituating transmitter gates d

The output of each unit in the trausmitter gate activates a long-term memory trace z~ such that d where/Us (t) is a US input. The readouts of all long-term memory traces are added to yield the conditioned response CR(t) oR(t) = i

Smi found that different C2(t) curves are obtained when the intmtim interval tSI) between CS and US onset are varied. Each curve shows a peak at the time when the US was ~ ~ ~ , and thewldth under each curve increases with increasing values of the ISI. The time and stan.dard .de,clarion is called a Weber Law. Computer simulations show that the model c e m e ~ r l b e s Smith's (196S) data. Ako in agreement with Smith (1968), the model shows th&t intensity change the C~(t) amplitudes but not the peak times of the CR(t) c o r r ~ s ~ to d~N~mt~Sl's. In s4[r~ment with data of Burkhardt and Ayres (1978)., when US's of SO, I00, 200 W . in duration are presented for 5 training trials, the peak amplitude of OR(t) during a test trial ~ with US ~ u . , ~ m . In al[l~enmnt with data of M ' ~ et ~. ~1977), a two-peak CR(t) f~tet'm~t k ~ when the CS and US are presented on ten trials with CS-US ISIs that alternate between 100 msec. sad ~ nmec. The present model clarifies how learning of properly thned individual responses is achieved. Learned timing of resp?nse sequences utilizes adaptive circuits for sensory-motor planning, such as awdanehe circuits ~ , 1987), into which copies of the circuit described herein may be naturally embedded. References Burkhardt, P.E. and Ayres, J.J.B. (1978). CS and US duration effects in o n e - t r i a l m ~ e o t m f ~ a r conditioning as assessed by conditioned suppression of licking in rats. Anhnal ~ aag ~ ' ~ e , 6, ~ 2 ~ I 0 . Grossberg, S. (Ed.) (19S7). The A d a p t i v e Brain, "volumes I and II. Amsterdam: Elsevier/Nerth-HoUand. Grossberg, S. and Levln,e, D.S. (I~7). Neural ~ of Blockk~, interstimuhm interval, mad m~ezlm7 ~ t Grossberg, S. and Schm~uk, N.A. (I~7). Neural CondRioned rehdbeeeme~t, inhibRkm, s ~ l oppm~ MUlenson, J.R., Kehoe, E.J., and Gormennno, L (1977). membrane response under Rxed and ~ OS-US in~.vals. nictitating Smith, M.C. (1968). CS-US interval and US membrane reepma~a. J o u r n a / d Comparsth,e au

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