Experiments in artificial psychology: conditioning of asynchronous neural network models.

An asynchronous model for the dynamics of neural networks admits learning behaviors characteristic of classical and operant conditioning provided that appropriate plasticity algorithms are chosen. Stimulus generalization and discrimination can also be observed. Studies of such psychological phenomena are carried out by computer simulation of networks with designated sensory, association, and motor neurons, and the results are compared to those for live subjects. Various prescriptions for plasticity are investigated, including those corresponding to reward, punishment, and unlearning routines. These are characterized by their effect on network stability as quantified by a newly proposed stability measure.

[1]  S. Solla,et al.  Memory networks with asymmetric bonds , 1987 .

[2]  Alessandro Treves,et al.  Metastable states in asymmetrically diluted Hopfield networks , 1988 .

[3]  D. Kleinfeld,et al.  "Unlearning" increases the storage capacity of content addressable memories. , 1987, Biophysical journal.

[4]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[5]  A. M. Uttley.  A two-pathway informon theory of conditioning and adaptive pattern recognition , 1976, Brain Research.

[6]  Gèunther Palm,et al.  Neural Assemblies: An Alternative Approach to Artificial Intelligence , 1982 .

[8]  G. Parisi,et al.  Asymmetric neural networks and the process of learning , 1986 .

[9]  J. A. Hertz,et al.  Irreversible spin glasses and neural networks , 1987 .

[10]  Stephen Grossberg,et al.  Classical and Instrumental Learning by Neural Networks , 1982 .

[11]  M. Carpenter,et al.  The Essentials of Neuroanatomy , 1978 .

[12]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[13]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[14]  Francis Crick,et al.  The function of dream sleep , 1983, Nature.

[15]  Arthur Wingfield,et al.  Human learning and memory : an introduction , 1979 .

[16]  E. Gardner,et al.  An Exactly Solvable Asymmetric Neural Network Model , 1987 .

[17]  R. Sutton,et al.  Simulation of anticipatory responses in classical conditioning by a neuron-like adaptive element , 1982, Behavioural Brain Research.

[18]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Karl Steinbuch,et al.  Automat und Mensch , 1962 .

[20]  J. J. Hopfield,et al.  ‘Unlearning’ has a stabilizing effect in collective memories , 1983, Nature.

[21]  A. M. Uttley.  Simulation studies of learning in an informon network , 1976, Brain Research.

[22]  J. Hopfield,et al.  The Logic of Limax Learning , 1985 .

[23]  Inhomogeneous magnetization in dilute asymmetric and symmetric systems. , 1988, Physical review letters.

[24]  Lev B. Ioffe,et al.  The statistical properties of the hopfield model of memory , 1986 .

[25]  Local reverberations in the nervous system and conditioned reflex , 1971 .

[26]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[27]  J. W. Clark,et al.  Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions , 1985 .

[28]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[29]  L. Crespi.  Quantitative variation of incentive and performance in the white rat. , 1942 .

[30]  E. Caianiello.  Outline of a theory of thought-processes and thinking machines. , 1961, Journal of theoretical biology.

[31]  Haim Sompolinsky,et al.  STATISTICAL MECHANICS OF NEURAL NETWORKS , 1988 .

[32]  Sompolinsky,et al.  Dynamics of spin systems with randomly asymmetric bonds: Langevin dynamics and a spherical model. , 1987, Physical review. A, General physics.