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NEURONAL NETWORKS AND CONNECTIONIST (PDP) MODELS

NEURONAL NETWORKS AND CONNECTIONIST (PDP) MODELS. Thorndike’s “Law of Effect” (1920’s) Reward strengthens connections for operant response Hebb’s “reverberatory circuits” (1940’s) Self-sustaining circuits of activity This “stm” consolidates memories

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NEURONAL NETWORKS AND CONNECTIONIST (PDP) MODELS

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  1. NEURONAL NETWORKS ANDCONNECTIONIST (PDP) MODELS • Thorndike’s “Law of Effect” (1920’s) • Reward strengthens connectionsfor operant response • Hebb’s “reverberatory circuits” (1940’s) • Self-sustaining circuits of activity • This “stm” consolidates memories • “Hebbian” learning (Co-occurrence of activation strengthens synaptic paths) • Memory as stable changes in connection strengths (CONNECTIONISM) • Selfridge’s Pandemonium (1955) • A parallel, distributed model ofpattern recognition

  2. Selfridge’s Pandemonium Model of Letter Recognition

  3. Rosenblatt’s Perceptrons (1962) • Simple PDP model that could learn • Limited to certain kinds of associations • Hinton & Anderson (1981) • PDP model of associative memory • Solves the “XOR” problem with hidden layers of units • Adds powerful new learning algorithms • McClelland & Rumelhart (1986) • Explorations in the Microstructure of Cognition • Shows power and scope of PDP approach • Emergence of “subsymbolic” models and brain metaphor • PDP modeling explodes

  4. A symbolic-level network model(McClelland, 1981) • The Jets and Sharks model • Parallel connections and weights between units • Excitatory and inhibitory connections • Retrieval, not encoding • “units” still symbolic

  5. A PDP TUTORIAL • A PDP model consists of • A set of processing units (neurons) • Usually arranged in “layers” • Connections from each unit in one layer to ALL units of the next Input Hidden Output

  6. Each unit has an activity level • Knowledge as distributed patterns of activity at input and output • Connections vary in strength • And may be excitatory or inhibitory input -.5 -.5 +.5 +.5 output

  7. j2 j1 • Input to unit k = sum of weighted connections • for each connection from layer j: input from (j) =activity(j) x weight(j to k) • The same net can associate a large number of distinct I/O patterns +1 +1 -1 k1 -.5 -.5 +.5 +.5 k1 +1 -1 +1 0 -.5 -.5 +.5 +.5 0

  8. The I/O function of units can be linear or nonlinear • Hidden layers add power and mysterye.g., solving the XOR problem: threshold +1 +1 +1 +1 +1 +1 threshold +1 -2 +1

  9. LEARNING IN PDP MODELS • Learning algorithms • Rules for adjusting weights during “training” • The Hebbian Rule • Co-activation strengthens connection • Change (jk) = activation (j) x activation (k) • The Delta Rule • Weights adjusted by amount of error compared to desired output • Change (jk) = activation (j) x r[error (k)] • Backpropagation • Delta rule adjustments cascade backward through hidden unit layers

  10. EVALUATION OF PDP MODELS • Wide range of successes of learning • XOR and other “nonlinear” problems • Text-to-speech associations • Concepts and stereotypes • Grammatical “rules” and exceptions • Powerful and intuitive framework • At least some “biological face validity” • Parallel processing • Distributed networks for memory • Graceful degradation • complex behavior emerges from “simple” system

  11. Limitations and issues • Too many “degrees of freedom”? • Learning even “simple” associations requires thousands of cycles • Some processes (e.g., backpropagation) have no known neural correlate • (yes, but massive feedback and “recurrent” loops in cortex) • Some failures to model human behavior • Pronouncing pseudohomophones • Abruptness of stages in verb learning • Catastrophic retroactive interference • Difficulty in modelling “serially ordered” and “rule-based” behavior • Need for processes outside of model

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