The Computational Brain
Patricia S. Churchland, Terrence J. Sejnowski, 1992


1. Introduction

2. Neuroscience Overview


Levels in nervous systems

Structure at various levels of organization

A short list of brain facts

3. Computational Overview


Looking up the answer

Linear associators

Constraint satisfaction: Hopfield networks and Boltzmann machines

Learning in neural nets

Competitive learning

Curve fitting

Feedfoward nets: Two examples

Recurrent nets

From toy world to real world

What good are optimization procedures to neuroscience7

Models: Realistic and abstract

Concluding remarks

4. Representing the World


Constructing a visual world

Thumbnail sketch of the mammalian visual system

Representing in the brain: What can we learn from the visual systeml

What is so special about distribution?

World enough and time

Shape from shading: A neurocomputational study

Stereo vision

Computational models of stereo vision

Hyperacuity: From mystery to mechanism

Vector averaging

Concluding remarks

5. Plasticity: Cells, Circuits, Brains, and Behavior


Learning and the hippocampus

Donald Hebb and synaptic plasticity

Memories are made of this: Mechanisms of neuronal plasticity

Cells and circuits

Decreasing synaptic strength

Back to systems and behavior

Being and timing

Development o~ nervous systems

Modules and networks

6. Sensorimotor Integration



Computation and the vestibule-ocular reflex

Time and time again

The segmental swimming oscillator

Modeling the neuron

Concluding remarks

7. Concluding and Beyond


Anatomical and Physiological Techniques


Permanent lesions

Reversible lesions and microlesions

Imaging techniques

Gross electrical and magnetic recording

Single-unit recording

Anatomical tract tracing

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