cognition, brain and consciousness: an introduction to cognitive neuroscience

Cognition, Brain and Consciousness: An Introduction to Cognitive NeuroscienceEdited by Bernard J. Baars and Nicole M. Gage2007 Academic Press

Chapter 3 Neurons and Their Connections

“There is no more important quest in the whole of science probably than the attempt to understand those very particular events in evolution by which brains worked out that special trick that enabled them to add to the scheme of things: color, sound, pain, pleasure, and all the facets of mental experience.”

Roger Sperry, 1976


Chapter Outline

1.0 Introduction

2.0 Working assumptions

3.0 Arrays and maps

4.0 How neural arrays adapt and learn

5.0 Coordinating neural nets

6.0 Summary


1.0 Introduction

Real and idealized neurons

A single bipolar neuronCortical neurons may have

10,000 dendrites (input fibers) and 1 or more axons (output

fibers)

An idealized neuronA simplified neuron with dendrites

shown on top and axon at the bottom


1.0 Introduction

Excitation and inhibition

Neurons are connected through synapses which can be excitatory or inhibitory

The probability that the next neuron will fire a spike will be increased if it an excitatory connection or decreased if it is an inhibitory connection


1.0 Introduction

Neural computation

Simplified -- idealized -- neurons are used in artificial neural nets (ANN) to model many brain functions.

ANNs are artificial but they have provided understanding of ways neural computation might work in the brain.


2.0 Working Assumptions

Starting simple: receptors, pathways, and circuits

Six working assumptions:

1. Neurons work using an integrate-and-fire action2. Connections are either excitatory or inhibitory3. Idealized neurons are used in artificial neural nets to model brain

function4. Neurons typically form two-way pathways, providing the basis

for re-entrant connectivity5. The nervous system is formed into arrays or maps of neurons6. Hebbian cell assemblies underlie the change from transient to

stable, lasting connections


3.0 Arrays and Maps

Maps flow into other maps: The nervous system often uses layers of neurons in giant arrays.


3.0 Arrays and Maps

Neuronal arrays usually have two-way connections


3.0 Arrays and Maps

Sensory and motor systems work together


3.0 Arrays and Maps

Temporal codes: spiking patterns and brain rhythms

Neurons have different spiking codes. These two electrode traces show the voltage of simulated neurons with differing spiking codes


3.0 Arrays and Maps

Choice-points in the flow of information

Ambiguous figures such as the face-vase illusion (a) and Necker cube (b) pose points at which the brain must make a decision or choice about how to perceive and interpret sensory input.


4.0 How Neural Arrays Adapt and Learn

Hebbian learning: ‘Neurons that fire together, wire together’

Donald Hebb was one of the most influential theorists for cognitive science and neuroscience. He clarified the notion of the cell assembly and proposed the best-known learning rule for neural networks



Neural Darwinism: survival of the fittest cells and synapses

An example of Neural Darwinism in learning: stages of encoding a neural activation pattern until dynamic synaptic activity allows

permanent connections to be strengthened, allow memories to be stored



Symbolic processing and neural nets

A network which represents a large set of propositions such as ‘a robin is a bird’ and ‘a rose has petals’.


5.0 Coordinating Neural Nets

An activation map of visual areas active while a subject is watching a movie. Note the correlation of neural activity in the left hemisphere (top of figure,marked with ‘l’) and the right hemisphere, (bottom of figure, marked with ‘r’) across differing visual areas such as V3 and V4.


6.0 Summary

A basic question in cognitive neuroscience is how nerve cells combine to perform complex cognitive functions such as perception, memory, and action. While neurons form the basic building block of cognition, we are still unfolding how they work both as individual cells and in synchrony in large scale arrays.

Some working assumptions about how neurons work -- such as the integrate-and-fire neuron, two-way pathways, cell assemblies and artificial neural nets -- have allowed scientists to begin to model the complex and dynamic activity in the brain that underlies human cognition.

cognition, brain and consciousness: an introduction to cognitive neuroscience

Documents

cognitive neuroscienceedited

introduction real

neural arrays

introduction excitation

brain functionneurons

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