Modelling Brain and Mind

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Algorithm

Algorithmic complexity

Logical depth

Crypticity

Fractal dimension

Synthetic psychology

Braitenberg vehicle

Artificial neural network

Calculus

Gradient descent

Global minimum

Local minimum

Homeostasis

Supervised learning

The Rescorla-Wagner model

The Delta-rule

Cue-competition

Decision boundary

Linear separability

Exclusive-OR problem (XOR problem)

Backpropogation

Attractor

Self-organization

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Presenting a network with examples of input patterns along with information about what the correct outputs should be in response to those inputs

The behaviour of a circuit that controls its output to reduce the differences between its output and target ouput

Comes from neural network research and was discovered as a way of modifying the synaptic weights of an artificial neuron

A straight line in the input space, separated positive outputs from negative outputs of a neuron

Solution corresponds to drawing a straight line in a graph, that separates one category of ouputs from another

A set of artificial neurons whose outputs are connected to the inputs of others

The order in which associations are learn between stimulus (cue) and a reinforcer (reward/punishment) matters

How difficult it is to deduce the shortest possbile algorithm required to generate a sequence.

Should generate an output of 1 when either the first or the second input is 1 and it should generate an output of 0 when both inputs are 1 or 0

The number of iterations of the shortest algorithm required to generate a sequence.

A motor whose output value is calculated by multiplying each wire by the corresponding sensor reading and adding them together

This measures the length of the shortest computer program required to generate a particular sequence of symbols.

A branch of maths concerned with finding and using the derivative of a function

A series of instructions that should be followed

A supervised learning algorithm for updating the synaptic weights in a neural network

A method for finding the valleys in a function (the lowest point, where the error is lowest)

A ratio comparing how detail ina pattern (a fractal pattern) changes with the scale at which it is measured

Synthesizing the behabiourof complex systems like the brain and mind by exploring how simple models of biological processes interact with an environment

Discovered as a way of describing how associative strengths should change during a classical conditioning task

Spontaneous emergence of global order from a system of individuals interacting according to simple rules in the absence of a supervisor

A property of complex systems, such as a recurrent neural network. It's a set of states that a system can adopt where one state always leadds to a second state, to a third state etc.

The value of the parameter that generates the lowest possible error

A secondary valley with a higher error where the algorithm will stop before the global minimum