Modelling Brain and Mind

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  Tempo Rimasto


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

A series of instructions that should be followed

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

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

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

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

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

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

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

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

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

The value of the parameter that generates the lowest possible error

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

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

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

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

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

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

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.

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

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

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

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