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Connectionism. Quiz. Your quizzes will be handed back next week. Papers. Handing in your paper: Hand in a hard copy to Loletta in the Philosophy Office by 3 p.m. this Thursday (October 25). Note: the office is closed from 1:00-2:00.

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Quiz

Your quizzes will be handed back next week.


Papers
Papers

Handing in your paper:

Hand in a hard copy to Loletta in the Philosophy Office by 3 p.m. this Thursday (October 25). Note: the office is closed from 1:00-2:00.

Hand in a soft copy to turnitin.com by Thursday night. Class name: PHIL2230, password: cogsci

Avoid plagiarism:

  • It’s wrong

  • You will be caught.


What is connectionism
What is connectionism?

  • Also known as:

    • Parallel distributed processing (PDP)

    • Artificial neural networks (ANN) or just “neural networks”

  • An alternative to symbolic representation

  • No language of thought

  • An attempt to model neural processes in the brain:

    • Parallel processing of information

    • Network of connections between neurons/units



Connectionist models consist of four parts – interconnected networks of simple units

  • Units

  • Activations

  • Connections

  • Connection weights

    Units are connected to each other in a network. In response to input, a unit is activated, sending signals to other units that it is connected with. The strength of those signals is determined by the connection weights between the connected units.

    Signals sent from one unit or another can be either excitatory or inhibitory.

.5

Unit connection unit

connection weight


Units and connections are arranged in input-output layers, usually with one or more hidden layer.


Close up of one unit
Close-up of one unit usually with one or more hidden layer.

a1, a2, and a3 represent the connection weights of the input it receives from other units.

The three ‘aj’s represent the connection weights of its output to other units.


A unit is activated when the combined weights of its input exceeds a set level.

E.g. cat recognition

Input threshold: .8

Output strength: .5

Meows

Fur

Pointed ears

Whiskers

.2

.5

.3

.3

.4

Input threshold: .9

Output: “it’s a cat”

.5

.4

Note: every connection has a weight, but I’ve only shown a few of the weights for simplicity.


A tribute to interactive activation at: exceeds a set level.

http://srsc.ulb.ac.be/pdp/iac/IAC.html

Built in 1981.

Demonstration of a neural network illustrating an artificial network that exhibits many properties of human memory.


This animated network represents information about two gangs: the Jets and the Sharks. The central pool of units represents members of the gangs (e.g. Sam, Art, etc.) The surrounding pools represent characteristics of these members, e.g. the names (“Sam”, “Art”, etc.), age, occupation, marital status, gang affiliation and educational level.Within most pools, units are connected with inhibitory weights, showing that they are mutually exclusive: if x is married, x is not single; if x is named “Art”, x is not named “Steve”, etc.


Things to try with the Sharks and Jets network: gangs: the Jets and the Sharks. The central pool of units represents members of the gangs (e.g. Sam, Art, etc.) The surrounding pools represent characteristics of these members, e.g. the names (“Sam”, “Art”, etc.), age, occupation, marital status, gang affiliation and educational level.

  • Find the characteristics of one particular member

  • Identify a member by certain characteristics (e.g. who is a Shark in his 20s?)

  • Identify general characteristics of members of a gang, or members with a certain characteristic (e.g. what characteristics are common to bookies in this group)?

    How does this compare to how memory works?


Network training gangs: the Jets and the Sharks. The central pool of units represents members of the gangs (e.g. Sam, Art, etc.) The surrounding pools represent characteristics of these members, e.g. the names (“Sam”, “Art”, etc.), age, occupation, marital status, gang affiliation and educational level.

Connection weights determine a network’s functioning.

Connection weights either “hand-coded” or built up during training

1) Hand-coded – connection weights set manually by the network builder

e.g. Sharks and Jets network is hand-coded


2) gangs: the Jets and the Sharks. The central pool of units represents members of the gangs (e.g. Sam, Art, etc.) The surrounding pools represent characteristics of these members, e.g. the names (“Sam”, “Art”, etc.), age, occupation, marital status, gang affiliation and educational level.Connection weights built up through training

Networks “learn”:

Connection weights often set at random before training

Networks are trained via back propagation

Responses of the network are judged right or wrong (the network is “rewarded” or “punished”)

When the output is judged correct, excitatory connections are strengthened, while inhibitory connections are weakened.

When output is judged incorrect, excitatory connections are weakened, while inhibitory connections are strengthened.

Training is slow. Needs a lot of feedback.


What connectionist networks can learn to do
What connectionist networks can learn to do gangs: the Jets and the Sharks. The central pool of units represents members of the gangs (e.g. Sam, Art, etc.) The surrounding pools represent characteristics of these members, e.g. the names (“Sam”, “Art”, etc.), age, occupation, marital status, gang affiliation and educational level.

  • Mine/rock discrimination

  • NETtalk

  • Forming past tenses of English verbs

  • Face recognition (men vs. women, wearing sunglasses)


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