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.
Your quizzes will be handed back next week.
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
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.
Unit connection unit
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.
E.g. cat recognition
Input threshold: .8
Output strength: .5
Input threshold: .9
Output: “it’s a cat”
Note: every connection has a weight, but I’ve only shown a few of the weights for simplicity.
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.
How does this compare to how memory works?
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
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.