- Connectionism, neural networks
- The idea that
*the hardware matters* - Neurally-inspired cognitive modeling

- The idea that
- What is a connectionist model?
- A network
- A vector of
**activations**(length*n*), corresponding roughly to the firing patterns of neurons - Sometimes a separate vector of
**outputs** - A matrix of
**weights**(dimensions*n*X*n*), corresponding roughly to the structures that connect neurons to one another: axons, dendrites; synapses

- A vector of
- A task
- A set of
**input**vectors - (Sometimes) a set of
**target**vectors

- A set of
- An activation rule

- A weight (learning) rule

- A network
- A connectionist unit (corresponding roughly to a neuron
or a cluster of neurons)

- Input and output
- Matrix-memory models: all units participate potentially in input and output
- Others: separate input, output, and sometimes
**hidden**units

- Representations
- Localist
- Distributed: input, output, hidden
- Multiple units participate in the representation of each concept.
- Multiple concepts are represented by each unit.

- Continuous and discrete time
- Static and sequential networks
- Learning
- Supervised
- Reinforcement
- Unsupervised

- Connectivity
- Feedforward
- Partially recurrent
- Completely recurrent; constraint satisfaction

**Connectionism**: Connectionist vs. Symbolic Models

Last updated: 26 February 1996

URL: http://www.indiana.edu/~gasser/Q351/connectionism1.html

Comments:
`gasser@salsa.indiana.edu`

Copyright 1996,
The Trustees of
Indiana University