Hopfield Nets
- The units of a Hopfield Nets are integrate and fire neurons.
- The net is well connected, and the connections are bidirectional.
- They can be continuous or discrete, but we'll consider discrete.
- Here's an example; the missing arcs are 0.
![](smallHopfield.gif)
- If you turn on some units, the net will run and settle into
a stable state (or a two state oscillator).
- 00111->00111
- So, it is an auto-associative memory.
- It retrieves patterns from the inputs.
- Here, if you put in a corrupted pattern, you get the original.
![](hopfieldT.gif)
- This is a spin-glass model from Physics.
- There is a lot of work with statistical mechanics that you
can use to prove things about these and related nets.
- You can use Hebbian learning rules to store the patterns. You
can store up to N patterns that differ by more than one bit
in a net of N Hopfield units.