55 lines
2.1 KiB
Markdown
55 lines
2.1 KiB
Markdown
|
*Time-dependent, highly local, strongly interactive*
|
|||
|
|
|
|||
|
|
- Oldest learning algorithm
|
|||
|
|
- Increases synaptic efficiency as a function of the correlation between presynaptic and postsynaptic activities
|
|||
|
|
|
|||
|
|
1. If two neurons on either side of a synapse are activated simultaneously/synchronously, then the strength of that synapse is selectively increased
|
|||
|
|
2. If two neurons on either side of a synapse are activated asynchronously, then that synapse is selectively weakened or eliminated
|
|||
|
|
|
|||
|
|
- Hebbian synapse
|
|||
|
|
- Time-dependent
|
|||
|
|
- Depends on times of pre/post-synaptic signals
|
|||
|
|
- Local
|
|||
|
|
- Interactive
|
|||
|
|
- Depends on both sides of synapse
|
|||
|
|
- True interaction between pre/post-synaptic signals
|
|||
|
|
- Cannot make prediction from either one by itself
|
|||
|
|
- Conjunctional or correlational
|
|||
|
|
- Based on conjunction of pre/post-synaptic signals
|
|||
|
|
- Conjunctional synapse
|
|||
|
|
- Modification classifications
|
|||
|
|
- Hebbian
|
|||
|
|
- **Increases** strength with **positively** correlated pre/post-synaptic signals
|
|||
|
|
- **Decreases** strength with **negatively** correlated pre/post-synaptic signals
|
|||
|
|
- Anti-Hebbian
|
|||
|
|
- **Decreases** strength with **positively** correlated pre/post-synaptic signals
|
|||
|
|
- **Increases** strength with **negatively** correlated pre/post-synaptic signals
|
|||
|
|
- Still Hebbian in nature, not in function
|
|||
|
|
- Non-Hebbian
|
|||
|
|
- Doesn't involve above correlations/time dependence etc
|
|||
|
|
|
|||
|
|
# Mathematically
|
|||
|
|
$$\Delta w_{kj}(n)=F\left(y_k(n),x_j(n)\right)$$
|
|||
|
|
- Generally
|
|||
|
|
- All Hebbian
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
## Hebb's Hypothesis
|
|||
|
|
$$\Delta w_{kj}(n)=\eta y_k(n)x_j(n)$$
|
|||
|
|
- Activity product rule
|
|||
|
|
- Exponential growth until saturation
|
|||
|
|
- No information stored
|
|||
|
|
- Selectivity lost
|
|||
|
|
|
|||
|
|
## Covariance Hypothesis
|
|||
|
|
$$\Delta w_{kj}(n)=\eta(x_j-\bar x)(y_k-\bar y)$$
|
|||
|
|
- Characterised by perturbation from of pre/post-synaptic signals from their mean over a given time interval
|
|||
|
|
- Average $x$ and $y$ constitute thresholds
|
|||
|
|
- Intercept at y = y bar
|
|||
|
|
- Similar to learning in the hippocampus
|
|||
|
|
|
|||
|
|
*Allows:*
|
|||
|
|
1. Convergence to non-trivial state
|
|||
|
|
- When x = x bar or y = y bar
|
|||
|
|
2. Prediction of both synaptic potentiation and synaptic depression
|