andy
1513f2b378
Affected files: STEM/AI/Classification/Classification.md STEM/AI/Classification/Decision Trees.md STEM/AI/Classification/Gradient Boosting Machine.md STEM/AI/Classification/Logistic Regression.md STEM/AI/Classification/Random Forest.md STEM/AI/Classification/Supervised.md STEM/AI/Classification/Supervised/README.md STEM/AI/Classification/Supervised/SVM.md STEM/AI/Classification/Supervised/Supervised.md STEM/AI/Learning.md STEM/AI/Neural Networks/Learning/Boltzmann.md STEM/AI/Neural Networks/Learning/Competitive Learning.md STEM/AI/Neural Networks/Learning/Credit-Assignment Problem.md STEM/AI/Neural Networks/Learning/Hebbian.md STEM/AI/Neural Networks/Learning/Learning.md STEM/AI/Neural Networks/Learning/README.md STEM/AI/Neural Networks/RNN/Autoencoder.md STEM/AI/Neural Networks/RNN/Deep Image Prior.md STEM/AI/Neural Networks/RNN/MoCo.md STEM/AI/Neural Networks/RNN/Representation Learning.md STEM/AI/Neural Networks/RNN/SimCLR.md STEM/img/comp-learning.png STEM/img/competitive-geometric.png STEM/img/confusion-matrix.png STEM/img/decision-tree.png STEM/img/deep-image-prior-arch.png STEM/img/deep-image-prior-results.png STEM/img/hebb-learning.png STEM/img/moco.png STEM/img/receiver-operator-curve.png STEM/img/reinforcement-learning.png STEM/img/rnn+autoencoder-variational.png STEM/img/rnn+autoencoder.png STEM/img/simclr.png STEM/img/sup-representation-learning.png STEM/img/svm-c.png STEM/img/svm-non-linear-project.png STEM/img/svm-non-linear-separated.png STEM/img/svm-non-linear.png STEM/img/svm-optimal-plane.png STEM/img/svm.png STEM/img/unsup-representation-learning.png
40 lines
1.3 KiB
Markdown
40 lines
1.3 KiB
Markdown
- Only single output neuron fires
|
|
|
|
1. Set of homogeneous neurons with some randomly distributed synaptic weights
|
|
- Respond differently to given set of input patterns
|
|
2. Limit imposed on strength of each neuron
|
|
3. Mechanism to allow neurons to compete for right to respond to a given subset of inputs
|
|
- Only one output neuron active at a time
|
|
- Or only one neuron per group
|
|
- ***Winner-takes-all neuron***
|
|
|
|
|
|
|
|
- Lateral inhibition
|
|
- Neurons inhibit other neurons
|
|
- Winning neuron must have highest induced local field for given input pattern
|
|
- Winning neuron is squashed to 1
|
|
- Others are clamped to 0
|
|
|
|
$$y_k=
|
|
\begin{cases}
|
|
1 & \text{if } v_k > v_j \text{ for all } j,j\neq k \\
|
|
0 & \text{otherwise}
|
|
\end{cases}
|
|
$$
|
|
|
|
- Neuron has fixed amount of weight spread amongst input synapses
|
|
- Sums to 1
|
|
- Learn by shifting weights from inactive to active input nodes
|
|
- Each input node relinquishes some proportion of weight
|
|
- Distributed amongst active nodes
|
|
|
|
$$\Delta w_{kj}=
|
|
\begin{cases}
|
|
\eta(x_j-w_{kj}) & \text{if neuron $k$ wins the competition}\\
|
|
0 & \text{if neuron $k$ loses the competition}
|
|
\end{cases}$$
|
|
|
|
- Individual neurons learn to specialise on ensembles of similar patterns
|
|
- Feature detectors
|
|
 |