40 lines
1.3 KiB
Markdown
40 lines
1.3 KiB
Markdown
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- Only single output neuron fires
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1. Set of homogeneous neurons with some randomly distributed synaptic weights
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- Respond differently to given set of input patterns
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2. Limit imposed on strength of each neuron
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3. Mechanism to allow neurons to compete for right to respond to a given subset of inputs
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- Only one output neuron active at a time
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- Or only one neuron per group
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- ***Winner-takes-all neuron***
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- Lateral inhibition
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- Neurons inhibit other neurons
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- Winning neuron must have highest induced local field for given input pattern
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- Winning neuron is squashed to 1
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- Others are clamped to 0
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$$y_k=
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\begin{cases}
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1 & \text{if } v_k > v_j \text{ for all } j,j\neq k \\
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0 & \text{otherwise}
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\end{cases}
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$$
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- Neuron has fixed amount of weight spread amongst input synapses
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- Sums to 1
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- Learn by shifting weights from inactive to active input nodes
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- Each input node relinquishes some proportion of weight
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- Distributed amongst active nodes
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$$\Delta w_{kj}=
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\begin{cases}
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\eta(x_j-w_{kj}) & \text{if neuron $k$ wins the competition}\\
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0 & \text{if neuron $k$ loses the competition}
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\end{cases}$$
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- Individual neurons learn to specialise on ensembles of similar patterns
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- Feature detectors
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