Kalle Kossio et al., 2021 - Google Patents
Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensationKalle Kossio et al., 2021
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- 3843061240957376997
- Author
- Kalle Kossio Y
- Goedeke S
- Klos C
- Memmesheimer R
- Publication year
- Publication venue
- Proceedings of the National Academy of Sciences
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Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless, behaviors and memories often persist over long times. In a standard model, associative memories are represented by assemblies of strongly …
- 210000002569 neurons 0 title abstract description 279
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- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
- G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
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- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
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- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
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- G06—COMPUTING; CALCULATING; COUNTING
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