Comments (3)
- Dellaferrera, G., Woźniak, S., Indiveri, G., Pantazi, A., & Eleftheriou, E. (2022). Introducing principles of synaptic integration in the optimization of deep neural networks. Nature Communications, 13(1), 1-14.
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- Perez-Nieves, N., Leung, V.C.H., Dragotti, P.L. et al. Neural heterogeneity promotes robust learning. Nat Commun 12, 5791 (2021). https://doi.org/10.1038/s41467-021-26022-3
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#232 Solved
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Related Issues (20)
- How to implement gradient accumulation in BrainPy HOT 5
- Using custom iterable or functional input in DSTrainer HOT 21
- Questions are the activation of neurons when input is current HOT 2
- Using `jax.disable_jit()` cannot disable jit for `bm.scan` HOT 3
- How to index and slice in the loss function during training HOT 8
- Convert a BrainPy model to process batched input by `jax.vmap` HOT 2
- Reopen issue #599 (memory issue) HOT 3
- Manually resetting BrainPy's delay variables cause JAX leaking error HOT 7
- Gradient accumulation generates JAX leaking error in BrainPy 2.5.0 HOT 3
- 2D bifurcation for 3+ D dynamical systems HOT 3
- Questions about backpropagation through delay variables HOT 6
- Monitor ion channel current with built-in models HOT 7
- Questions about writing custom gradient functions in BrainPy HOT 8
- multiple GPU devices simulation and training of one dynamic system in brainpy HOT 15
- How to view the computational graph in BrainPy HOT 1
- Question about customizing a synapse model HOT 5
- Strange behavior of bm.hessian() HOT 4
- bp.dnn.ToFlaxRNNCell is not working HOT 4
- Neural ODE
- csr and ij connection show unexpected different behavior(for csr connection, multiple connection is not regarded) HOT 1
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