| Author/Title |
Research Type |
Related Fields |
| Liu, Y.H., Smith, S., Mihalas S., Shea-Brown E., Sumbul U., “Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators”, Advances in Neural Information Processing Systems, 2022. |
Publications, Articles |
Applied Mathematics, Computer Science, Machine Learning, Mathematical Neuroscience |
| Liu, Y.H., Ghosh A., Richards B. A., Shea-Brown E., Lajoie G., “Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules”, Advances in Neural Information Processing Systems, 2022. |
Publications, Articles |
Applied Linear Algebra, Applied Mathematics, Computer Science, Dynamical Systems, Machine Learning, Mathematical Neuroscience |
| Liu, Y.H., Smith, S.J., Mihalas S., Shea-Brown E., Sumbul U., “Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network”, Proceedings of the National Academy of Sciences, 2021. |
Publications, Articles |
Computer Science, Machine Learning, Mathematical Neuroscience |
| Liu, Y.H., Smith, S.J., Mihalas S., Shea-Brown E., Sumbul U., “A solution to temporal credit assignment using cell-type-specific modulatory signals”, bioRxiv, 2020. |
Publications, Articles |
Machine Learning, Mathematical Neuroscience |
| Liu, Hexuan, Jimin Kim, and Eli Shlizerman. "Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans." Philosophical Transactions of the Royal Society B: Biological Sciences 373.1758 (2018): 20170377. |
Publications, Articles |
Mathematical Neuroscience |
| Liu, Y.H., Baratin A., Cornford J., Mihalas S., Shea-Brown E., Lajoie G., “How connectivity structure shapes rich and lazy learning in neural circuits”, International Conference on Learning Representations (ICLR), 2024 (accepted). |
Publications, Articles |
Applied Mathematics, Computer Science, Machine Learning, Mathematical Neuroscience |
