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Brain-and-Cognition-Papers-with-Code

✨ Papers, Code, Datasets for Neuroscience and Cognition Science

This repository contains a reading list of papers with code on Neuroscience and Cognition Science. Note that most of the papers are related to machine learning, transfer learning, or meta-learning.
In addition, I will separately list papers from important conferences starting from 2023, e.g., NIPS, ICML, ICLR, CVPR etc. This repository is still being continuously improved. If you have found any relevant papers that need to be included in this repository, please feel free to submit a pull request (PR) or open an issue.
means that the corresponding papers are the books about Neuroscience and Cognition Science.
All the papers are provided in './Papers' folder

1 Brain-inspired Paper

[1] Ben-Iwhiwhu, E., Dick, J., Ketz, N. A., Pilly, P. K., & Soltoggio, A. (2022). Context meta-reinforcement learning via neuromodulation. Neural Networks, 152, 70-79.

[2] Robertazzi, F., Vissani, M., Schillaci, G., & Falotico, E. (2022). Brain-inspired meta-reinforcement learning cognitive control in conflictual inhibition decision-making task for artificial agents. Neural Networks, 154, 283-302.

[3] Grossman, C. D., & Cohen, J. Y. (2022). Neuromodulation and neurophysiology on the timescale of learning and decision-making. Annual review of neuroscience, 45, 317-337.

[4] Dorrell, W., Yuffa, M., & Latham, P. E. (2023, July). Meta-learning the inductive bias of simple neural circuits. In International Conference on Machine Learning (pp. 8389-8402). PMLR.

[5] Schmidgall, S., & Hays, J. (2022). Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks. arXiv preprint arXiv:2206.12520.

[6] Lechner, M., Hasani, R., Amini, A., Henzinger, T. A., Rus, D., & Grosu, R. (2020). Neural circuit policies enabling auditable autonomy. Nature Machine Intelligence, 2(10), 642-652.

[7] Najarro, E., & Risi, S. (2020). Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems, 33, 20719-20731.

[8] Wilson, D. G., Cussat-Blanc, S., Luga, H., & Harrington, K. (2018). Neuromodulated Learning in Deep Neural Networks. arXiv preprint arXiv:1812.03365.

[9] Vecoven, N., Ernst, D., Wehenkel, A., & Drion, G. (2020). Introducing neuromodulation in deep neural networks to learn adaptive behaviours. PloS one, 15(1), e0227922.

[10] Ben-Iwhiwhu, E. (2023). Neuromodulated networks for lifelong learning and adaptation (Doctoral dissertation, Loughborough University).

[11] Tang, Y., Zhang, C., Xu, H., Chen, S., Cheng, J., Leng, L., ... & He, Z. (2023). Neuro-modulated hebbian learning for fully test-time adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3728-3738).

[12] Zhang, T., Cheng, X., Jia, S., Li, C. T., Poo, M. M., & Xu, B. (2023). A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost. Science Advances, 9(34), eadi2947.

[13] Palm, R. B., Najarro, E., & Risi, S. (2021, July). Testing the genomic bottleneck hypothesis in Hebbian meta-learning. In NeurIPS 2020 Workshop on Pre-registration in Machine Learning (pp. 100-110). PMLR.

[14] Vafaii, H., Yates, J., & Butts, D. (2024). Hierarchical VAEs provide a normative account of motion processing in the primate brain. Advances in Neural Information Processing Systems, 36.

[15] Chen, Z., Qing, J., & Zhou, J. H. (2024). Cinematic mindscapes: High-quality video reconstruction from brain activity. Advances in Neural Information Processing Systems, 36.

[16] Sarch, G. H., Tarr, M. J., Fragkiadaki, K., & Wehbe, L. (2023). Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images. bioRxiv, 2023-05.

[17] Yuan, Z., Zhang, D., Yang, Y., Chen, J., & Li, Y. (2024). PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection. Advances in Neural Information Processing Systems, 36.

[18] Choi, M., Han, K., Wang, X., Zhang, Y., & Liu, Z. (2024). A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains. Advances in Neural Information Processing Systems, 36.

[19] Tang, J., Du, M., Vo, V., Lal, V., & Huth, A. (2024). Brain encoding models based on multimodal transformers can transfer across language and vision. Advances in Neural Information Processing Systems, 36.

[20] Ye, H., Zheng, Y., Li, Y., Zhang, K., Kong, Y., & Yuan, Y. (2024). RH-BrainFS: Regional Heterogeneous Multimodal Brain Networks Fusion Strategy. Advances in Neural Information Processing Systems, 36.

[21] Li, T., Wen, Z., Li, Y., & Lee, T. S. (2024). Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity. Advances in Neural Information Processing Systems, 36.

[22] Gokcen, E., Jasper, A., Xu, A., Kohn, A., Machens, C. K., & Yu, B. M. (2024). Uncovering motifs of concurrent signaling across multiple neuronal populations. Advances in Neural Information Processing Systems, 36.

[23] Lin, X., Li, L., Shi, B., Huang, T., Mi, Y., & Wu, S. (2024). Slow and Weak Attractor Computation Embedded in Fast and Strong EI Balanced Neural Dynamics. Advances in Neural Information Processing Systems, 36.

[24] Zhang, Y., He, T., Boussard, J., Windolf, C., Winter, O., Trautmann, E., ... & Paninski, L. (2024). Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes. Advances in Neural Information Processing Systems, 36.

[25] Sun, Z., & Mu, Y. (2024). Rewiring Neurons in Non-Stationary Environments. Advances in Neural Information Processing Systems, 36.

[26] Celotto, M., Bím, J., Tlaie, A., De Feo, V., Toso, A., Lemke, S., ... & Panzeri, S. (2024). An information-theoretic quantification of the content of communication between brain regions. Advances in Neural Information Processing Systems, 36.

[27] Ye, J., Collinger, J., Wehbe, L., & Gaunt, R. (2024). Neural data transformer 2: multi-context pretraining for neural spiking activity. Advances in Neural Information Processing Systems, 36.

2. Human-like Paper

[1] Toosi, T., & Issa, E. (2024). Brain-like Flexible Visual Inference by Harnessing Feedback Feedforward Alignment. Advances in Neural Information Processing Systems, 36.

Specific Conference

NIPS23

[1] Ellis, K. (2024). Human-like few-shot learning via bayesian reasoning over natural language. Advances in Neural Information Processing Systems, 36.

[2] Chen, Z., Qing, J., & Zhou, J. H. (2024). Cinematic mindscapes: High-quality video reconstruction from brain activity. Advances in Neural Information Processing Systems, 36.

[3] Li, T., Wen, Z., Li, Y., & Lee, T. S. (2024). Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity. Advances in Neural Information Processing Systems, 36.

[4] Toosi, T., & Issa, E. (2024). Brain-like Flexible Visual Inference by Harnessing Feedback Feedforward Alignment. Advances in Neural Information Processing Systems, 36.

[5] Vafaii, H., Yates, J., & Butts, D. (2024). Hierarchical VAEs provide a normative account of motion processing in the primate brain. Advances in Neural Information Processing Systems, 36.

[6] Sarch, G. H., Tarr, M. J., Fragkiadaki, K., & Wehbe, L. (2023). Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images. bioRxiv, 2023-05.

[7] Yuan, Z., Zhang, D., Yang, Y., Chen, J., & Li, Y. (2024). PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection. Advances in Neural Information Processing Systems, 36.

[8] Choi, M., Han, K., Wang, X., Zhang, Y., & Liu, Z. (2024). A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains. Advances in Neural Information Processing Systems, 36.

[9] Tang, J., Du, M., Vo, V., Lal, V., & Huth, A. (2024). Brain encoding models based on multimodal transformers can transfer across language and vision. Advances in Neural Information Processing Systems, 36.

[10] Ye, H., Zheng, Y., Li, Y., Zhang, K., Kong, Y., & Yuan, Y. (2024). RH-BrainFS: Regional Heterogeneous Multimodal Brain Networks Fusion Strategy. Advances in Neural Information Processing Systems, 36.

[11] Li, T., Wen, Z., Li, Y., & Lee, T. S. (2024). Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity. Advances in Neural Information Processing Systems, 36.

[12] Gokcen, E., Jasper, A., Xu, A., Kohn, A., Machens, C. K., & Yu, B. M. (2024). Uncovering motifs of concurrent signaling across multiple neuronal populations. Advances in Neural Information Processing Systems, 36.

[13] Lin, X., Li, L., Shi, B., Huang, T., Mi, Y., & Wu, S. (2024). Slow and Weak Attractor Computation Embedded in Fast and Strong EI Balanced Neural Dynamics. Advances in Neural Information Processing Systems, 36.

[14] Zhang, Y., He, T., Boussard, J., Windolf, C., Winter, O., Trautmann, E., ... & Paninski, L. (2024). Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes. Advances in Neural Information Processing Systems, 36.

[15] Sun, Z., & Mu, Y. (2024). Rewiring Neurons in Non-Stationary Environments. Advances in Neural Information Processing Systems, 36.

[16] Celotto, M., Bím, J., Tlaie, A., De Feo, V., Toso, A., Lemke, S., ... & Panzeri, S. (2024). An information-theoretic quantification of the content of communication between brain regions. Advances in Neural Information Processing Systems, 36.

[17] Ye, J., Collinger, J., Wehbe, L., & Gaunt, R. (2024). Neural data transformer 2: multi-context pretraining for neural spiking activity. Advances in Neural Information Processing Systems, 36.

ICML23