Computational neuroscience and artificial neural network machine learning represent distinct fields with minimal theoretical overlap

W. Soo, V. Goudar, Xiao-Jing Wang. Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies. 2023. https://doi.org/10.1101/2023.10.10.561588

This study bridges computational neuroscience and recurrent neural network training to model biological cognition and memory.

Arthur Juliani, Samuel A. Barnett, Brandon Davis, M. Sereno, Ida Momennejad. Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning. 2022. https://doi.org/10.48550/arXiv.2206.03312

Neuro-Nav bridges reinforcement learning from artificial intelligence with canonical behavioral and neural models in cognitive science.

Chengting Yu, Yang-Guang Du, Mufeng Chen, Aili Wang, Gaoang Wang, Erping Li. MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks. 2022. https://doi.org/10.3389/fnins.2022.945037

Research on spiking neural networks explicitly incorporates biological properties like synaptic plasticity into machine learning frameworks.

D. Pham, M. Titkanlou, Roman Mouček. A hybrid Spiking Neural Network–Transformer architecture for motor imagery and sleep apnea detection. 2025. https://doi.org/10.3389/fnins.2025.1716204

Combining spiking neural networks and transformers integrates biologically plausible temporal processing with machine learning models.

Thomas Miconi. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks. 2016. https://doi.org/10.1101/057729

Recurrent neural networks are shown to successfully replicate complex cortical dynamics observed in animal brains during cognitive tasks.

David Rotermund, Klaus R. Pawelzik. Biologically plausible learning in a deep recurrent spiking network. 2019. https://doi.org/10.1101/613471

This work develops biologically plausible deep spiking networks that approach the performance of standard artificial neural networks.

Hareebin Yuttachai, Billel Arbaoui, Yusraw O-manee. Indirect feedback alignment in deep learning for cognitive agent modeling: enhancing self-confidence analytics in the workplace. 2024. https://doi.org/10.11591/ijece.v14i6.pp6699-6710

Indirect feedback alignment integrates biological plausibility into deep learning credit assignment for cognitive modeling.

Anirudh Apparaju, Ognjen Arandjelović. Towards New Generation, Biologically Plausible Deep Neural Network Learning. 2022. https://doi.org/10.3390/sci4040046

Studies on biologically plausible deep neural network learning directly compare artificial learning rules with biological mechanisms.

Motaghian F, Nazari S, Dominguez-Morales JP, Jafari R. Topology-aware design of spiking neural networks via modular graph architectures.. 2026. https://doi.org/10.1371/journal.pone.0344997

Spiking neural networks combine biologically plausible architectures with artificial neural network optimization for improved performance.