# Cognitive computational neuroscience

arXiv: https://arxiv.org/abs/1807.11819 · HTML: https://mlsanity.com/paper/1807.11819/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2018.

## Built on by (most cited first, 30 of 43)

- [Neither hype nor gloom do DNNs justice](https://mlsanity.com/paper/2312.05355/) (2023)
- [The Topology and Geometry of Neural Representations](https://mlsanity.com/paper/2309.11028/) (2023)
- [Visual representations in the human brain are aligned with large language models](https://mlsanity.com/paper/2209.11737/) (2022)
- [Neural Network based Successor Representations of Space and Language](https://mlsanity.com/paper/2202.11190/) (2022)
- [Word class representations spontaneously emerge in a deep neural network trained on next word prediction](https://mlsanity.com/paper/2302.07588/) (2023)
- [The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding](https://mlsanity.com/paper/2109.13392/) (2021)
- [Neural Network based Formation of Cognitive Maps of Semantic Spaces and the Emergence of Abstract Concepts](https://mlsanity.com/paper/2210.16062/) (2022)
- [VLRS-Bench: A Vision-Language Reasoning Benchmark for Remote Sensing](https://mlsanity.com/paper/2602.07045/) (2026)
- [The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain](https://mlsanity.com/paper/2103.06123/) (2021)
- [Complex Recurrent Spectral Network](https://mlsanity.com/paper/2312.07296/) (2023)
- [AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature Space](https://mlsanity.com/paper/2406.18344/) (2024)
- [A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots](https://mlsanity.com/paper/2103.08183/) (2021)
- [Controlled-rearing studies of newborn chicks and deep neural networks](https://mlsanity.com/paper/2112.06106/) (2021)
- [Computational Inference in Cognitive Science: Operational, Societal and Ethical Considerations](https://mlsanity.com/paper/2210.13526/) (2022)
- [Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments](https://mlsanity.com/paper/2211.10515/) (2022)
- [Computational Models to Study Language Processing in the Human Brain: A Survey](https://mlsanity.com/paper/2403.13368/) (2024)
- [LaVCa: LLM-assisted Visual Cortex Captioning](https://mlsanity.com/paper/2502.13606/) (2025)
- [Linguistics and Human Brain: A Perspective of Computational Neuroscience](https://mlsanity.com/paper/2602.08275/) (2026)
- [DreamCatcher: Revealing the Language of the Brain with fMRI using GPT Embedding](https://mlsanity.com/paper/2306.10082/) (2023)
- [Analysis of Argument Structure Constructions in a Deep Recurrent Language Model](https://mlsanity.com/paper/2408.03062/) (2024)
- [Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents](https://mlsanity.com/paper/2109.06580/) (2021)
- [Learning in Wilson-Cowan model for metapopulation](https://mlsanity.com/paper/2406.16453/) (2024)
- [Development of collective behavior in newborn artificial agents](https://mlsanity.com/paper/2111.03796/) (2021)
- [Beyond the limitations of any imaginable mechanism: large language models and psycholinguistics](https://mlsanity.com/paper/2303.00077/) (2023)
- [Flexible and Inherently Comprehensible Knowledge Representation for Data-Efficient Learning and Trustworthy Human-Machine Teaming in Manufacturing Environments](https://mlsanity.com/paper/2305.11597/) (2023)
- [Life-inspired Interoceptive Artificial Intelligence for Autonomous and Adaptive Agents](https://mlsanity.com/paper/2309.05999/) (2023)
- [Beyond Labels: Advancing Cluster Analysis with the Entropy of Distance Distribution (EDD)](https://mlsanity.com/paper/2311.16621/) (2023)
- [Brain-like emergent properties in deep networks: impact of network architecture, datasets and training](https://mlsanity.com/paper/2411.16326/) (2024)
- [BI-RADS prediction of mammographic masses using uncertainty information extracted from a Bayesian Deep Learning model](https://mlsanity.com/paper/2503.13999/) (2025)
- [Image-to-Brain Signal Generation for Visual Prosthesis with CLIP Guided Multimodal Diffusion Models](https://mlsanity.com/paper/2509.00787/) (2025)

## Abstract (arXiv)

To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive science has developed computational models of human cognition, decomposing task performance into computational components. However, its algorithms still fall short of human intelligence and are not grounded in neurobiology. Computational neuroscience has investigated how interacting neurons can implement component functions of brain computation. However, it has yet to explain how those components interact to explain human cognition and behavior. Modern technologies enable us to measure and manipulate brain activity in unprecedentedly rich ways in animals and humans. However, experiments will yield theoretical insight only when employed to test brain-computational models. It is time to assemble the pieces of the puzzle of brain computation. Here we review recent work in the intersection of cognitive science, computational neuroscience, and artificial intelligence. Computational models that mimic brain information processing during perceptual, cognitive, and control tasks are beginning to be developed and tested with brain and behavioral data.