# Deep Learning for Cognitive Neuroscience

arXiv: https://arxiv.org/abs/1903.01458 · HTML: https://mlsanity.com/paper/1903.01458/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 7 of 7)

- [Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience](https://mlsanity.com/paper/2406.01352/) (2024)
- [A Rubric for Human-like Agents and NeuroAI](https://mlsanity.com/paper/2212.04401/) (2022)
- [Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements](https://mlsanity.com/paper/2206.10587/) (2022)
- [Active Inference Tree Search in Large POMDPs](https://mlsanity.com/paper/2103.13860/) (2021)
- [A brain basis of dynamical intelligence for AI and computational neuroscience](https://mlsanity.com/paper/2105.07284/) (2021)
- [Self-Emotion-Mediated Exploration in Artificial Intelligence Mirrors: Findings from Cognitive Psychology](https://mlsanity.com/paper/2302.06615/) (2023)
- [Acoustic characterization of speech rhythm: going beyond metrics with recurrent neural networks](https://mlsanity.com/paper/2401.14416/) (2024)

## Abstract (arXiv)

Neural network models can now recognise images, understand text, translate languages, and play many human games at human or superhuman levels. These systems are highly abstracted, but are inspired by biological brains and use only biologically plausible computations. In the coming years, neural networks are likely to become less reliant on learning from massive labelled datasets, and more robust and generalisable in their task performance. From their successes and failures, we can learn about the computational requirements of the different tasks at which brains excel. Deep learning also provides the tools for testing cognitive theories. In order to test a theory, we need to realise the proposed information-processing system at scale, so as to be able to assess its feasibility and emergent behaviours. Deep learning allows us to scale up from principles and circuit models to end-to-end trainable models capable of performing complex tasks. There are many levels at which cognitive neuroscientists can use deep learning in their work, from inspiring theories to serving as full computational models. Ongoing advances in deep learning bring us closer to understanding how cognition and perception may be implemented in the brain -- the grand challenge at the core of cognitive neuroscience.