# Suggested further readings 

## Introduction

* [Catalyzing next-generation Artificial Intelligence through NeuroAI (2023)](https://www.nature.com/articles/s41467-023-37180-x)
* [The neuroconnectionist research programme (2023)](https://www.nature.com/articles/s41583-023-00705-w)
* [A deep learning framework for neuroscience (2019)](https://www.nature.com/articles/s41593-019-0520-2)
* [Neuroscience-Inspired Artificial Intelligence (2017)](https://pubmed.ncbi.nlm.nih.gov/28728020/)
* [Out of distribution generalization in machine learning (2021)](https://arxiv.org/abs/2103.02667)
* [Universal Intelligence: A Definition of Machine Intelligence (2007)](https://arxiv.org/abs/0712.3329)
* [Emergent behaviour and neural dynamics in artificial agents tracking odour plumes (2023)](https://www.nature.com/articles/s42256-022-00599-w)

## Tutorial 1

* [Universal Language Model Fine-tuning for Text Classification (2018)](https://arxiv.org/abs/1801.06146)
* [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models (2021)](https://arxiv.org/abs/2109.10282)

## Tutorial 2

* [A neural network that finds a naturalistic solution for the production of muscle activity (2015)](https://www.nature.com/articles/nn.4042)
* [MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks (2024)](https://elifesciences.org/reviewed-preprints/88591v2)
* [μSim: A goal-driven framework for elucidating the neural control of movement through musculoskeletal modeling (2024)](https://www.biorxiv.org/content/10.1101/2024.02.02.578628v2.abstract)

## Tutorial 3

* [Human-level concept learning through probabilistic program induction (2015)](https://www.science.org/doi/abs/10.1126/science.aab3050)
* [Learning Task-General Representations with Generative Neuro-Symbolic Modeling (2020)](https://arxiv.org/abs/2006.14448)