# The neuroconnectionist research programme

arXiv: https://arxiv.org/abs/2209.03718 · HTML: https://mlsanity.com/paper/2209.03718/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2022.

## Built on by (most cited first, 30 of 48)

- [Getting aligned on representational alignment](https://mlsanity.com/paper/2310.13018/) (2023)
- [Continual Learning and Catastrophic Forgetting](https://mlsanity.com/paper/2403.05175/) (2024)
- [Neither hype nor gloom do DNNs justice](https://mlsanity.com/paper/2312.05355/) (2023)
- [Brain-inspired learning in artificial neural networks: a review](https://mlsanity.com/paper/2305.11252/) (2023)
- [Dimensions underlying the representational alignment of deep neural networks with humans](https://mlsanity.com/paper/2406.19087/) (2024)
- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)
- [The feasibility of artificial consciousness through the lens of neuroscience](https://mlsanity.com/paper/2306.00915/) (2023)
- [Visual representations in the human brain are aligned with large language models](https://mlsanity.com/paper/2209.11737/) (2022)
- [Universal dimensions of visual representation](https://mlsanity.com/paper/2408.12804/) (2024)
- [Poisson Variational Autoencoder](https://mlsanity.com/paper/2405.14473/) (2024)
- [Better artificial intelligence does not mean better models of biology](https://mlsanity.com/paper/2504.16940/) (2025)
- [End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions](https://mlsanity.com/paper/2308.09431/) (2023)
- [Are Vision Transformers More Data Hungry Than Newborn Visual Systems?](https://mlsanity.com/paper/2312.02843/) (2023)
- [Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion Inference](https://mlsanity.com/paper/2302.09582/) (2023)
- [Approaching human 3D shape perception with neurally mappable models](https://mlsanity.com/paper/2308.11300/) (2023)
- [Adapting to time: Why nature may have evolved a diverse set of neurons](https://mlsanity.com/paper/2404.14325/) (2024)
- [Philosophy of Cognitive Science in the Age of Deep Learning](https://mlsanity.com/paper/2405.04048/) (2024)
- [Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli](https://mlsanity.com/paper/2411.01505/) (2024)
- [Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)](https://mlsanity.com/paper/2507.17897/) (2025)
- [Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes](https://mlsanity.com/paper/2306.12045/) (2023)
- [Achieving More Human Brain-Like Vision via Human EEG Representational Alignment](https://mlsanity.com/paper/2401.17231/) (2024)
- [Superposition disentanglement of neural representations reveals hidden alignment](https://mlsanity.com/paper/2510.03186/) (2025)
- [A mathematical theory for understanding when abstract representations emerge in neural networks](https://mlsanity.com/paper/2510.09816/) (2025)
- [Unveiling the Tapestry: the Interplay of Generalization and Forgetting in Continual Learning](https://mlsanity.com/paper/2211.11174/) (2022)
- [Characterising representation dynamics in recurrent neural networks for object recognition](https://mlsanity.com/paper/2308.12435/) (2023)
- [Inducing Human-like Biases in Moral Reasoning Language Models](https://mlsanity.com/paper/2411.15386/) (2024)
- [Shared representations in brains and models reveal a two-route cortical organization during scene perception](https://mlsanity.com/paper/2507.13941/) (2025)
- [Exploiting heterogeneous delays for efficient computation in low-bit neural networks](https://mlsanity.com/paper/2510.27434/) (2025)
- [MIMIC-MJX: Neuromechanical Emulation of Animal Behavior](https://mlsanity.com/paper/2511.20532/) (2025)
- [Characterizing Universal Object Representations Across Vision Models](https://mlsanity.com/paper/2605.13675/) (2026)

## Abstract (arXiv)

Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism. ANNs have been lauded as the current best models of information processing in the brain, but also criticized for failing to account for basic cognitive functions. We propose that arguing about the successes and failures of a restricted set of current ANNs is the wrong approach to assess the promise of neuroconnectionism. Instead, we take inspiration from the philosophy of science, and in particular from Lakatos, who showed that the core of scientific research programmes is often not directly falsifiable, but should be assessed by its capacity to generate novel insights. Following this view, we present neuroconnectionism as a cohesive large-scale research programme centered around ANNs as a computational language for expressing falsifiable theories about brain computation. We describe the core of the programme, the underlying computational framework and its tools for testing specific neuroscientific hypotheses. Taking a longitudinal view, we review past and present neuroconnectionist projects and their responses to challenges, and argue that the research programme is highly progressive, generating new and otherwise unreachable insights into the workings of the brain.