Journal Club

The SNAIL Journal Clubs are held bi-weekly during which we review papers that span a variety of topics in neuroscience, AI, and NeuroAI. On this page, you'll find summaries of the papers that have been reviewed in our journal clubs.

August 18th, 2023

Title: The neuroconnectionist research programme

Authors: Adrien Doerig, Rowan P. Sommers, Katja Seeliger, Blake Richards, Grace W. Lindsay, Konrad P. Kording, Talia Konkle,  Marcel A. J. van Gerven & Tim C. Kietzmann

Journal / Conference: Nature Reviews Neuroscience

Background and Objectives: 

The paper introduces neuroconnectionism as a progressive, novel research programme that uses artificial neural networks and deep learning to model cognitive processes. The authors propose neuroconnectionism as a Lakatosian philosophy, with several core assumptions and many auxiliary (“belt”) research directions and experiments.

Key Ideas:

The main core of the neuroconnectionist research programme consists of two assumptions:


Neuroconnectionism has become a central model for cognitive neuroscience, as it is able to model complex cognitive tasks from the behavioural level to the neuronal level. ANNs are able to carry out these complex tasks while remaining interpretable and biologically relevant, which allows scientists to create better models of the brain and gain a deeper understanding of cognitive processes. Future directions for neuroconnectionism include developing multitask networks, conducting more experiments in naturalistic experimental conditions, and further modelling of adaptive cognitive development.

August 4th, 2023

Title: Self-supervised video pretraining yields human-aligned visual representations

Authors: Nikhil Parthasarathy, S. M. Ali Eslami, João Carreira, Olivier J. Hénaff

Journal / Conference: Preprint

Background and Objectives: 

The paper explores the question of how a neural network can learn to represent objects in a self-supervised manner, aligning more closely with human perception (for a definition of what is meant by alignment see the Methods section below). The authors propose that by employing self-supervised contrastive learning on video data, they can achieve more human-like object perception than was possible with previous pretraining methods that used static images.


Dataset: "[...] we hypothesized that collecting a minimally-curated video dataset matched to the rough properties of ImageNet would be beneficial for learning a more general visual model from videos." To validate this hypothesis, the authors developed a data curation pipeline (VideoNet) to selectively filter online videos. The aim was to obtain video training data that more accurately reflects the distribution of categories found in ImageNet.

Self-supervised contrastive learning: Similar to other contrastive SSL algorithms, the definition of positive and negative pairs is crucial. Positive pairs were identified by sampling from 2.56-second video clips within VideoNet, while negative frame pairs were selected from different clips. Along with the inherent temporal augmentations happening by sampling video clips, the authors implemented a series of standard augmentations. Additionally, they introduced an innovative multi-scale contrastive attention pooling method for aggregating positive/negative pairs across different views.

Key Results:


Robustness to distributional shifts, the ability to generalize to new tasks, and alignment with human cognition (as gauged by attention map alignments and shape bias) may all be attainable through the appropriate pre-training paradigm. This paper posits that both the natural temporal augmentations found within videos and the specific content of the training data play significant roles in achieving these attributes.