14 papers · ranked by Valyu relevance
Chloe A. Game, Nils Piechaud, Kerry L. Howell
Deep learning (DL) is a powerful tool to extract ecological information from large image datasets efficiently and consistently. However, applying these methods remains challenging, due in part to the complexity of DL workflows and the dynamic nature of available tools. To address this, we created a practical guide and…
Omveer Sharma, Keren Weidenfeld, Dalit Barkan, Oren Gal
Breast cancer cells that disseminate to distant organs can remain dormant (non-proliferative) for years before reactivating and progressing into lethal metastatic disease. Understanding the transition between dormancy and reactivation is therefore critical for early intervention and treatment. In this study, we…
M.A. Lieftinck, T. Verlaan, M.J.T. Reinders
Deep Neural Networks (DNNs) are renowned for their high accuracy and versatility, which has led to their application in many fields of research, including biology. However, this accuracy often comes at the expense of interpretability, making it challenging to reason about the inner workings of most DNNs. Particularly…
Mo Zhou, Emily Schwartz, Arish Alreja, R. Mark Richardson + 2 more
Deep neural networks have shown high accuracy in modeling neural responses in the visual system, but most models rely on supervised learning, which requires training on ground-truth labels that are typically unavailable in real-world settings. While unsupervised models can address this limitation, they miss another key…
Matteo De Matola, Giorgio Arcara
Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come…
David Martínez-Enguita, Thomas Hillerton, Julia Åkesson, Rebecka Jörnsten + 1 more
Deep learning models routinely compress omics into low-dimensional codes, yet many equally accurate embeddings fail to reflect how cells are wired, which limits explanation and causal reasoning. We present a simple, architecture-agnostic approach to make latent spaces biologically legible: a protein-protein interaction…
María Peña Fernández, Lara Lloret Iglesias, Jesús Marco de Lucas
One of the most compelling ideas for bridging neuroscience and artificial neural networks is the establishment of a framework based on three main components: network architecture, optimization mechanism, and loss (or objective) function to be minimized. While the first two components have been extensively explored, the…
Bruno J. Zorzet, Victoria Peterson, Diego H. Milone, Rodrigo Echeveste
Motor imagery (MI) brain-computer interfaces (BCIs) are promising technologies for neurorehabilitation. In this context, deep learning (DL) models are increasingly being used to decode the mental imagination of movement. However, countless studies across multiple domains have shown that DL models are susceptible to…
Ashena Gorgan Mohammadi, Manu Srinath Halvagal, Friedemann Zenke
Tracking prey or recognizing a lurking predator is as crucial for survival as anticipating their actions. To guide behavior, the brain must extract information about object identities and their dynamics from entangled sensory inputs. How it accomplishes this feat remains an open question. Predictive coding theories…
Daniel L. Kimmel, Kimberly L. Stachenfeld, Nikolaus Kriegeskorte, Stefano Fusi + 2 more
Abstraction and generalization are essential for flexible decision-making in novel situations. Recent work in humans and monkeys has shown how abstract variables are encoded by the representational geometry of neural population activity. However, these observations—which are typically made after learning has…
Matteo Farina, Pietro Zamberlan, Arno Onken, Ulisse Ferrari
For datasets with thousands of neurons and images, vision transformers have proven successful at predicting neural responses to stimuli. However, they are expected to underperform in low-data regimes, where CNNs and Gaussian processes are considered more effective. We ask whether transformers can be made competitive…
Megumi Yoshihara, Takuya Isomura
Active inference has been proposed as a unified explanation of perception and action. Previous work has shown that canonical neural networks that minimize shared Helmholtz energy are cast as performing active inference of the external environment. However, how animals flexibly adapt to newly encountered environments…
Théo Desbordes, Itsaso Olasagasti, Nicolas Piron, Sophie Schwartz + 1 more
Multivariate decoding analyses have become a cornerstone method in cognitive neuroscience. When applied to time-resolved brain imaging signals, they provide insights into the temporal dynamics of information processing in the brain. In particular, the temporal generalization (TG) method—where a decoder trained at one…
Casper Kerrén, Stephanie Theves, Mikael Johansson, Peter Gärdenfors + 1 more
Flexible decision-making in uncertain environments requires inferring latent structure and selecting behaviourally relevant information. Here, we tested the hypothesis that internal models support this process by compressing high-dimensional input into lower-dimensional, goal-relevant subspaces. Human participants…