22 papers · ranked by Valyu relevance
Subba Reddy Oota, Manish Gupta, Raju S. Bapi, Gaël Jobard + 2 more
'Frédéric Alexandre' 'Xavier Hinaut'] How does the brain represent different modes of information? Can we design a system that automatically understands what the user is thinking? Such questions can be answered by studying brain recordings like functional magnetic resonance imaging (fMRI). As a first step, the…
Jennifer Williams, Leila Wehbe
individual's brain Authors: ['Jennifer Williams' 'Leila Wehbe'] Similar to how differences in the proficiency of the cardiovascular and musculoskeletal system predict an individual's athletic ability, differences in how the same brain region encodes information across individuals may explain their behavior. However…
S Yogesh Kumaran, Arvindh Arun, Jerrin John
—Language models have been shown to be rich enough to encode fMRI activations of certain Regions of Interest in our Brains. Previous works have explored transfer learning from representations learned for popular natural language processing tasks for predicting brain responses. In our work, we improve the performance of…
Sana Ahmadi, Francois Paugam, Tristan Glatard, Pierre Bellec
The optimal training of a vision transformer for brain encoding depends on three factors: model size, data size, and computational resources. This study investigates these three pillars, focusing on the effects of data scaling, model scaling, and high-performance computing on brain encoding results. Using VideoGPT to…
Subba Reddy Oota, Jashn Arora, Vijay Rowtula, Manish Gupta + 1 more
'Raju S. Bapi'] Enabling effective brain-computer interfaces requires understanding how the human brain encodes stimuli across modalities such as visual, language (or text), etc. Brain encoding aims at constructing fMRI brain activity given a stimulus. There exists a plethora of neural encoding models which study brain…
Anirudha Kemtur, Francois Paugam, Basile Pinsard, Yann Harel + 5 more
Videogames provide a promising framework to understand brain activity in a rich, engaging, and active environment, in contrast to mostly passive tasks currently dominating the field, such as image viewing. Analyzing videogames neuroimaging data is, however, challenging, and relies on time-intensive manual annotations…
Nima Maleki, Hamid Karimi-Rouzbahani
Sensory neural coding, the brain’s process of transforming inputs into informative patterns of neural activity, generates complex and multiplexed neural codes which are hard to interpret. Although decoding methods have facilitated the interpretation of these codes, the specific features of neural activity that…
Janet M. Baker, Peter Cariani
Time is essential for understanding the brain. A temporal theory for realizing major brain functions (e.g., sensation, cognition, motivation, attention, memory, learning, and motor action) is proposed that uses temporal codes, time-domain neural networks, correlation-based binding processes and signal dynamics. It…
Kang Wang, Dengchang Wu, Caihong Ji, Benyan Luo + 2 more
The current study sought to address whether the stage of memory encoding is compromised in patients with postacute anti-NMDA receptor encephalitis and how the effects of postacute anti-NMDA receptor encephalitis on memory encoding are substantiated at the neural level. Thus, we examined memory performance and…
Stefano Panzeri, Ella Janotte, Alejandro Pequeño-Zurro, Jacopo Bonato + 1 more
'Jacopo Bonato' 'Chiara Bartolozzi'] In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain…
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…
Robert Worden
This paper uses simple arguments to derive a negative conclusion: that a computer cannot be conscious. If the brain is only a neural computer, brains cannot be conscious. Consciousness implies that there is something else happening in the brain, besides computation. In a running computer, information about outside…
Jerry Tang, Amanda LeBel, Shailee Jain, Alexander G. Huth
A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, decoders that reconstruct continuous language use invasive recordings from surgically implanted electrodes^1–3^, while decoders that use non-invasive…
Julia Berezutskaya, Anne-Lise Saive, Karim Jerbi, Marcel van Gerven
Artificial intelligence (AI) is a fast-growing field focused on modeling and machine implementation of various cognitive functions with an increasing number of applications in computer vision, text processing, robotics, neurotechnology, bioinspired computing and others. In this chapter, we describe how AI methods can…
Hamid Karimi-Rouzbahani
Distinct neural processes are often encoded across distinct time scales of neural activations. However, it has remained unclear if this multiscale coding strategy is also implemented for separate features of the same process. One difficulty is that the conventional methods of time scale analysis provide imperfect…
Erica L. Busch, Jessie Huang, Andrew Benz, Tom Wallenstein + 4 more
Brain activity as measured with functional magnetic resonance imaging (fMRI) gives the illusion of intractably high dimensionality, rife with collection and biological noise. Non-linear dimensionality reductions like UMAP, tSNE, and PHATE have proven useful for high-throughput biomedical data, but have not been…
Erhard Bieberich
Consciousness remains poorly understood as a causative force: existing theories treat it as an epiphenomenal correlate of neural activity rather than explaining how inner experience controls its substrate. I present Recurrent Integration Fractal Theory (RIFT), proposing that consciousness arises when fractal…
Janet M. Baker, Peter Cariani
Waves are fundamental. In our view, waves in the brain may constitute and drive organized neural activity patterns on individual neural and population levels. Their interactions follow basic physical principles. Taking a comprehensive, temporal and spatiotemporal perspective, we endeavor to explain multiple brain…
Michal T Kucewicz, Jan Cimbalnik, Jesus S Garcia-Salinas, Milan Brazdil + 1 more
Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have…
Hongkeun Kim
Why do some moments imprint themselves in memory while others vanish without a trace? This meta-analysis identifies a dissociation in large-scale brain networks during encoding: networks associated with impairing encoding are task-invariant, whereas those supporting it are task-specific. Drawing on 56 functional…
Km Bhavna, Azman Akhter, Romi Banerjee, Dipanjan Roy
Decoding of brain tasks aims to identify individuals’ brain states and brain fingerprints to predict behavior. Deep learning provides an important platform for analyzing brain signals at different developmental stages to understand brain dynamics. Due to their internal architecture and feature extraction techniques…
Aliye Hazal Koyuncu, Jacopo Movilli, Sevil Sahin, Dmitrii V. Kriukov + 2 more
This work describes a competing activation network, which is regulated by chemical feedback at the liquid-surface interface. Feedback loops dynamically tune the concentration of chemical components in living systems, thereby controlling regulatory processes in neural, genetic, and metabolic networks. Advances in…