arXiv subjects

Laurent Caplette

Publications and source records attributed to Laurent Caplette.

4 recordsLinked to original sources

arXiv2026

Improved cross-validated distances for multivariate pattern analysis

Characterizing the dissimilarity of neural representations between experimental conditions, and tracking it across time, is a central goal of multivariate pattern analysis. Guggenmos et al. (2018) assessed the reliability of many of the measures that can be used for that purpose on MEG data and recommended the use of either the cross-validated Euclidean distance or the within-class-corrected Pearson distance. In this commentary, we show that we can improve upon these distances. First, we show that the cross-validated Euclidean distance is equivalent to a sum of between-partition distances and that this equivalence can be leveraged to obtain a generalized variant, with increased reliability and accuracy. Second, we use the relationship between Euclidean distance and Pearson correlation to define a cross-validated correlation distance in a similar way. The resulting distance is more accurate and interpretable than a formulation proposed by Guggenmos and colleagues. Finally, we discuss the relationship between our generalized cross-validation and within-class correction, another strategy often used to increase reliability, and we show that generalized cross-validation results in higher accuracy for the correlation distance.

q-bio.NC↗

arXiv2026

Time^2: A framework for the neural dynamics of visual perception

Whenever we look at an object, we seem to perceive it immediately. However, this is not the case for two reasons. First, it takes hundreds of milliseconds for the brain to process visual information reaching the retina. Second, we have to look at an object for a certain amount of time to perceive it (and we typically look at it for hundreds of milliseconds) -- during that time, visual information is continuously received on our retinas. These facts together imply that visual information is both processed and received through time. These two temporal facets of perception, which we term processing time and stimulus time, are often conflated in the literature. Moreover, processing time and stimulus time are usually not considered together in experiments. Here, we argue that, to obtain a more complete portrait of visual perception and constrain further models of vision, it is essential to consider and measure both temporal facets simultaneously. We present a new method designed to do so that is based on reverse correlation: Time^2. We show that this method allows us to precisely characterize many neural phenomena, including rhythmic perception, predictive processing and coarse-to-fine sampling.

q-bio.NC↗

arXiv2024

Latent Representation Learning for Multimodal Brain Activity Translation

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.

cs.LG↗

arXiv2024

Looking through the mind's eye via multimodal encoder-decoder networks

In this work, we explore the decoding of mental imagery from subjects using their fMRI measurements. In order to achieve this decoding, we first created a mapping between a subject's fMRI signals elicited by the videos the subjects watched. This mapping associates the high dimensional fMRI activation states with visual imagery. Next, we prompted the subjects textually, primarily with emotion labels which had no direct reference to visual objects. Then to decode visual imagery that may have been in a person's mind's eye, we align a latent representation of these fMRI measurements with a corresponding video-fMRI based on textual labels given to the videos themselves. This alignment has the effect of overlapping the video fMRI embedding with the text-prompted fMRI embedding, thus allowing us to use our fMRI-to-video mapping to decode. Additionally, we enhance an existing fMRI dataset, initially consisting of data from five subjects, by including recordings from three more subjects gathered by our team. We demonstrate the efficacy of our model on this augmented dataset both in accurately creating a mapping, as well as in plausibly decoding mental imagery.

eess.IV↗