arXiv subjects

Alain de Cheveigné

Publications and source records attributed to Alain de Cheveigné.

4 recordsLinked to original sources

arXiv2026

Graceful forgetting: Memory as a process

A rational framework is proposed to explain how we accommodate unbounded sensory input within bounded memory. According to this framework, memory is stored as a statistic-like representation that is repeatedly summarized and compressed to make room for new input. Summarization of sensory input must be rapid; that of abstract trace might be slower and more deliberative, drawing on elaborative processes some of which might occasionally reach consciousness (as in mind-wandering). Short-term sensory traces are summarized as simple statistics organized into structures such as a time series, graph or dictionary, and longer-term abstract traces as more complex statistic-like structures. Summarization at multiple time scales requires an intensive process of memory curation which might account for the high metabolic consumption of the brain at rest. Summarization may be guided by heuristics to help choose which statistics to apply at each step, so that the trace is useful for a wide range of future needs, the objective being to "represent the past" rather than tune for a specific task. However, the choice of statistics (or of heuristics to guide that choice) is a potential target for learning, possibly over long-term scales of development or evolution. The framework is intended as an aid to make sense of our extensive empirical and theoretical knowledge of memory and bring us closer to understanding it in functional and mechanistic terms.

q-bio.NC↗

arXiv2025

Feed-forward active magnetic shielding

Magnetic fields from the brain are tiny relative to ambient fields which therefore need to be suppressed. The common solution of passive shielding is expensive, bulky and insufficiently effective, thus motivating research into the alternative of active shielding which comes in two flavours: feed-back and feed-forward. In feed-back designs (the most common), corrective fields are created by coils driven from sensors within the area that they correct, for example from the main sensors of an MEG device. In feed-forward designs (less common), corrective fields are driven from dedicated reference sensors outside the area they correct. Feed-forward can achieve better performance than feed-back, in principle, however its implementation is hobbled by an unavoidable coupling between coils and reference sensors, which reduces the effectiveness of the shielding and may affect stability, complicating the design. This paper suggests a solution that relies on a ``decoupling matrix," inserted in the signal pathway between sensors and corrective coils, to counteract the spurious coupling. This allows feed-forward shielding do reduce the ambient field to zero across the full frequency range, in principle, although performance may be limited by other factors such as current noise. The solution, which is fully data-driven and does not require geometric calculations, high-tolerance fabrication, or physical calibration, has been evaluated by simulation, but not implemented in hardware. It might contribute to the deployment of a new generation of measurement systems based on optically-pumped magnetometers (OPM). The lower cost and reduced constraints of those systems are a strong incentive to likewise reduce the cost and constraints of the shielding required to operate them, hence the appeal of active shielding.

physics.med-ph↗

arXiv2022

Graceful Forgetting II. Data as a Process

Data are rapidly growing in size and importance for society, a trend motivated by their enabling power. The accumulation of new data, sustained by progress in technology, leads to a boundless expansion of stored data, in some cases with an exponential increase in the accrual rate itself. Massive data are hard to process, transmit, store, and exploit, and it is particularly hard to keep abreast of the data store as a whole. This paper distinguishes three phases in the life of data: acquisition, curation, and exploitation. Each involves a distinct process, that may be separated from the others in time, with a different set of priorities. The function of the second phase, curation, is to maximize the future value of the data given limited storage. I argue that this requires that (a) the data take the form of summary statistics and (b) these statistics follow an endless process of rescaling. The summary may be more compact than the original data, but its data structure is more complex and it requires an on-going computational process that is much more sophisticated than mere storage. Rescaling results in dimensionality reduction that may be beneficial for learning, but that must be carefully controlled to preserve relevance. Rescaling may be tuned based on feedback from usage, with the proviso that our memory of the past serves the future, the needs of which are not fully known.

cs.AI↗

arXiv2021

EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices

People suffering from hearing impairment often have difficulties participating in conversations in so-called `cocktail party' scenarios with multiple people talking simultaneously. Although advanced algorithms exist to suppress background noise in these situations, a hearing device also needs information on which of these speakers the user actually aims to attend to. The correct (attended) speaker can then be enhanced using this information, and all other speakers can be treated as background noise. Recent neuroscientific advances have shown that it is possible to determine the focus of auditory attention from non-invasive neurorecording techniques, such as electroencephalography (EEG). Based on these new insights, a multitude of auditory attention decoding (AAD) algorithms have been proposed, which could, combined with the appropriate speaker separation algorithms and miniaturized EEG sensor devices, lead to so-called neuro-steered hearing devices. In this paper, we provide a broad review and a statistically grounded comparative study of EEG-based AAD algorithms and address the main signal processing challenges in this field.

eess.SP↗