Biology subjects

Olasagasti, I.

Publications and source records attributed to Olasagasti, I..

7 recordsLinked to original sources

bioRxiv2025

Temporal evolution of Neural Codes: The Added Value of a Geometric Approach to Linear Coefficients

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 time point is tested on others--is commonly used to assess the stability of neural representations over time. However, TG performance can be ambiguous: distinct representational dynamics--such as sparse versus distributed activity, or scaling of activity versus recruitment of new units--can yield similar TG matrices. Moreover, even when generalization is strong, underlying neural representations may still be evolving in ways that TG alone fails to reveal. This ambiguity of performance profiles can mask meaningful changes in the geometry of neural representations. In this study, we use controlled simulations to demonstrate how different dynamic processes can produce indistinguishable TG profiles. To resolve these ambiguities, we propose a complementary approach based on the geometry of the learned linear coefficients. Specifically, we quantify the Rotation Angle {theta} between decision subspaces (with cosine similarity) and the Feature Density (capturing whether feature contributions are distributed or sparse). Together, these measures complement TG analyses, revealing how neural representations evolve in space and time. Beyond time-resolved decoding, our approach applies broadly to any linear model, offering a geometric perspective on representational dynamics. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/681582v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@c1584forg.highwire.dtl.DTLVardef@18cdd30org.highwire.dtl.DTLVardef@10d7c22org.highwire.dtl.DTLVardef@50a6fa_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2022

A deep hierarchy of predictions enables assignment of semantic roles in real-time speech comprehension

Understanding speech requires mapping fleeting and often ambiguous soundwaves to meaning. While humans are known to exploit their capacity to contextualize to facilitate this process, how internal knowledge is deployed on-line remains an open question. Here, we present a model that extracts multiple levels of information from continuous speech online. The model applies linguistic and nonlinguistic knowledge to speech processing, by periodically generating top-down predictions and incorporating bottom-up incoming evidence in a nested temporal hierarchy. We show that a nonlinguistic context level provides semantic predictions informed by sensory inputs, which are crucial for disambiguating among multiple meanings of the same word. The explicit knowledge hierarchy of the model enables a more holistic account of the neurophysiological responses to speech compared to using lexical predictions generated by a neural-network language model (GPT-2). We also show that hierarchical predictions reduce peripheral processing via minimizing uncertainty and prediction error. With this proof-of-concept model we demonstrate that the deployment of hierarchical predictions is a possible strategy for the brain to dynamically utilize structured knowledge and make sense of the speech input.

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bioRxiv2022

Rhythmic modulation of prediction errors: a possible role for the beta-range in speech processing

Natural speech perception requires processing the current acoustic input while keeping in mind the preceding one and predicting the next. This complex computational problem could be handled by a multi timescale hierarchical inferential process that coordinates information flow up and down the language hierarchy. While theta and low-gamma neural frequency scales are convincingly involved in bottom-up syllable-tracking and phoneme-level speech encoding, the beta rhythm is more loosely associated with top-down processes without being assigned yet a specific computational function. Here we tested the hypothesis that the beta rhythm drives the precision of states during the speech recognition hierarchical inference process. We used a predictive coding model that recognizes syllables on-line in natural sentences, in which the precision of prediction errors is rhythmically modulated, resulting in alternating bottom-up vs. top-down processing regimes. We show that recognition performance increases with the rate of precision updates, with an optimal efficacy in the beta range (around 20 Hz). The model further performs when prediction errors pertaining respectively to syllable timing and syllable identity oscillate in antiphase. These results suggest that online syllable recognition globally benefits from the alternation of bottom-up and top-down dominant regime at beta rate, and that the gain is stronger when different features are also analyzed in alternation. These results speak to a discontinuous account of inferential operations in speech processing.

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bioRxiv2019

Audio-visual combination of syllables involves time-sensitive dynamics following from fusion failure

In face-to-face communication, audio-visual (AV) stimuli can be fused, combined or perceived as mismatching. While the left superior temporal sulcus (STS) is admittedly the locus of AV integration, the process leading to combination is unknown. Analysing behaviour and time-/source-resolved human MEG data, we show that while fusion and combination both involve early detection of AV physical features discrepancy in the STS, combination is associated in with activity of AV asynchrony-sensitive regions (auditory and inferior frontal cortices). Based on dynamic causal modelling, and neural signal decoding, we further show that AV speech integration outcome primarily depends on whether the STS can quickly converge onto an existing multimodal syllable representation, and that combination results from subsequent temporal processing, presumably re-ordering, of discrepant AV stimuli.

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bioRxiv2019

Converging intracortical signatures of two separated processing timescales in human early auditory cortex.

Neural oscillations in auditory cortex are argued to support parsing and representing speech constituents at their corresponding temporal scales. Yet, how incoming sensory information interacts with ongoing spontaneous brain activity, what features of the neuronal microcircuitry underlie spontaneous and stimulus-evoked spectral fingerprints, and what these fingerprints entail for stimulus encoding, remain largely open questions. We used a combination of human invasive electrophysiology, computational modeling and decoding techniques to assess the information encoding properties of brain activity and to relate them to a plausible underlying neuronal microarchitecture. We analyzed intracortical auditory EEG activity from 10 patients while they were listening to short sentences. Pre-stimulus neural activity in early auditory cortical regions often exhibited power spectra with a shoulder in the delta range and a small bump in the beta range. Speech decreased power in the beta range, and increased power in the delta-theta and gamma ranges. Using multivariate machine learning techniques, we assessed the spectral profile of information content for two aspects of speech processing: detection and discrimination. We obtained better phase than power information decoding, and a bimodal spectral profile of information content with better decoding at low (delta-theta) and high (gamma) frequencies than at intermediate (beta) frequencies. These experimental data were reproduced by a simple rate model made of two subnetworks with different timescales, each composed of coupled excitatory and inhibitory units, and connected via a negative feedback loop. Modeling and experimental results were similar in terms of pre-stimulus spectral profile (except for the iEEG beta bump), spectral modulations with speech, and spectral profile of information content. Altogether, we provide converging evidence from both univariate spectral analysis and decoding approaches for a dual timescale processing infrastructure in human auditory cortex, and show that it is consistent with the dynamics of a simple rate model.\n\nAuthor summaryLike most animal vocalizations, speech results from a pseudo-rhythmic process that reflects the convergence of motor and auditory neural substrates and the natural resonance properties of the vocal apparatus towards efficient communication. Here, we leverage the excellent temporal and spatial resolution of intracranial EEG to demonstrate that neural activity in human early auditory cortical areas during speech perception exhibits a dual-scale spectral profile of power changes, with speech increasing power in low (delta-theta) and high (gamma - high-gamma) frequency ranges, while decreasing power in intermediate (alpha-beta) frequencies. Single-trial multivariate decoding also resulted in a bimodal spectral profile of information content, with better decoding at low and high frequencies than at intermediate ones. From both spectral and informational perspectives, these patterns are consistent with the activity of a relatively simple computational model comprising two reciprocally connected excitatory/inhibitory sub-networks operating at different (low and high) timescales. By combining experimental, decoding and modeling approaches, we provide consistent evidence for the existence, information coding value and underlying neuronal architecture of dual timescale processing in human auditory cortex.

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bioRxiv2018

Integrating prediction errors at two time scales permits rapid recalibration of speech sound categories

Speech perception is assumed to arise from internal models of specific sensory features associated speech sounds. When these features change, the listener should recalibrate its internal model by appropriately weighing new versus old evidence in a volatility dependent manner. Models of speech recalibration have classically ignored volatility. Those that explicitly consider volatility have been designed to describe human behavior in tasks where sensory cues are associated with arbitrary experimenter-defined categories or rewards. In such settings, a model that maintains a single representation of the category but continuously adapts the learning rate works well. Using neurocomputational modelling we show that recalibration of existing "natural" categories is better described when sound categories are represented at different time scales. We illustrate our proposal by modeling the rapid recalibration of speech categories (Luttke et al. 2016).

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bioRxiv2018

Combining predictive coding with neural oscillations optimizes on-line speech processing

Speech comprehension requires segmenting continuous speech to connect it on-line with discrete linguistic neural representations. This process relies on theta-gamma oscillation coupling, which tracks syllables and encodes them in decipherable neural activity. Speech comprehension also strongly depends on contextual cues predicting speech structure and content. To explore the effects of theta-gamma coupling on bottom-up/top-down dynamics during on-line speech perception, we designed a generative model that can recognize syllable sequences in continuous speech. The model uses theta oscillations to detect syllable onsets and align both gamma-rate encoding activity with syllable boundaries and predictions with speech input. We observed that the model performed best when theta oscillations were used to align gamma units with input syllables, i.e. when bidirectional information flows were coordinated, and internal timing knowledge was exploited. This work demonstrates that notions of predictive coding and neural oscillations can usefully be brought together to account for dynamic on-line sensory processing.

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