arXiv subjects

Alejandro Tabas

Publications and source records attributed to Alejandro Tabas.

9 recordsLinked to original sources

arXiv2026

Prediction emerges in RNNs trained for perception

The brain is highly proficient at making sense of noisy and ambiguous sensory inputs. Predictive processing hypothesises that this ability relies on prediction. However, it is unclear why the brain would have evolved to predict the sensory world, a computationally expensive process, in order to aid perception. Here we use simulations to argue that prediction naturally emerges in systems optimised for perception. We train recurrent neural networks (RNNs) to denoise a tokenised version of Bach's compositions at a range of noise levels. Afterwards, we enquire whether the states of the networks contain predictive information about the next token. We test this by freezing the RNN weights and training a linear readout from its states on prediction. We compare the performance of the linear readout with that of an independently trained linear benchmark model. The results show that the linear readout from the RNNs outperforms the benchmark model at moderate levels of noise, indicating that the networks rely on a predictive mechanism to support perception. We further show that the responses of the RNNs to sensory inputs are proportional to prediction error. Together, the results demonstrate that neural signatures of predictive processing emerge, without any further training constraints, from optimisation of perception.

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arXiv2026

Transcranial magnetic stimulation of visual-motion area V5/MT modulates sensory thalamus responses during visual speech recognition

Responses in the sensory thalamic nuclei are modulated by perceptual tasks. Whether such response modulations rely on feedback from cerebral cortex in humans is unknown. Here, we addressed this question in the context of visual speech recognition: the visual sensory thalamus, i.e. the lateral geniculate nucleus (LGN), has differential BOLD-responses to visual speech than non-speech control tasks. We tested whether such response modulation relies on the function of the visual association cortex, specifically visual-motion area V5/MT. We applied inhibitory transcranial magnetic stimulation (TMS) over bilateral visual-motion sensitive areas V5/MT on 26 healthy adults. Subsequently, participants performed a visual speech and a colour recognition task on identical muted videos of speaking faces during functional magnetic resonance imaging (fMRI). The LGN showed a significant signal change between the visual speech task and the colour task following Vertex stimulation as active control region. This modulation was significantly reduced following inhibitory V5/MT stimulation. V5/MT stimulation also reduced task-dependent functional connectivity between V5/MT and the LGN. These results identify corticothalamic feedback as integral mechanism in visual processing. In particular, the visual association cortex has a causal role in modulating LGN responses during speech recognition.

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arXiv2025

Detecting absence: A dedicated prediction-error signal emerging in the auditory thalamus

How does the brain know what is out there and what is not? Living organisms cannot rely solely on sensory signals for perception because they are noisy and ambiguous. To transform sensory signals into stable percepts, the brain uses its prior knowledge or beliefs. Current theories describe perceptual beliefs as probability distributions over the features of the stimuli, summarised by their mean and variance. Beliefs are updated by feature prediction errors: the mismatch between expected and observed feature values. This framework explains how the brain encodes unexpected changes in stimulus features (e.g., higher or lower pitch, stronger or weaker motion). How the brain updates beliefs about a stimulus' presence or absence is, however, unclear. We propose that the detection of absence relies on a distinct form of prediction error dedicated to reducing the beliefs on stimulus occurrence. We call this signal absence prediction error. Using the human auditory system as a model for sensory processing, we developed a paradigm designed to test this hypothesis. fMRI results showed that absence prediction error is encoded in the auditory thalamus and cortex, indicating that absence is explicitly represented in subcortical sensory pathways. Moreover, while feature prediction error is already encoded in the auditory midbrain, absence prediction error was not, implying that absence-related error signals are supported by a different circuit. These results identify a neural mechanism for the detection of sensory absence. Such mechanisms may be disrupted in conditions such as psychosis, where predictions about absence and presence are impaired.

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arXiv2021

Frequency and frequency modulation share the same predictive encoding mechanisms in human auditory cortex

Expectations can substantially influence perception. Predictive coding is a theory of sensory processing that aims to explain the neural mechanisms underlying the effect of expectations in sensory processing. Its main assumption is that sensory neurons encode prediction error with respect to expected sensory input. Neural populations encoding prediction error have been previously reported in the human auditory cortex (AC); however, most studies focused on the encoding of pure tones and induced expectations by stimulus repetition, potentially confounding prediction error with effects of neural habituation. Here, we systematically studied prediction error to pure tones and fast frequency modulated (FM) sweeps across different auditory cortical fields in humans. We conducted two fMRI experiments, each using one type of stimulus. We measured BOLD responses across the bilateral auditory cortical fields Te1.0, Te1.1, Te1.2, and Te3 while participants listened to sequences of sounds. We induced subjective expectations on the incoming sounds independently of stimulus repetition using abstract rules. Our results indicate that pure tones and FM-sweeps are encoded as prediction error with respect to the participants' expectations across auditory cortical fields. The topographical distribution of neural populations encoding prediction error to pure tones and FM-sweeps was highly correlated in left Te1.1 and Te1.2, and in bilateral Te3, suggesting that predictive coding is the general encoding mechanism in AC.

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arXiv2021

Concurrent generative models inform prediction error in the human auditory pathway

Predictive coding is the leading algorithmic framework to understand how expectations shape our experience of reality. Its main tenet is that sensory neurons encode prediction error: the residuals between a generative model of the sensory world and the actual sensory input. However, it is yet unclear how this scheme generalises to the multi-level hierarchical architecture of sensory processing. Theoretical accounts of predictive coding agree that neurons computing prediction error and the generative model exist at all levels of the processing hierarchy. However, there is not a current consensus of how predictions from independent models at different stages are integrated during the computation of prediction error. Here we investigated predictive processing with respect to two independent concurrent generative models in the auditory pathway using functional magnetic resonance imaging. We used two paradigms where human participants listened to sequences of either pure tones or FM-sweeps while we recorded BOLD responses in inferior colliculus (IC), medial geniculate body (MGB), and auditory cortex (AC). Each paradigm included the induction of two generative models: one based on local stimulus statistics; and another model based on the subjective expectations induced by task instruction. We used Bayesian model comparison to test whether neural responses in IC, MGB, and AC encoded prediction error with respect to either of the two generative models, or a combination of both. Results showed that neural populations in bilateral IC, MGB, and AC encode prediction error with respect to a combination of the two generative models, suggesting that the predictive architecture of predictive coding might be more complex than previously hypothesised.

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arXiv2021

Fast frequency modulation is encoded according to the listener expectations in the human subcortical auditory pathway

Expectations aid and bias our perception. In speech, expected words are easier to recognise than unexpected words, particularly in noisy environments, and incorrect expectations can make us misunderstand our conversational partner. Expectations are combined with the output from the sensory pathways to form representations of speech in the cerebral cortex. However, it is unclear whether expectations are propagated further down to subcortical structures to aid the encoding of the basic dynamic constituent of speech: fast frequency-modulation (FM). Fast FM-sweeps are the basic invariant constituent of consonants, and their correct encoding is fundamental for speech recognition. Here we tested the hypothesis that subjective expectations drive the encoding of fast FM-sweeps characteristic of speech in the human subcortical auditory pathway. We used fMRI to measure neural responses in the human auditory midbrain (inferior colliculus) and thalamus (medial geniculate body). Participants listened to sequences of FM-sweeps for which they held different expectations based on the task instructions. We found robust evidence that the responses in auditory midbrain and thalamus encode the difference between the acoustic input and the subjective expectations of the listener. The results indicate that FM-sweeps are already encoded at the level of the human auditory midbrain and that encoding is mainly driven by subjective expectations. We conclude that the subcortical auditory pathway is integrated in the cortical network of predictive speech processing and that expectations are used to optimise the encoding of even the most basic acoustic constituents of speech.

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arXiv2020

Predictive coding underlies adaptation in the subcortical sensory pathway

The subcortical sensory pathways are the fundamental channels for mapping the outside world to our minds. Sensory pathways efficiently transmit information by adapting neural responses to the local statistics of the sensory input. The longstanding mechanistic explanation for this adaptive behaviour is that neuronal habituation scales activity to the local statistics of the stimuli. An alternative account is that neural coding is directly driven by expectations of the sensory input. Here we used abstract rules to manipulate expectations independently of local stimulus statistics. The ultra-high-field functional-MRI data show that expectations, and not habituation, are the main driver of the response amplitude to tones in the human auditory pathway. These results provide first unambiguous evidence of predictive coding and abstract processing in a subcortical sensory pathway, indicating that the brain only holds subjective representations of the outside world even at initial points of the processing hierarchy.

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arXiv2019

Neural modelling of the encoding of fast frequency modulation

Frequency modulation (FM) is a basic constituent of vocalisation in many animals as well as in humans. In human speech, short rising and falling FM-sweeps called formant transitions characterise individual speech sounds. There are two representations of FM in the ascending auditory pathway: a spectral representation, holding the instantaneous frequency of the stimuli; and a sweep representation, consisting of neurons that respond selectively to FM direction. To-date computational models use feedforward mechanisms to explain FM encoding. However, from neuroanatomy we know that there are massive feedback projections in the auditory pathway. Here, we found that a classical FM-sweep perceptual effect, the sweep pitch shift, cannot be explained by standard feedforward processing models. We hypothesised that the sweep pitch shift is caused by a predictive interaction between the sweep and the spectral representation. To test this hypothesis, we developed a novel model of FM encoding incorporating a predictive feedback mechanism. The model fully accounted for experimental data that we acquired in a perceptual experiment with human participants as well as previously published experimental results. We also designed a new family of stimuli for a second perceptual experiment to further validate the model. Combined, our results indicate that predictive interaction between different frequency encoding and direction encoding neural representations plays an important role in the neural processing of FM. In the brain, this mechanism is likely to occur at early stages of the processing hierarchy.

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arXiv2017

Early processing of consonance and dissonance in human auditory cortex

Pitch is the perceptual correlate of sound's periodicity and a fundamental property of the auditory sensation. The interaction of two or more pitches gives rise to a sensation that can be characterized by its degree of consonance or dissonance. In the current study, we investigated the neuromagnetic representations of consonant and dissonant musical dyads using a new model of cortical activity, in an effort to assess the possible involvement of pitch-specific neural mechanisms in consonance processing at early cortical stages. In the first step of the study, we developed a novel model of cortical pitch processing designed to explain the morphology of the pitch onset response (POR), a pitch-specific subcomponent of the auditory evoked N100 component in the human auditory cortex. The model explains the neural mechanisms underlying the generation of the POR and quantitatively accounts for the relation between its peak latency and the perceived pitch. Next, we applied magnetoencephalography (MEG) to record the POR as elicited by six consonant and dissonant dyads. The peak latency of the POR was strongly modulated by the degree of consonance within the stimuli; specifically, the most dissonant dyad exhibited a POR with a latency that was about 30ms longer than that of the most consonant dyad, an effect that greatly exceeds the expected latency difference induced by a single pitch sound. Our model was able to predict the POR latency pattern observed in the neuromagnetic data, and to generalize this prediction to additional dyads. These results indicate that the neural mechanisms responsible for pitch processing exhibit an intrinsic differential response to concurrent consonant and dissonant pitch combinations, suggesting that the perception of consonance and dissonance might be an emergent property of the pitch processing system in human auditory cortex.

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