Biology subjects

Vinck, M.

Publications and source records attributed to Vinck, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Improved detection and spatiotemporal spectral analysis of neural traveling waves

Traveling waves (TWs) are a fundamental mode of neural dynamics, yet existing detection methods are limited by sensor geometry, spatial-frequency resolution, signal amplitude, and ambiguity between propagating and standing-wave patterns. Here we introduce the Traveling Wave Index (TWINDEX), a framework for three-dimensional spatiotemporal spectral analysis of TWs across temporal frequency, spatial frequency, and propagation direction. TWINDEX generalizes to irregular sensor layouts, and quantifies wave strength as the reduction in circular phase variance produced by a candidate planar wave. This normalization yields robust behavior at both low and high spatial frequencies and suppresses coherent in-phase activity. Directional moments further separate planar from standing waves. We derive analytical links between TWINDEX, parametric planar-wave fit, and distance-phase correlation, and introduce projected distance-phase correlation (ProDPC) for sensitive single-trial planar-wave detection. Applying these methods to large-scale marmoset ECoG and human EEG, we identify alpha/low-beta TWs localized in spatial and temporal frequency, with physiologically plausible propagation speeds. Across trials and epochs, waves occur in oppositely directed propagation modes, demonstrating that alpha/beta activity is associated with both feedforward- and feedback-directed large-scale dynamics.

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bioRxiv2026

Information Flow and Traveling Waves show distinct cortical trajectories

Traveling waves (TWs) are a hallmark of cortical dynamics, proposed to organize neuronal activity across space and time. Yet their propagation can oppose the causal flow of information, challenging their functional interpretation. We demonstrate this using computational simulations and validate our predictions in electrophysiological recordings. We introduce the Directional Information Flow Field (DIFF), which adapts Granger causality to two-dimensional neural recordings to map directed interactions across cortical networks. In computational models, DIFF recovers the direction and sources of information flow, whereas delays and inhibition can cause TWs to propagate in opposing or orthogonal directions. We validate these findings in human and non-human primate electrophysiological recordings, revealing reversed or orthogonal flow axes, distinct spatial origins, and frequency-dependent differences in feedforward and feedback dynamics. These findings challenge the functional interpretation of TWs and establish DIFF as a complementary framework for revealing directed neural communication underlying cortical dynamics.

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bioRxiv2026

Attention reshapes the information dynamics of thalamic and cortical prediction-error learning

Prediction errors (PEs) drive perceptual learning by updating internal models of the sensory environment, yet it remains unclear how attention reshapes their representation across distributed thalamocortical circuits. Using intracranial stereoelectroencephalography (sEEG) from 17 patients performing a roving auditory oddball task under attended and unattended conditions, we quantified PE encoding using mutual information and co-information to capture redundant and synergistic PE representations. Attention modulated PE encoding in both the thalamus and the temporal cortex, but with distinct informational dynamics. Thalamic encoding showed a stable reduction of PE information during distraction, consistent with state-dependent thalamocortical gating. In contrast, the temporal cortex expressed two opposing learning trajectories during attended listening that converged once attention was diverted, revealing distinct cortical learning regimes rather than a uniform attentional effect. Attention further reorganized the informational content of cortical PE representations by altering the balance between redundant and synergistic information. A biologically constrained neural network showed that attention-dependent changes in inhibition and long-range connectivity reproduced these dynamics through Hebbian learning. Together, these findings suggest that attention regulates predictive learning not simply by changing the strength of PE responses, but by reshaping how distributed thalamocortical circuits represent and integrate sensory evidence over time.

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bioRxiv2025

Representational drift shows same-class acceleration in visual cortex and artificial neural networks

The neural code is not fixed but shows substantial representational drift over time. It has been proposed that representational drift reflects continuous learning resulting from input-dependent plasticity. Using theoretical analysis and simulations in artificial neural networks, we show that input-dependent plasticity entails a same-class-acceleration principle: Representational drift for a given class of stimuli is predominantly caused by presenting that class of stimuli, rather than presenting other classes of stimuli. We analyze electrophysiological recordings of mouse visual cortex to examine whether within-session representational drift is consistent with this principle. Within-session representational drift was not explained by changes in behavioral state. Instead, it showed a systematic temporal structure that reflected sensory experience rather than behavior. Drift for a given set of stimuli accelerated during blocks presenting that set of stimuli and slowed down during blocks in which other stimuli were presented. Thus, sensory inputs, not elapsed time, organize representational drift in a stimulus-specific manner, consistent with theoretical predictions for training neural networks.

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bioRxiv2025

A mean-field model of neural networks with PV and SOM interneurons reveals connectivity-based mechanisms of gamma oscillations

Classic theoretical models of cortical oscillations are based on the interactions between two populations of excitatory and inhibitory neurons. Nevertheless, experimental studies and network simulations suggest that interneuron subclasses such as parvalbumin (PV) and somatostatin (SOM) exert distinct control over oscillatory dynamics. Yet, we lack a theoretical understanding of the mechanisms underlying oscillations in E-PV-SOM circuits and of the differences with respect to the classical mechanisms for oscillations in simpler E-I networks. Here, we derive a biologically realistic mean-field model of a canonical three-population E-PV-SOM circuit. This model robustly generates oscillations whose features are consistent with experimental observations, including the relative timing of PV and SOM activity and the effects of optogenetic perturbations. By reducing the model to a linear analytical form, we demonstrate that gamma oscillations emerge directly from the cell-specific connectivity of the three-population circuit. This connectivity motif alone accounts for experimentally observed phase relationships, with PV activity consistently leading that of SOM neurons. Together, this mean field model identifies a distinct structural mechanism giving rise to oscillations in canonical E-PV-SOM circuits and provides theoretical primitives for constructing large-scale, cell-type-specific models of cortical dynamics.

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bioRxiv2025

Voltage-dependent reversal potentials in spiking recurrent neural networks enhance energy efficiency and task performance

Spiking recurrent neural networks (SRNNs) rival gated RNNs on various tasks, yet they still lack several hallmarks of biological neural networks. We introduce a biologically grounded SRNN that implements Dales law with voltage-dependent AMPA and GABA reversal potentials. These reversal potentials modulate synaptic gain as a function of the postsynaptic membrane potential, and we derive theoretically how they make each neurons effective dynamics and subthreshold resonance input-dependent. We trained SRNNs on the Spiking Heidelberg Digits dataset, and show that SRNN with reversal potentials cuts spike energy by up to 4x, while increasing task accuracy. This leads to high-performing Dalean SRNNs, substantially improving on Dalean networks without reversal potentials. Thus, Dales law with reversal potentials, a core feature of biological neural networks, can render SRNNs more accurate and energy-efficient.

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bioRxiv2025

Biomimetic self-regulation in intrinsically motivated robots

From weaving spiders to hibernating mammals and migratory birds, nature presents numerous examples of organisms exhibiting extraordinary autonomous behaviors that ensure their self-maintenance. However, physiological needs often interact and compete. This requires living organisms to handle them as a coordinated system of internal needs rather than as isolated subsystems. We present an artificial agent equipped with a neural mass model replicating fundamental self-regulatory behaviors observed in desert lizards. Our results demonstrate that this agent not only autonomously regulates its internal temperature by navigating to areas with optimal environmental conditions, but also harmonizes this process with other internal needs, such as energy, hydration, security, and mating. This biomimetic agent outperforms a control agent lacking interoceptive awareness in terms of efficiency, fairness, and stability. Additionally, to demonstrate the flexibility of our framework, we develop a "cautious" agent that prioritizes security over other needs, achieving a Maslow-like hierarchical organization of internal needs. Together, our findings suggest that grounding robot behavior in biological principles of self-regulation provides a robust framework for designing multipurpose, intrinsically motivated agents capable of resolving trade-offs in dynamic environments.

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bioRxiv2025

Motivational Cognitive Maps for Self-Regulated Autonomous Navigation

The mammalian hippocampal formation plays a critical role in efficient and flexible navigation. Hippocampal place cells exhibit spatial tuning, characterized by increased firing rates when an animal occupies specific locations in its environment. However, the mechanisms underlying the encoding of spatial information by hippocampal place cells remain not fully understood. Evidence suggests that spatial preferences are shaped by multimodal sensory inputs. Yet, existing hippocampal models typically rely on a single sensory modality, overlooking the role of interoceptive information in the formation of cognitive maps. In this paper, we introduce the Motivational Hippocampal Autoencoder (MoHA), a biologically inspired model that integrates interoceptive (motivational) and exteroceptive (visual) information to generate motivationally modulated cognitive maps. MoHA captures key hippocampal firing properties across different motivational states and, when embedded in a reinforcement learning agent, generates adaptive internal representations that drive goal-directed foraging behavior. Grounded in the principle of biological autonomy, MoHA enables the agent to dynamically adjust its navigation strategies based on internal drives, ensuring that behavior remains flexible and context-dependent. Our results show the benefits of integrating motivational cognitive maps into artificial agents with a varying set of goals, laying the foundation for self-regulated multi-objective reinforcement learning.

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bioRxiv2025

Broadband synergy versus oscillatory redundancy in the visual cortex

The cortex generates diverse neural dynamics, ranging from broadband fluctuations to narrowband oscillations at specific frequencies. Here, we investigated whether broadband and oscillatory dynamics play different roles in the encoding and transmission of visual information. We used information-theoretical measures to dissociate neural signals sharing common information (i.e., redundancy) from signals encoding complementary information (i.e., synergy). We analyzed electrocorticography (ECoG) and local field potentials (LFP) in the visual cortex of human and non-human primates (macaque) to investigate the extent to which broadband signals (BB) and narrowband gamma (NBG) oscillations conveyed synergistic or redundant information about images. In both species, the information conveyed by BB signals was highly synergistic within and between visual areas. By contrast, the information carried by NBG was primarily redundant within and between the same visual areas. Finally, the information conveyed by BB signals emerged early after stimulus onset, while NBG sustained information at later time points. These results suggest a potential dual role of BB and NBG cortical dynamics in visual processing, with broadband dynamics supporting nonlinear pattern recognition and oscillations facilitating information maintenance across the cortex.

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bioRxiv2025

What do neural travelling waves tell us about information flow?

In many behavioral conditions, neural activity manifests itself within and across brain regions as traveling waves, revealing the importance of analyzing spatiotemporal dynamics in electrophysiological data. Most methods detect traveling waves by measuring spatial phase gradients, i.e., monotonic and ordered phase changes through space. It is unclear, however, how these traveling waves relate to the causal directionality of information flow. Here, we analyze systems of coupled nodes with an external input to one node. We demonstrate that the phase ordering in traveling waves does not always correspond to the direction of effective information flow. We show that discrepancies can emerge in the case of systems with delays and inhibitory influences. As a methodological solution, we show that Granger causality analysis can, in linear systems, recover the directionality of the information flow. We propose a new measure called DIFF, the Directional Information Flow Field. DIFF is constructed by analyzing directed causal influences in space and time between neighbours, yielding a vector field. As a proof of principle, we show that, in a 2D field where a connected network is perturbed by an external input, the Divergence of DIFF can identify the spatial source of the perturbations. We propose that causal inference methods provide complementary information to phase-based traveling wave methods in analyzing system dynamics and information flow.

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bioRxiv2025

A cortical microcircuit model reveals distinct inhibitory mechanisms of network oscillations and stability

We identify a computational mechanism for network oscillations distinct from classic excitatory-inhibitory networks - CAMINOS (Canonical Microcircuit Network Oscillations) - in which different inhibitory-interneuron classes make distinct causal contributions to network oscillations and stability. A computational network model of the canonical microcircuit consisting of SOM, PV and excitatory neurons reproduced key experimental findings, including: Stochastic gamma oscillations with drive-dependent frequency; precise phase-locking of PV interneurons and delayed firing of SOM interneurons; and the distinct effects of optogenetic perturbations of SOM and PV cells. In CAMINOS, the generation of network oscillations depends on both the precise spike timing of SOM and PV interneurons, with PV cells regulating oscillation frequency and network stability, and delayed SOM firing controlling the oscillation amplitude. The asymmetric PV-SOM connectivity is found to be the key source ingredient to generate these oscillations, that naturally establishes distinct PV and SOM-cell spike timing. The CAMINOS model predicts that increased SOM/PV densities along the cortical hierarchy leads to decreased oscillation frequencies (from gamma to alpha/beta) and increased seizure susceptibility, suggesting a unified circuit model for oscillations across different frequency bands.

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bioRxiv2025

Neural responses in early, but not late, visual cortex are well predicted by random-weight CNNs with sufficient model complexity

Convolutional neural networks (CNNs) were inspired by the organization of the primate visual system, and in turn have become effective models of the visual cortex, allowing for accurate predictions of neural stimulus responses. While training CNNs on brain-relevant object-recognition tasks may be an important pre-requisite to predict brain activity, the CNNs brain-like architecture alone may already allow for accurate prediction of neural activity. Here, we evaluated the performance of both task-optimized and brain-optimized convolutional neural networks (CNNs) in predicting neural responses across visual cortex, and performed systematic architectural manipulations and comparisons between trained and untrained feature extractors to reveal key structural components influencing model performance. For human and monkey area V1, random-weight CNNs employing the ReLU activation function, combined with either average or max pooling, significantly outperformed other activation functions. Random-weight CNNs matched their trained counterparts in predicting V1 responses. The extent to which V1 responses can be predicted correlated strongly with the neural networks complexity, which reflects the non-linearity of neural activation functions and pooling operations. However, this correlation between encoding performance and complexity was significantly weaker for higher visual areas that are classically associated with object recognition, such as monkey IT. To test whether this difference between visual areas reflects functional differences, we trained neural network models on both texture discrimination and object recognition tasks. Consistent with our hypothesis, model complexity correlated more strongly with performance on texture discrimination than object recognition. Our findings indicate that random-weight CNNs with sufficient model complexity allow for comparable prediction of V1 activity as trained CNNs, while higher visual areas require precise weight configurations acquired through training via gradient descent.

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bioRxiv2024

Stimulus-specificity of surround-induced responses in primary visual cortex

Recent work suggests that stimuli in the surround can drive V1 neurons even without direct visual input to the classical receptive field (RF). These surround-induced responses may represent a prediction of the occluded stimulus, a prediction error, or alternatively, a representation of the gray patch covering the RF. Using Neuropixels recordings in mouse V1, we found that a distal surround stimulus increased V1 firing rates for gray patches up to 90{degrees} in diameter, while LGN firing rates decreased for the same stimuli. These responses occurred across a wide range of conditions: they were elicited by both moving and stationary surround stimuli, did not require spatial continuity or motion coherence, and persisted even for large gray patches (90{degrees}) where there was no mismatch between the classical RF stimulus ([~]20{degrees}) and the near surround. They also emerged when the gray patch appeared as a salient object against a uniform black or white background. Additionally, response magnitudes and latencies were highly similar for black/white uniform surface stimuli on a gray background, with latencies increasing with the gray-patch diameter. These findings are difficult to reconcile with the predictive coding interpretation and fit best with the hypothesis that surround-induced responses reflect the representation of the uniform surface itself and may thereby contribute to image segmentation processes.

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bioRxiv2024

Hippocampal place cell sequences during a visual discrimination task: recapitulation of paths near the chosen reward site and independence from perirhinal activity

Compressed hippocampal place-cell sequences have been associated with memory storage, retrieval and planning, but it remains unclear how they align with activity in the parahippocampal cortex. In a visuospatial discrimination task, we found a wide repertoire of hippocampal place cell sequences, which recapitulated paths across the task environment. Place cell sequences generated at reward sites predominantly reiterated trajectories near the chosen maze side, whereas trajectories associated with the side chosen in the previous trial were underrepresented. We hypothesized that neurons in the perirhinal cortex, which during the task display broad firing fields correlated with the animals location, might reactivate in concert with hippocampal sequences. However, we found no evidence of significant perirhinal engagement during virtual trajectories, indicating that these hippocampal memory-related operations can occur independently of the perirhinal cortex.

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bioRxiv2023

Inhibitory feedback enables predictive learning of multiple sequences in neural networks

Anticipating future events is a key computational task for neuronal networks. Experimental evidence suggests that reliable temporal sequences in neural activity play a functional role in the association and anticipation of events in time. However, how neurons can differentiate and anticipate multiple spike sequences remains largely unknown. We implement a learning rule based on predictive processing, where neurons exclusively fire for the initial, unpredictable inputs in a spiking sequence, leading to an efficient representation with reduced post-synaptic firing. Combining this mechanism with inhibitory feedback leads to sparse firing in the network, enabling neurons to selectively anticipate different sequences in the input. We demonstrate that intermediate levels of inhibition are optimal to decorrelate neuronal activity and to enable the prediction of future inputs. Notably, each sequence is independently encoded in the sparse, anticipatory firing of the network. Overall, our results demonstrate that the interplay of self-supervised predictive learning rules and inhibitory feedback enables fast and efficient classification of different input sequences.

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bioRxiv2023

Differential population coding of natural movies through spike counts and temporal sequences

Information in the nervous system is encoded by the spiking patterns of large populations of neurons. The analysis of such high-dimensional data is typically restricted to simple, arbitrarily defined features like spike rates, which discards information in the temporal structure of spike trains. Here, we use a recently developed method called SpikeShip based on optimal transport theory, which captures information from all of the relative spike-timing relations among neurons. We compared spike-rate and spike-timing codes in neural ensembles from six visual areas during natural video presentations. Temporal spiking sequences conveyed substantially more information about natural movies than population spike-rate vectors, especially for larger number of neurons. As previously, shown, population rate vectors exhibited substantial drift across repetitions and between blocks. Conversely, encoding through temporal sequences was stable over time, and did not show representational drift both within and between blocks. These findings reveal a purely spike-based neural code that is based on relative spike timing relations in neural ensembles alone.

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bioRxiv2023

Optogenetic stimulation reveals frequency-dependent resonance and encoding in V1 excitatory and inhibitory neurons

Cortical information processing is thought to be facilitated by the resonant properties of individual neurons and neuronal networks, which selectively amplify inputs at specific frequencies. We used optogenetics to test how different input frequencies are encoded by excitatory cells and parvalbumin-expressing (PV) interneurons in mouse V1. Spike phase-locking and power increased with frequency, reaching a broad peak around 80-100Hz. This effect was observed only for Chronos, a fast-kinetic opsin, but not for Channelrhodopsin-2. Surprisingly, neurons did not exhibit narrow-band resonance in specific frequency-ranges, and showed reliably phase-locking up to 140Hz. Strong phase-locking at high frequencies reflected non-linear input/output transformations, with neurons firing only in a narrow part of the cycle. By contrast, low-frequency inputs were encoded in a more continuous manner. Correspondingly, spectral coherence and firing rates showed little dependence on frequency and did not reflect transferred power. To investigate whether strong phase-locking facilitated the reliable encoding of inputs, we analyzed various spike-train distances and Fano factor. Interestingly, responses to lower rather than higher frequencies had more globally reliable spike-counts and timing structure. These findings have various practical implications for understanding the effects of optogenetic stimulation and choice of opsin. Furthermore, they show both PV and excitatory neurons respond with more local precision, i.e. phase-locking, to high-frequency inputs, but have more globally reliable responses to low-frequency inputs, suggesting differential coding regimes for these frequencies.

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bioRxiv2023

Distinct roles of PV and Sst interneurons in visually-induced gamma oscillations

Sensory processing relies on interactions between excitatory and inhibitory neurons, which are often coordinated by 30-80Hz gamma oscillations. However, the specific contributions of distinct interneurons to gamma synchronization remain unclear. We performed high-density recordings from V1 in awake mice and used optogenetics to identify PV+ (Parvalbumin) and Sst+ (Somatostatin) interneurons. PV interneurons were highly phase-locked to visually-induced gamma oscillations. Sst cells were heterogeneous, with only a subset of narrow-waveform cells showing strong gamma phase-locking. Interestingly, PV interneurons consistently fired at an earlier phase in the gamma cycle ({approx}6ms or 60 degrees) than Sst interneurons. Consequently, PV and Sst activity showed differential temporal relations with excitatory cells. In particular, the 1st and 2nd spikes in burst events, which were strongly gamma phase-locked, shortly preceded PV and Sst activity, respectively. These findings indicate a primary role of PV interneurons in synchronizing excitatory cells and suggest that PV and Sst interneurons control the excitability of somatic and dendritic neural compartments with precise time delays coordinated by gamma oscillations.

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