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Vinck, M.

Publications and source records attributed to Vinck, M..

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Distributed representations of prediction error signals across the cortical hierarchy are synergistic

An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. brain signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded redundant and synergistic information during auditory prediction error processing. In both tasks, we observed multiple patterns of synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between auditory and frontal regions. Using a brain-constrained neural network, we simulated the spatio-temporal patterns of synergy and redundancy observed in the experimental results and further demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback and feedforward connections. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.

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Cell-type-specific propagation of visual flicker

Rhythmic flicker stimulation has gained interest as a treatment for neurodegenerative diseases and a method for frequency tagging neural activity in human EEG/MEG recordings. Yet, little is known about the way in which flicker-induced synchronization propagates across cortical levels and impacts different cell types. Here, we used Neuropixels to simultaneously record from LGN, V1, and CA1 while presenting visual flicker stimuli at different frequencies. LGN neurons showed strong phase locking up to 40Hz, whereas phase locking was substantially weaker in V1 units and absent in CA1 units. Laminar analyses revealed an attenuation of phase locking at 40Hz for each processing stage, with substantially weaker phase locking in the superficial layers of V1. Gamma-rhythmic flicker predominantly entrained fast-spiking interneurons. Optotagging experiments showed that these neurons correspond to either PV+ or narrow-waveform Sst+ neurons. A computational model could explain the observed differences in phase locking based on the neurons capacitative low-pass filtering properties. In summary, the propagation of synchronized activity and its effect on distinct cell types strongly depend on its frequency.

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Distinct feedforward and feedback pathways for cell-type specific attention effects in macaque V4.

Spatial attention selectively enhances neural responses to visual stimuli. There are two long-standing hypotheses about how top-down feedback enhances sensory responses in areas like V4: First, by amplifying V1-to-V4 feedforward communication via 30-80Hz gamma-coherence. Second, via top-down feedback to V4 supra- and infra-granular layers. To test these hypotheses, we recorded distinct cell-types across macaque V1 and V4 layers. Attention increased both V1-V4 gamma-coherence and V4 spike-rates, yet with distinct laminar and cell-type profiles. Surprisingly, V1 gamma did not engage V4 excitatory neurons, but only Layer-4 fast-spiking interneurons. Similar observations were made in mouse visual-cortex, where feedforward gamma-influences preferentially recruit optogenetically-tagged PV+ and narrowwaveform SSt+ interneurons. By contrast, attention enhanced V4 spike-rates in both excitatory neurons and fast-spiking interneurons, with the strongest and earliest modulation in Layer-2/3, consistent with a feedback influence. These findings reveal distinct feedforward and feedback pathways for the attentional modulation of inter-areal coherence and spike rates, respectively.

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Sequence anticipation and STDP emerge from a voltage-based predictive learning rule

Intelligent behavior depends on the brains ability to anticipate future events. However, the learning rules that enable neurons to predict and fire ahead of sensory inputs remain largely unknown. We propose a plasticity rule based on predictive processing, where the neuron learns a low-rank model of the synaptic input dynamics in its membrane potential. Neurons thereby amplify those synapses that maximally predict other synaptic inputs based on their temporal relations, which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local information. Consequently, neurons learn sequences over long timescales and shift their spikes towards the first inputs in a sequence. We show that this mechanism can explain the development of anticipatory signalling and recall in a recurrent network. Furthermore, we demonstrate that the learning rule gives rise to several experimentally observed STDP (spike-timing-dependent plasticity) mechanisms. These findings suggest prediction as a guiding principle to orchestrate learning and synaptic plasticity in single neurons.

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The role of anatomical connection strength for interareal communication in macaque cortex

What is the relationship between anatomical connection strength and rhythmic synchronization? Simultaneous recordings of 15 cortical areas in two macaque monkeys show that interareal networks are functionally organized in spatially distinct modules with specific synchronization frequencies, i.e. frequency-specific functional connectomes. We relate the functional interactions between 91 area pairs to their anatomical connection strength defined in a separate cohort of twenty six subjects. This reveals that anatomical connection strength predicts rhythmic synchronization and vice-versa, in a manner that is specific for frequency bands and for the feedforward versus feedback direction, even if interareal distances are taken into account. These results further our understanding of structure-function relationships in large-scale networks covering different modality-specific brain regions and provide strong constraints on mechanistic models of brain function. Because this approach can be adapted to non-invasive techniques, it promises to open new perspectives on the functional organization of the human brain.

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Developmental loss of ErbB4 in PV interneurons disrupts state-dependent cortical circuit dynamics.

GABAergic inhibition plays an important role in the establishment and maintenance of cortical circuits during development. Neuregulin 1 (Nrg1) and its interneuron-specific receptor ErbB4 are key elements of a signaling pathway critical for the maturation and proper synaptic connectivity of interneurons. Using conditional deletions of the ERBB4 gene in mice, we tested the role of this signaling pathway at two developmental timepoints in parvalbumin-expressing (PV) interneurons, the largest subpopulation of cortical GABAergic cells. Loss of ErbB4 in PV interneurons during embryonic, but not late postnatal, development leads to alterations in the activity of excitatory and inhibitory cortical neurons, along with severe disruption of cortical temporal organization. These impairments emerge by the end of the second postnatal week, prior to the complete maturation of the PV interneurons themselves. Early loss of ErbB4 in PV interneurons also results in profound dysregulation of excitatory pyramidal neuron dendritic architecture and a redistribution of spine density at the apical dendritic tuft. In association with these deficits, excitatory cortical neurons exhibit normal tuning for sensory inputs, but a loss of state-dependent modulation of the gain of sensory responses. Together these data support a key role for early developmental Nrg1/ErbB4 signaling in PV interneurons as powerful mechanism underlying the maturation of both the inhibitory and excitatory components of cortical circuits.

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Stimulus-specific plasticity of macaque V1 spike rates and gamma

When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here we show in awake macaque area V1 that both, repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects showed some persistence on the timescale of minutes. Further, gamma increases were specific to the presented stimulus location. Importantly, repetition effects on gamma and on firing rates generalized to natural images. These findings suggest that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters, both for generating efficient stimulus responses and possibly for memory formation.

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Predictability in natural images determines V1 firing rates and synchronization: A deep neural network approach

Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that neural activity results from an integration and comparison of bottom-up inputs with contextual predictions, a process in which firing rates and synchronization may play distinct roles. Here, we quantified stimulus predictability for natural images based on self-supervised, generative neural networks. When the precise pixel structure of a stimulus falling into the V1 receptive field (RF) was predicted by the spatial context, V1 exhibited characteristic{gamma} -synchronization (30-80Hz), despite no detectable modulation of firing rates. In contrast to{gamma} , {beta}-synchronization emerged exclusively for unpredictable stimuli. Natural images with high structural predictability were characterized by high compressibility and low dimensionality. Yet, perceptual similarity was mainly determined by higher-level features of natural stimuli, not by the precise pixel structure. When higher-level features of the stimulus in the receptive field were predicted by the context, neurons showed a strong reduction in firing rates and an increase in surround suppression that was dissociated from synchronization patterns. These findings reveal distinct roles of synchronization and firing rates in the predictive coding of natural images.

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A general theory of coherence between brain areas

What does neuronal coherence tell us about neuronal communication? Does coherence between field potentials (e.g. LFP, EEG, MEG) reflect spiking entrainment or coupling between oscillators? Is it a mechanism for communication between brain areas, or a byproduct of interareal connectivity and spectral power? We hypothesized that interareal coherence is explained by the fact that outputs from one cortical area give rise to synaptic inputs in the same brain area, and correlated synaptic inputs in another area. Our mathematical analysis demonstrates that coherence between a sending and receiving area is precisely predicted from only two parameters: Interareal connectivity and oscillatory synchronization in the sending area. This model predicts narrow-band coherence even in case of a flat transfer function and in the absence of spiking entrainment in a downstream area, and reproduces frequency-specific Granger-causality patterns between brain areas (gamma feedforward, beta feedback). In general, we find that Granger-causality between field potentials is dominated by oscillatory synchronization in the sending area, whereas spiking entrainment is determined by the resonant properties of the receiver. Our model accurately reproduces LFP-LFP beta-coherence between macaque areas 7B and F5 in the absence of beta phase-locking within area F5. Together, these findings uncover a precise mechanistic model of interareal coherence as a (by)product of connectivity and power.

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A geometry of spike sequences: Fast, unsupervised discovery of high-dimensional neural spiking patterns based on optimal transport theory

Neural coding and memory formation depend on temporal spiking sequences that span high-dimensional neural ensembles. The unsupervised discovery and characterization of these spiking sequences requires a suitable dissimilarity measure to spiking patterns, which can then be used for clustering and decoding. Here, we present a new dissimilarity measure based on optimal transport theory called SpikeShip, which compares multineuron spiking patterns based on all the relative spike-timing relationships among neurons. SpikeShip computes the optimal transport cost to make all the relative spike-timing relationships (across neurons) identical between two spiking patterns. We show that this transport cost can be decomposed into a temporal rigid translation term, which captures global latency shifts, and a vector of neuron-specific transport flows, which reflect inter-neuronal spike timing differences. SpikeShip can be effectively computed for high-dimensional neuronal ensembles, has a low (linear) computational cost that has the same order as the spike count, and is sensitive to higher-order correlations. Furthermore SpikeShip is binless, can handle any form of spike time distributions, is not affected by firing rate fluctuations, can detect patterns with a low signal-to-noise ratio, and can be effectively combined with a sliding window approach. We compare the advantages and differences between SpikeShip and other measures like SPIKE and Victor-Purpura distance. We applied SpikeShip to large-scale Neuropixel recordings during spontaneous activity and visual encoding. We show that high-dimensional spiking sequences detected via SpikeShip reliably distinguish between different natural images and different behavioral states. These spiking sequences carried complementary information to conventional firing rate codes. SpikeShip opens new avenues for studying neural coding and memory consolidation by rapid and unsupervised detection of temporal spiking patterns in high-dimensional neural ensembles.

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Cortical Hierarchy and The Dual Counterstream Architecture.

Hierarchy is a major organizational principle of the cortex and underscores modern computational theories of cortical function. The local microcircuit amplifies long-distance inter-areal input, which show distance-dependent changes in their laminar profiles. Statistical modeling of these changes in laminar profiles demonstrates that inputs from multiple hierarchical levels to their target areas show remarkable consistency, allowing the construction of a cortical hierarchy based on a principle of hierarchical distance. The statistical modeling that is applied to structure can also be applied to laminar differences in the oscillatory coherence between areas thereby determining a functional hierarchy of the cortex. Close examination of the anatomy of inter-areal connectivity reveals a dual counterstream architecture with well-defined distance-dependent feedback and feedforward pathways in both the supra- and infragranular layers, suggesting a multiplicity of feedback pathways with well-defined functional properties. These findings are consistent with feedback connections providing a generative network involved in a wide range of cognitive functions. A dynamical model constrained by connectivity data shed insights into the experimentally observed signatures of frequency-dependent Granger causality for feedforward versus feedback signaling. Concerted experiments capitalizing on recent technical advances and combining tract-tracing, high-resolution fMRI, optogenetics and mathematical modeling hold the promise of a much improved understanding of lamina-constrained mechanisms of neural computation and cognition. However, because inter-areal interactions involve cortical layers that have been the target of important evolutionary changes in the primate lineage, these investigations will need to include human and non-human primates comparisons. PlanO_LIIntroduction C_LIO_LIHierarchy - signatures of inputs to the local circuits. C_LIO_LIModels of hierarchy C_LIO_LIHierarchy - input consistency C_LIO_LIDual stream architecture. C_LIO_LIFunctional characteristics of FF and FB pathways. C_LIO_LIThe predictive brain and the importance of top-down generative networks. C_LIO_LIConclusion. C_LI

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Gamma-cycle duration predicts instantaneous amplitude, spike rate and synchrony in macaque V1.

Circuits of excitatory and inhibitory neurons can generate rhythmic activity in the gamma frequency-range (30-80Hz). Individual gamma-cycles show spontaneous variability in amplitude and duration. The mechanisms underlying this variability are not fully understood. We recorded local-field-potentials (LFPs) and spikes from awake macaque V1, and developed a noise-robust method to detect gamma-cycle amplitudes and durations. Amplitudes and durations showed a weak but positive correlation. This correlation, and the joint amplitude-duration distribution, is well reproduced by a dampened harmonic oscillator driven by stochastic noise. We show that this model accurately fits LFP power spectra and is equivalent to a linear PING (Pyramidal Interneuron Network Gamma) circuit. The model recapitulates two additional features of V1 gamma: (1) Amplitude-duration correlations decrease with oscillation strength; (2) Amplitudes and durations exhibit strong and weak autocorrelations, respectively, depending on oscillation strength. Finally, longer gamma-cycles are associated with stronger spike-synchrony, but lower spike-rates in both (putative) excitatory and inhibitory neurons. In sum, V1 gamma-dynamics are well described by the simplest possible model of gamma: A linear harmonic oscillator driven by noise.

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A distinct class of bursting neurons with strong gamma synchronization and stimulus selectivity in monkey V1

Cortical computation depends on interactions between excitatory and inhibitory neurons. The contributions of distinct neuron-types to sensory processing and network synchronization in primate visual-cortex remain largely undetermined. We show that in awake monkey V1, there exists a distinct cell-type ({approx}30% of neurons) that has narrow-waveform action-potentials, high spontaneous discharge-rates, and fires in high-frequency bursts. These neurons are more stimulus-selective and phase-locked to gamma (30-80Hz) oscillations as compared to other neuron types. Unlike the other neuron-types, their gamma phase-locking is highly predictive of their orientation tuning. We find evidence for strong rhythmic inhibition in these neurons, suggesting that they interact with interneurons to act as excitatory pacemakers for the V1 gamma rhythm. These neurons have not been observed in other primate cortical areas and we find that they are not present in rodent V1. However, they resemble the excitatory \"chattering\" neurons previously identified by intracellular recordings in cat V1. Given its properties, this neuron type should be pivotal for the encoding and transmission of V1 stimulus information.

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Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations

The integration of direct bottom-up inputs with contextual information is a canonical motif in neocortical circuits. In area V1, neurons may reduce their firing rates when the (classical) receptive field input can be predicted by the spatial context. We previously hypothesized that gamma-synchronization (30-80Hz) provides a complementary signal to rates, encoding whether stimuli are predicted from spatial context by preferentially synchronizing neuronal populations receiving predictable inputs. Here we investigated how rates and synchrony are modulated by predictive context. Large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma-synchronization, but only when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma-synchronization while increasing firing rates. Differences between colors, including strong gamma-responses to red, arose because of stimulus adaptation to a full-screen background, with a prominent difference in adaptation between M- and L-cone signaling pathways. Thus, synchrony signals whether RF inputs are predicted from spatial context and may encode relationships across space, while firing rates increase when stimuli are unpredicted from the context.

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Functional determinants of enhanced and depressed inter-areal information flow in NREM sleep between neuronal ensembles in rat cortex and hippocampus

Compared to wakefulness, neuronal activity during non-REM sleep is characterized by a decreased ability to integrate information, but also by the re-emergence of task-related information patterns. To investigate the mechanisms underlying these seemingly opposing phenomena, we measured directed information flow by computing transfer entropy between neuronal spiking activity in three cortical regions and the hippocampus of rats across brain states. State-dependent information flow resulted to be jointly determined by the anatomical distance between neurons and by their functional specialization. We distinguished two regimes, operating at short and long time scales, respectively. From wakefulness to non-REM sleep, transfer entropy at short time scales increased for inter-areal connections between neurons showing behavioral task correlates. Conversely, transfer entropy at long time scales became stronger between non-task modulated neurons and weaker between task- modulated neurons. These results may explain how, during non-REM sleep, a global inter-areal disconnection is compatible with highly specific task-related information transfer.\n\nAuthor SummaryThe brain remains active during deep sleep, yet we still do not know which rules govern information processing between neurons across wakefulness and sleep. Here we provide a first study of how information flow at the level of spiking activity varies as a function of brain state, temporal scale, brain area and behavioral task correlates of single neurons. We found that inter-areal communication at millisecond time scales is enhanced during sleep compared to wakefulness between neurons that code for task information. Conversely, non-modulated neurons showed more prominent communication at longer time scales. These results indicate that multiple, functionally determined communicative architectures coexist in the brain, and provide a novel framework to understand information processing and its consequences during sleep.

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A novel distance measure for the unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles

Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. SPOTDisClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDis). Then, it performs density-based clustering based on the resulting inter-pattern dissimilarity matrix. SPOTDisClust does not require binning and can detect complex patterns (beyond sequential activation) even when high levels of out-of-pattern \"noise\" spiking are present. Our method handles efficiently the additional information from increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns.\n\nAuthor summaryThe brain encodes information by ensembles of neurons, and recent technological developments allow researchers to simultaneously record from over thousands of neurons. Neurons exhibit spontaneous activity patterns, which are constrained by experience and development, limiting the portion of state space that is effectively visited. Patterns of spontaneous activity may contribute to shaping the synaptic connectivity matrix and contribute to memory consolidation, and synaptic plasticity formation depends crucially on the temporal spiking order among neurons. Hence, the unsupervised detection of spike sequences is a sine qua non for understanding how spontaneous activity contributes to memory formation. Yet, sequence detection presents major methodological challenges like the sparsity and stochasticity of neuronal output, and its high dimensionality. We propose a dissimilarity measure between neuronal patterns based on optimal transport theory, determining their similarity from the pairwise cross-correlation matrix, which can be taken as a proxy of the \"trace\" that is left on the synaptic matrix. We then perform unsupervised clustering and visualization of patterns using density clustering on the dissimilarity matrix and low-dimensional embedding techniques. This method does not require binning of spike times, is robust to noise, jitter and rate fluctuations, and can detect more patterns than the number of neurons.

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