Biology subjects

Mejias, J. F.

Publications and source records attributed to Mejias, J. F..

At least 19 recordsLinked to original sources

bioRxiv2025

Functional connectivity drifts during sleep as a marker of fluctuations in the level of consciousness

During the wake-sleep cycle, consciousness waxes and wanes, and this is thought to be reflected in varying levels of integration between brain areas. Recent studies challenged the notion that consciousness is homogeneously present or absent in a brain state, as exemplified by conscious reports found in otherwise unconscious Non-REM sleep. We tested if functional connectivity between neurons varies within brain states in a way compatible with a fluctuating level of consciousness. We examined directed functional connectivity between neurons across the wake-sleep cycle in rats, at a scale of a few seconds. We analyzed patterns of functional connectivity to determine if Non-REM sleep contains epochs in which inter-areal integration is comparable to that observed in wakefulness and REM sleep, and vice versa. This study will potentially reveal if circuit-level connectivity patterns are observed during sleep stages, in line with the presence of an alternation between levels of consciousness not only between but also within brain states.

neuroscience↗

bioRxiv2025

A dynamical systems model of arousal-driven behavioural state transitions

When completing a task, animals switch between being engaged and disengaged. What neural and physiological processes trigger these behavioural state transitions? We estimated the engagement state of mice performing a visual decision-making task using a hidden Markov model of response times and found that intermediate arousal, as measured using baseline pupil size, was associated with more engagement in the task. Additionally, we show that changes in arousal (both mean and variability of the pupil baseline) predict changes in behavioural state. To explain this, we propose a double-well model, in which arousal causes behavioural state transitions by reshaping the attractor landscape of population neural activity. These results highlight a possible mechanism of arousal-related changes in behaviour.

animal behavior and cognition↗

bioRxiv2025

Mesoscale imaging of the human cerebellum reveals converging regional specialization of its morphology, vasculature and cytoarchitecture.

The human cerebellar cortex contains the highest density and number of neurons in the human brain, yet this thin and tightly-folded structure has remained largely inaccessible to in-vivo imaging. We introduce an imaging framework to enable high-resolution, comprehensive imaging of the human cerebellar cortex. We validated the in-vivo estimates of cortical morphology against post-mortem measures. Crucially, our findings challenge the commonly held view of the cerebellar cortex as being uniform. Our findings reveal interlobular heterogeneity in both cortical morphology and vascular organization. We demonstrate that this spatial heterogeneity correlates with granular layer cell density. This convergence of morphology, cytoarchitecture and vascularization offers new mechanistic insights and reframes how cerebellar structure and function should be interpreted in health and disease.

neuroscience↗

bioRxiv2025

A computational architecture incorporating shallow brain networks: integrating parallel cortical and subcortical processing

Artificial neural networks commonly have deep hierarchical structures that were originally inspired by the neuroanatomical evidence of cortico-cortical connectivity pattern found in the mammalian brain. Largely neglected in those models are non-hierarchical aspects of brain architecture, namely the subcortical pathways and the interactions between cortical and subcortical areas regardless of their hierarchical locations. Inspired by this principle, we present a computational model combining cortical hierarchical processing with subcortical pathways based on neuroanatomical evidence. We show the versatility of our model by implementing the cortical hierarchy in two alternative ways--a convolutional feedforward network and a predictive coding network. Both model variants can replicate behavioral observations in humans and monkeys on a perceptual context-dependent decision-making task. The model also reveals that subcortical structures lead decisions for easy trials while the more complex hierarchical network is necessary for the harder trials. Our results suggest that the parallel cortico-subcortical processing explored in the model represents a fundamental property that cannot be neglected in understanding the computational principles used by the brain. SignificanceArtificial intelligence and computational neuroscience models, particularly deep learning and predictive coding architectures, have been traditionally dominated by cortico-centric hierarchical frameworks. However, extensive neurobiological evidence suggests that cortical areas, regardless of their hierarchical classification, are deeply interconnected with subcortical structures in real brains. We propose here a computational framework demonstrating that parallel cortical-subcortical architectures can yield complex and more flexible computational capabilities, aligning with the observed behavior in the mammalian brain. Our model addresses key limitations in existing deep learning and predictive coding networks, offering a more biologically plausible and functionally advantageous alternative.

neuroscience↗

bioRxiv2024

Efficient laminar-distributed interactions and orientation selectivity in the mouse V1 cortical column

The emergence of orientation selectivity in the visual cortex is a well-known phenomenon in neuroscience, but the details of such emergence and the role of different cortical layers and cell types, particularly in rodents which lack a topographical organization of orientation-selectivity (OS) properties, are less clear. To tackle this question, we use an existing biologically detailed model of the mouse V1 cortical column, which is constrained by existing connectivity data across cortical layers and between pyramidal, PV, SST and VIP cell types. Using this model as a basis, we implemented activity-dependent structural plasticity induced by stimulation with orientated drifting gratings, leading to a good match of tuning properties of pyramidal cells with experimentally observed OS laminar distribution, their evoked firing rate and tuning width. We then employed a mean-field model to uncover the role of co-tuned subnetworks in laminar signal propagation and explain the effects of intra- and inter-laminar coupling distributions. Our plasticity-induced modified model and mean-field model were able to explain both the excitatory enhancement through co-tuned subnetworks and inter-laminar disynaptic inhibition. Overall, our work highlights the importance of the clustering of neural selectivity features for effective excitatory transmission in cortical circuits.

neuroscience↗

bioRxiv2024

Cortical networks with multiple interneuron types generate oscillatory patterns during predictive coding

Predictive coding (PC) proposes that our brains work as an inference machine, generating an internal model of the world and minimizing predictions errors (i.e., differences between external sensory evidence and internal prediction signals). Theoretical models of PC often rely on high-level approaches, and therefore implementations detailing which neurons or pathways are used to compute prediction errors or adapt the internal representations, as well as their level of agreement with biological circuitry, are currently missing. Here we propose a computational model of PC, which integrates a neuroanatomically informed hierarchy of cortical areas with a precise laminar organization and cell-type-specific connectivity between pyramidal, PV, SST and VIP cells. Our model efficiently performs PC, even in the presence of external and internal noise, by forming latent representations of naturalistic visual input (MNIST, fashion-MNIST and grayscale CIFAR-10) via Hebbian learning and using them to predict sensory input by minimizing prediction errors. The model assumes that both positive and negative prediction errors are computed by stereotypical pyramidal-PV-SST-VIP circuits with the same structure but different incoming input. During sensory inference, neural oscillatory activity emerges in the system due to interactions between representation and prediction error microcircuits, with optogenetics-inspired inactivation protocols revealing a differentiated role of PV, SST and VIP cell types in such dynamics. Finally, our model shows anomalous responses to deviant stimuli within series of same-image presentations, in agreement with experimental results on mismatch negativity and oddball paradigms. We argue that our model constitutes an important step to better understand the circuits mediating PC in real cortical networks. Author summaryPredictive coding (PC) suggests that the brain constantly generates expectations about the world and updates these expectations based on incoming sensory input. While this theory is widely accepted, we still lack detailed models that show how specific neurons and brain circuits might carry out these processes. Here, we present a computational model which addresses this gap by including biologically plausible brain circuitry with specific types of neurons (pyramidal, PV, SST, and VIP cells) and their connections. It efficiently learns to form internal representations of visual information and uses them to predict sensory input, adjusting its predictions when errors occur. We found that particular types of neurons play different roles in these processes, and that neural oscillations emerge during the training and inference processes. Our model also replicates neural patterns observed in experiments where unexpected stimuli appear. By integrating anatomical and functional details, our work brings us closer to understanding how the brain uses predictive coding at the circuit level.

neuroscience↗

bioRxiv2024

Adaptive arousal regulation: Pharmacologically shifting the peak of the Yerkes-Dodson curve by catecholaminergic enhancement of arousal

Performance typically peaks at moderate arousal levels, consistent with the Yerkes-Dodson law, as confirmed by recent human and mouse pupillometry studies. Arousal states are influenced by neuromodulators like catecholamines (noradrenaline; NA and dopamine; DA) and acetylcholine (ACh). To explore their causal roles in this law, we pharmacologically enhanced arousal while measuring human decision-making and spontaneous arousal fluctuations via pupil size. The catecholaminergic agent atomoxetine (ATX) increased overall arousal and shifted the entire arousal-performance curve, suggesting a relative arousal mechanism where performance adapts to arousal fluctuations within arousal states. In contrast, the cholinergic agent donepezil (DNP) did not affect arousal or the curve. We modeled these findings in a neurobiologically plausible computational framework, showing how catecholaminergic modulation alters a disinhibitory neural circuit that encodes sensory evidence for decision-making. This work suggests that performance adapts flexibly to arousal fluctuations, ensuring optimal performance in each and every arousal state.

neuroscience↗

bioRxiv2024

Cell type specific firing patterns in a V1 cortical column model depend on feedforward and feedback activity

Stimulation of specific cell groups under different network regimes (e.g., spontaneous activity or sensory-evoked activity) can provide insights into the neural dynamics of cortical columns. While these protocols are challenging to perform experimentally, modelling can serve as a powerful tool for such explorations. Using detailed electrophysiological and anatomical data from mouse V1, we built a novel spiking network model of a cortical column, which incorporates pyramidal cells and three distinct interneuron types (PV, SST, and VIP cells, specified per lamina), as well as the dynamic and voltage-dependent properties of AMPA, GABA, and NMDA receptors. We first demonstrate that thalamocortical feedforward (FF) and feedback (FB) stimuli arriving in the column have opposite effects, leading to net columnar excitation and inhibition respectively and revealing translaminar gain control via full-column inhibition by layer 6. We then perturb one cell group (i.e. a cell type in a specific layer) at a time and observe the effects on other cell groups under distinct network states: spontaneous, feedforward-driven, feedback-driven, and a combination of feedforward and feedback. Our findings reveal that when a given group is perturbed, the columnar response varies significantly based on its state, with strong sensory feedforward input decreasing columnar sensitivity to all perturbations and feedback input serving as modulator of intra columnar interactions. Given that activity changes within specific neuronal populations are difficult to predict a priori in experiments, our model may constitute a useful tool to predict outcomes of perturbations and assist in experimental design. Author SummaryIn this study, we explored how stimulating specific groups of neurons under different conditions (spontaneous or stimulus-evoked states) can help us understand the dynamics of visual cortical columns in mouse cortex. We developed a data-constrained model of a cortical column that includes several cell types (excitatory neurons as well as multiple inhibitory neuron types) and synaptic receptors. Our findings reveal that signals coming from higher brain areas (feedback input) and sensory input (feedforward input) have different impacts on the column: sensory inputs generally increase activity within the column, while feedback tends to decrease it. We then considered input targeting one cell type at a time to see how it affects the internal dynamics. Our results show that neural responses to selected input vary depending on whether the cortical column is in a resting state or being stimulated by feedforward input or feedback input. This model is a useful tool for predicting the outcomes of these cell interactions, which can help in planning real-world experiments and understanding how our brain processes information.

neuroscience↗

bioRxiv2023

Distributed evidence accumulation across macaque large-scale neocortical networks during perceptual decision making

Despite the traditional view of parietal cortex as an important region for perceptual decision-making, recent evidence suggests that sensory accumulation occurs simultaneously across many cortical regions. We explored this hypothesis by integrating connectivity, cellular and receptor density datasets and building a large-scale macaque cortical model able to integrate conflicting sensory signals and perform a decision-making task. Our results reveal sensory evidence accumulation supported by a distributed network of temporal, parietal and frontal regions, with flexible sequential bottom-up or top-down modulation pathways depending on task difficulty. The model replicates experimental lesioning effects and reveals that the causal irrelevance of parietal areas like LIP for decision performance is explained by compensatory mechanisms within a distributed integration process. The model also reproduces observed temporal gating effects of distractor timing during and after the integration process. Overall, our work hints at perceptual integration during decision-making as a broad distributed phenomenon, providing multiple testable predictions.

neuroscience↗

bioRxiv2023

Diverse and flexible behavioral strategies arise in recurrent neural networks trained on multisensory decision making

Behavioral variability across individuals leads to substantial performance differences during cognitive tasks, although its neuronal origin and mechanisms remain elusive. Here we use recurrent neural networks trained on a multisensory decision-making task to investigate inter-subject behavioral variability. By uniquely characterizing each network with a random synaptic-weights initialization, we observed a large variability in the level of accuracy, bias and decision speed across these networks, mimicking experimental observations in mice. Performance was generally improved when networks integrated multiple sensory modalities. Additionally, individual neurons developed modality-, choice- or mixed-selectivity, these preferences were different for excitatory and inhibitory neurons, and the concrete composition of each network reflected its preferred behavioral strategy: fast networks contained more choice- and mixed-selective units, while accurate networks had relatively less choice-selective units. External modulatory signals shifted the preferred behavioral strategies of networks, suggesting an explanation for the recently observed within-session strategy alternations in mice.

neuroscience↗

bioRxiv2023

Synaptic plasticity is required for oscillations in a V1 cortical column model with multiple interneuron types

Neural rhythms are ubiquitous in cortical recordings, but it is unclear whether they emerge due to the basic structure of cortical microcircuits, or depend on function. Using detailed electrophysiological and anatomical data of mouse V1, we explored this question by building a spiking network model of a cortical column incorporating pyramidal cells, PV, SST and VIP inhibitory interneurons, and dynamics for AMPA, GABA and NMDA receptors. The resulting model matched in vivo cell-type-specific firing rates for spontaneous and stimulus-evoked conditions in mice, although rhythmic activity was absent. Upon introduction of long-term synaptic plasticity in the form of an STDP rule, broad-band (15-60 Hz) oscillations emerged, with feedforward/feedback input streams enhancing/suppressing the oscillatory drive, respectively. These plasticity-triggered rhythms relied on all cell types, and specific experience-dependent connectivity patterns were required to generate oscillations. Our results suggest that neural rhythms are not necessarily intrinsic properties of cortical circuits, but rather they may arise from structural changes elicited by learning-related mechanisms.

neuroscience↗

bioRxiv2023

A disinhibitory circuit mechanism explains a general principle of peak performance during mid-level arousal

Perceptual decision-making is highly dependent on the momentary arousal state of the brain, which fluctuates over time on a scale of hours, minutes, and even seconds. The textbook relationship between momentary arousal and task performance is captured by an inverted U-shape, as put forward in the Yerkes-Dodson law (Yerkes and Dodson, 1908). This law suggests optimal performance at moderate levels of arousal, and impaired performance at low or high arousal levels. However, despite its popularity, the evidence for this relationship in humans is mixed at best. Here, we use pupil-indexed arousal and performance data from various perceptual decision-making tasks to provide converging evidence for the inverted U-shaped relationship between spontaneous arousal fluctuations and performance across different decision types (discrimination, detection) and sensory modalities (visual, auditory). To further understand this relationship, we built a neurobiologically plausible mechanistic model and show that it is possible to reproduce our findings by incorporating two types of interneurons that are both modulated by an arousal signal. The model architecture produces two dynamical regimes under the influence of arousal: one regime in which performance increases with arousal, and another regime in which performance decreases with arousal, together forming an inverted U-shaped arousal-performance relationship. We conclude that the inverted U-shaped arousal-performance relationship is a general and robust property of sensory processing. It might be brought about by the influence of arousal on two types of interneurons that together act as a disinhibitory pathway for the neural populations that encode the available sensory evidence used for the decision. Significance statementWhen people are repeatedly performing the exact same task, their performance fluctuates over time. One of the causes of this behavioral variability is spontaneous fluctuations in arousal. According to the Yerkes-Dodson law, task performance is optimal at moderate levels of species arousal, with impaired performance at very low or high arousal levels. However, until now, the evidence supporting this law as a general mechanism in human decision-making is mixed, and a neural mechanism that may explain the inverted U-shaped arousal-performance relationship is lacking. We show that the Yerkes-Dodson law is a general law that holds for human observers across decision-making tasks and settings. Furthermore, we present a simple and neurobiologically plausible mechanistic model that can explain its existence.

neuroscience↗

bioRxiv2023

Emergence of distributed working memory in a human brain network model

Working memory is a fundamental cognitive function which allows to transiently store and manipulate relevant information in memory. While it has been traditionally linked to activity in specific prefrontal cortical areas, recent electrophysiological and imaging evidence has shown co-occurrent activities in different brain regions during working memory. To dissect the mechanisms behind the emergence of such distributed working memory activity in the human brain, we built and analyzed a detailed, data-constrained model of the human brain combining multiple sources of large-scale brain data. The model reveals the emergence of distributed working memory patterns which rely on long-range synaptic projections, differing from classical models where working memory emerges due to local recurrent connectivity. When compared to existing evidence regarding areas linked to working memory, our model explains almost 80% of the observed variability across brain areas. Our results predict important differences between the distributed working memory in human and non-human primates, and propose a potential solution for the debate regarding the role of early visual areas in working memory encoding. This work provides a novel approach to simulate the human brain which combines the richness of large-scale brain dynamics with the insight of mechanistic models of cognitive functions.

neuroscience↗

bioRxiv2022

Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception

The ventral visual processing hierarchy of the cortex needs to fulfill at least two key functions: Perceived objects must be mapped to high-level representations invariantly of the precise viewing conditions, and a generative model must be learned that allows, for instance, to fill in occluded information guided by visual experience. Here, we show how a multilayered predictive coding network can learn to recognize objects from the bottom up and to generate specific representations via a top-down pathway through a single learning rule: the local minimization of prediction errors. Trained on sequences of continuously transformed objects, neurons in the highest network area become tuned to object identity invariant of precise position, comparable to inferotemporal neurons in macaques. Drawing on this, the dynamic properties of invariant object representations reproduce experimentally observed hierarchies of timescales from low to high levels of the ventral processing stream. The predicted faster decorrelation of error-neuron activity compared to representation neurons is of relevance for the experimental search for neural correlates of prediction errors. Lastly, the generative capacity of the network is confirmed by reconstructing specific object images, robust to partial occlusion of the inputs. By learning invariance from temporal continuity within a generative model, despite little change in architecture and learning rule compared to static input- reconstructing Hebbian predictive coding networks, simply by shifting the training paradigm to dynamic inputs, the approach generalizes the predictive coding framework to dynamic inputs in a more biologically plausible way than self-supervised networks with non-local error-backpropagation. Author SummaryNeurons in the inferotemporal cortex of primates respond to images of complex objects independent of position, rotational angle, or size. While feedforward models of visual perception such as deep neural networks can explain this, they fail to account for the use of top-down information, for example when sensory evidence is scarce. Here, we address the question of how the neuronal networks in the brain learn both bottom-up and top-down processing without labels as they are used in the artificial supervised learning paradigm. Building on previous work that explains vision as a process of iteratively improving predictions, learning in the predictive coding network is driven by the local minimization of prediction errors. When trained on sequences of moving inputs, the network learns both invariant high-level representations comparable to those in the inferotemporal cortex of primates, and a generative model capable of reconstructing whole objects from partially occluded input images in agreement with experimental recordings from early visual areas. Advancing the search for experimental hallmarks of prediction errors, we find that error neurons in the higher areas of the network change their activity on a shorter timescale than representation neurons.

neuroscience↗

bioRxiv2022

Spontaneous Variations in Arousal Modulate Subsequent Visual Processing and Local Field Potential Dynamics in the Ferret during Quiet Wakefulness

Behavioral states affect neuronal responses throughout the cortex and influence visual processing. Quiet wakefulness (QW) is a behavioral state during which subjects are quiescent, but awake and connected to the environment. Here, we examined the effects of pre-stimulus arousal variability on post-stimulus neural activity in primary visual cortex (V1) and posterior parietal cortex (PPc) in awake ferrets, using the pupil diameter as an indicator of arousal. We observed that during low arousal, low- frequency power increases during visual stimulation, and that the peak alpha frequency shifted depending on the arousal state. High arousal increased gamma power as well as low-frequency inter- and intra-areal coherence. Using a simplified model of laminar circuits, we show that this connectivity pattern is compatible with feedback signals targeting infragranular layers in area PPc and supragranular layers in V1. Neurons in V1 displayed higher firing rates at their preferred orientations on high-arousal trials. Broad-spiking cells in V1 entrained to high-frequency oscillations (>80 Hz), whereas narrow-spiking neurons phase-locked to low (12-18 Hz) and high-frequency (>80 Hz) rhythms. These results indicate that the variability and sensitivity of post-stimulus cortical responses and coherence depend on the pre-stimulus behavioral state and account for the neuronal response variability observed during repeated stimulation.

neuroscience↗

bioRxiv2022

A connectome-based model of conscious access in monkey cortex

A growing body of evidence suggests that conscious perception of a sensory stimulus coincides with all-or-none activity across multiple cortical areas, a phenomenon called ignition. In contrast, the same stimulus, when undetected, induces only transient activity. In this work, we report a large-scale model of the macaque cortex based on recently quantified structural mesoscopic connectome data. We use this model to simulate a detection task, and demonstrate how a dynamical bifurcation mechanism produces ignition-like events in the model network. The model predicts that feedforward excitatory transmission is primarily mediated by the fast AMPA receptors to ensure rapid signal propagation from sensory to associative areas. In contrast, a greater proportion of the inter-areal feedback projections and local recurrent excitation depend on the slow NMDA receptors, to ensure ignition of distributed frontoparietal activity. Our model predicts, counterintuitively, that fast-responding sensory areas contain a higher ratio of NMDA to AMPA receptors compared to association cortical areas that show slow, sustained activity. We validate this prediction using cortex-wide in-vitro receptor autoradiography data. Finally, we show how this model can account for various behavioral and physiological effects linked to consciousness. Together, these findings clarify the neurophysiological mechanisms of conscious access in the primate cortex and support the concept that gradients of receptor densities along the cortical hierarchy contribute to distributed cognitive functions.

neuroscience↗

bioRxiv2021

Inferring large-scale structural connectivity from local activity in a model of the mouse large-scale cortical network

AO_SCPLOWBSTRACTC_SCPLOWInferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the Generalized Partial Directed Coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. The model contains 19 cortical areas comprised of spiking neurons, with areas connected by long-range projections with weights obtained from a tract-tracing cortical connectome. We show that GPDC values provide a reasonable estimate of structural connectivity, with an average Pearson correlation over simulations of 0.74. Moreover, even in a typical electrophysiological recording scenario containing five areas, the mean correlation was above 0.6. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

neuroscience↗

bioRxiv2020

Cerebellar Purkinje cells can differentially modulate coherence between sensory and motor cortex depending on region and behavior

Coherence among sensory and motor cortices is indicative of binding of critical functions in perception, motor planning, action and sleep. Evidence is emerging that the cerebellum can impose coherence between cortical areas, but how and when it does so is unclear. Here, we studied coherence between primary somatosensory (S1) and motor (M1) cortices during sensory stimulation of the whiskers in the presence and absence of optogenetic stimulation of cerebellar Purkinje cells in awake mice. Purkinje cell activation enhanced and reduced sensory-induced S1-M1 coherence in the theta and gamma bands, respectively. This impact only occurred when Purkinje cell stimulation was given simultaneously with sensory stimulation; a 20 ms delay was sufficient to alleviate its impact, suggesting the existence of a fast, cerebellar sensory pathway to S1 and M1. The suppression of gamma band coherence upon Purkinje cell stimulation was significantly stronger during trials with relatively large whisker movements, whereas the theta band changes did not show this correlation. In line with the anatomical distribution of the simple spike and complex spike responses to whisker stimulation, this suppression also occurred following focal stimulation of medial crus 2, but not of lateral crus 1. Granger causality analyses and computational modeling of the involved networks suggest that Purkinje cells control S1-M1 coherence most prominently via the ventrolateral thalamus and M1. Our results indicate that coherences between sensory and motor cortices in different frequency ranges can be dynamically modulated by cerebellar input, and that the modulation depends on the behavioral context and is site-specific. Significance StatementCoherent activity between sensory and motor areas is essential in sensorimotor integration. We show here that the cerebellum can differentially affect cortical theta and gamma band coherences evoked by whisker stimulation via a fast ascending and predictive pathway. In line with the functional heterogeneity of its modular organization, the impact of the cerebellum is region-specific and tuned to ongoing motor responses. These data highlight site-specific and context-dependent interactions between the cerebellum and the cerebral cortex that can come into play during a plethora of sensorimotor functions.

neuroscience↗