Biology subjects

Aru, J.

Publications and source records attributed to Aru, J..

10 recordsLinked to original sources

bioRxiv2025

Electrophysiological Mechanisms of Psychedelic Drugs: A Systematic Review

Serotonergic psychedelics are known for their profound effects on consciousness and are gaining renewed interest as potential psychiatric treatments. These advances underscore the need to clarify the mechanisms of action of these compounds. This systematic review compiles and critically evaluates 23 in vitro and 26 in vivo electrophysiological studies on psychedelic compounds, with an emphasis on layer 5 pyramidal neurons in the prefrontal cortex, where 5-HT2A receptors are densely expressed. Our findings reveal that psychedelics exert complex, heterogeneous effects on neuronal excitability, synaptic transmission, and local oscillations. These results challenge the simplified view that psychedelics uniformly increase cortical excitability. Instead, they modulate both excitatory and inhibitory processes in a cell-type- and compartment-specific manner, with evidence for biphasic, dose-dependent, and context-sensitive responses. Activation of 5-HT2A receptors leads to intricate calcium signaling, downregulating excitatory currents and firing rates in many neurons, while enhancing glutamate release and activating a subset of projection fibers. Modulation of presynaptic and extrasynaptic GluN2B-containing NMDA receptors appears central to these effects, and some indirect evidence supports the involvement of intracellular 5-HT2A receptors. These insights prompt a reassessment of prevailing models of psychedelic action and underscore the value of incorporating electrophysiological data into psychedelic neuropharmacology.

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bioRxiv2025

Comparing a computational model of visual problem solving with human vision on a difficult vision task.

Human vision is not merely a passive process of interpreting sensory input but can also function as a problem-solving process incorporating generative mechanisms to interpret ambiguous or noisy data. This synergy between the generative and discriminative components, often described as analysis-by-synthesis, enables robust perception and rapid adaptation to out-of-distribution inputs. In this work, we investigate a computational implementation of the analysis-by-synthesis paradigm using genetic search in a generative model, applied to a visual problem-solving task inspired by star constellations. The search is guided by low-level cues based on the structural fitness of candidate solutions compared to the test images. This dataset serves as a testbed for exploring how inferred signals can guide the synthesis of suitable solutions in ambiguous conditions, framing visual inference as an instance of complex problem solving. Drawing on insights from human experiments, we develop a generative search algorithm and compare its performance to humans, examining factors such as accuracy, reaction time, and overlap in drawings. Our results shed light on possible mechanisms of human visual problem solving and highlight the potential of generative search models to emulate aspects of this process. Author summaryHuman vision is not just about passively receiving information from the environment. Rather, it also involves actively making sense of what we see. When faced with unclear or incomplete visual input, our brains use prior knowledge to fill in gaps and create the most likely interpretation. This ability helps us recognize objects and patterns even in difficult conditions. In this study, we explore how this process can be replicated using computer models. Specifically, we test a method that generates possible interpretations of ambiguous visual data, inspired by the way people recognize star constellations. By comparing the models performance with human participants, we examine how well it mirrors human perception. We analyze factors such as accuracy, response time, and similarities in the interpretations produced. Our findings offer insights into how people make sense of uncertain visual information and suggest ways in which computer models can be designed to mimic this ability. These results can contribute to our understanding of human perception as well as help advance artificial vision systems beyond simple pattern recognition.

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bioRxiv2023

A Biophysical Model of Visual Rivalry Links Cellular Mechanisms to Signatures of Conscious Perception

Contemporary models of perceptual awareness lack tractable neurobiological constraints. Inspired by recent cellular recordings in a mouse model of tactile threshold detection, we constructed a biophysical model of perceptual awareness that incorporated essential features of thalamocortical anatomy and cellular physiology. Our model reproduced, and mechanistically explains, the key in vivo neural and behavioural signatures of perceptual awareness in the mouse model, as well as the response to a set of causal perturbations. We generalised the same model (with identical parameters) to a more complex task - visual rivalry - and found that the same thalamic-mediated mechanism of perceptual awareness determined perceptual dominance. This led to the generation of a set of novel, and directly testable, electrophysiological predictions. Analyses of the model based on dynamical systems theory show that perceptual awareness in simulations of both threshold detection and visual rivalry arises from the emergent systems-level dynamics of thalamocortical loops.

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bioRxiv2021

Double-μPeriscope: a tool for multi-layer optical recordings, optogenetic stimulations or both

Intelligent behavior and cognitive functions in mammals depend on cortical microcircuits made up of a variety of excitatory and inhibitory cells that form a forest-like complex across six layers. Mechanistic understanding of cortical microcircuits requires both manipulation and monitoring of multiple layers and interactions between them. However, existing techniques are limited as to simultaneous monitoring and stimulation at different depths without damaging a large volume of cortical tissue. Here, we present a relatively simple and versatile method for delivering light to any two cortical layers simultaneously. The method uses a tiny optical probe consisting of two micro-prisms mounted on a single shaft. We demonstrate the versatility of the probe in three sets of experiments: first, two distinct cortical layers were optogenetically and independently manipulated; second, one layer was stimulated while the activity of another layer was monitored; third, the activity of thalamic axons distributed in two distinct cortical layers were simultaneously monitored in awake mice. Its simple-design, versatility, small-size and low-cost allow the probe to be applied widely to address important biological questions.

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bioRxiv2019

A Bayesian decomposition of BAC firing as a mechanism for apical amplification in neocortical pyramidal neurons

Pyramidal cells in layer 5 of the neocortex have two distinct integration sites. These cells integrate inputs to basal dendrites in the soma while integrating inputs to the tuft in a site at the top of the apical trunk. The two sites communicate by action potentials that backpropagate to the apical site and by backpropagation-activated calcium spikes (BAC firing) that travel from the apical to the somatic site. Six key messages arise from the probabilistic information-theoretic analyses of BAC firing presented here. First, we suggest that pyramidal neurons with BAC firing could convert the odds in favour of the presence of a feature given the basal data into the odds in favour of the presence of a feature given the basal data and the apical input, by a simple Bayesian calculation. Second, the strength of the cells response to basal input can be amplified when relevant to the current context, as specified by the apical input, without corrupting the message that it sends. Third, these analyses show rigorously how this apical amplification depends upon communication between the sites. Fourth, we use data on action potentials from a very detailed multi-compartmental biophysical model to study our general model in a more realistic setting, and demonstrate that it describes the data well. Fifth, this form of BAC firing meets criteria for distinguishing modulatory from driving interactions that have been specified using recent definitions of multivariate mutual information. Sixth, our general decomposition can be extended to cases where, instead of being purely driving or purely amplifying, apical and basal inputs can be partly driving and partly amplifying to various extents. These conclusions imply that an advance beyond the assumption of a single site of integration within pyramidal cells is needed, and suggest that the evolutionary success of neocortex may depend upon the cellular mechanisms of context-sensitive selective amplification hypothesized here.\n\nAuthor summaryThe cerebral cortex has a key role in conscious perception, thought, and action, and is predominantly composed of a particular kind of neuron: the pyramidal cells. The distinct shape of the pyramidal neuron with a long dendritic shaft separating two regions of profuse dendrites allows them to integrate inputs to the two regions separately and combine the results non-linearly to produce output. Here we show how inputs to this more distant site strengthen the cells output when it is relevant to the current task and environment. By showing that such neurons have capabilities that transcend those of neurons with the single site of integration assumed by many neuroscientists, this splitting of the neuronal atom offers a radically new viewpoint from which to understand the evolution of the cortex and some of its many pathologies. This also suggests that approaches to artificial intelligence using neural networks might come closer to something analogous to real intelligence, if, instead of basing them on processing elements with a single site of integration, they were based on elements with two sites, as in cortex.

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bioRxiv2019

Individual differences in the effects of priors on perception: a multi-paradigm approach

The present study investigated individual differences in how much subjects rely on prior information, such as expectations or knowledge, when faced with perceptual ambiguity. The behavioural performance of forty-four participants was measured on four different visual paradigms (Mooney face recognition, illusory contours, blur detection and representational momentum) in which priors have been shown to affect perception. In addition, questionnaires were used to measure autistic and schizotypal traits in the non-clinical population. We hypothesized that someone who in the face of ambiguous or noisy perceptual input relies heavily on priors, would exhibit this tendency across a variety of tasks. This general pattern would then be reflected in high pairwise correlations between the behavioural measures and an emerging common factor. On the contrary, our results imply that there is no single factor that explains the individual differences present in the aforementioned tasks, as further evidenced by the overall lack of robust correlations between the separate paradigms. Instead, a two-factor structure reflecting differences in the hierarchy of perceptual processing was the best fit for explaining the individual variance in these tasks. This lends support to the notion that mechanisms underlying the effects of priors likely originate from several independent sources and that it is important to consider the role of specific tasks and stimuli more carefully when reporting effects of priors on perception.

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bioRxiv2018

Identifying task-relevant spectral signatures of perceptual categorization in the human cortex

Human brain has developed mechanisms to efficiently decode sensory information according to perceptual categories of high prevalence in the environment, such as faces, symbols, objects. Neural activity produced within localized brain networks has been associated with the process that integrates both sensory bottom-up and cognitive top-down information processing. Yet, how specifically the different types and components of neural responses reflect the local networks selectivity for categorical information processing is still unknown. By mimicking the decoding of the sensory information with machine learning we can obtain accurate artificial decoding models. Having the artificial system functionally on par with the biological one we can analyze the mechanics of the artificial system to gain insights into the inner workings of its biological counterpart. In this work we train a Random Forest classification model to decode eight perceptual categories from visual stimuli given a broad spectrum of human intracranial signals (4 - 150 Hz) obtained during a visual perception task, and analyze which of the spectral features the algorithm deemed relevant to the perceptual decoding. We show that network selectivity for a single or multiple categories in sensory and non-sensory cortices is related to specific patterns of power increases and decreases in both low (4 - 50 Hz) and high (50 - 150 Hz) frequency bands. We demonstrate that the locations and patterns of activity that are identified by the algorithm not only coincide with the known spectro-spatial signatures, but extend our knowledge by uncovering additional spectral signatures describing neural mechanisms of visual category perception in human brain.\n\nSignificance statementPrevious works have shown where and when perceptual category information can be decoded from the human brain, our study adds to that line of research by allowing to identify spectrotemporal patterns that contribute to category decoding without the need to formulate a priori hypothesis on which spectral components and at which times are worth investigating. Application of this method to an extensive dataset of human intracerebral recordings delineates the locations that are predictive of several perceptual categories from the locations that are have narrow specialization, identifies spectral signatures characteristic of each of 8 perceptual categories and allows to observe global and category-specific patterns of neural activity pertinent to functional perceptual categorization.

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bioRxiv2018

Waving goodbye to contrast: Self-generated hand movements attenuate visual sensitivity

It is well known that the human brain continuously predicts the sensory consequences of its own body movements, which typically results in sensory attenuation. Yet, the extent and exact mechanisms underlying sensory attenuation are still debated. To explore this issue, we asked participants to decide which of two visual stimuli was of higher contrast in a virtual reality situation where one of the stimuli could appear behind the participants invisible moving hand or not. Over two experiments, we measured the effects of such \"virtual occlusion\" on first-order sensitivity and on metacognitive monitoring. Our findings show that self-generated hand movements reduced the apparent contrast of the stimulus. This result can be explained by the active inference theory. Moreover, sensory attenuation seemed to affect only first-order sensitivity and not (second-order) metacognitive judgments of confidence.

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bioRxiv2017

Frequency-Resolved Correlates Of Visual Object Recognition In Human Brain Revealed By Deep Convolutional Neural Networks

Previous work demonstrated a direct correspondence between the hierarchy of the human visual areas and layers of deep convolutional neural networks (DCNN) trained on visual object recognition. We used DCNNs to investigate which frequency bands correlate with feature transformations of increasing complexity along the ventral visual pathway. By capitalizing on intracranial depth recordings from 100 patients and 11293 electrodes we assessed the alignment between the DCNN and signals at different frequency bands in different time windows. We found that gamma activity, especially in the low gamma-band (30 - 70 Hz), matched the increasing complexity of visual feature representations in the DCNN. These findings show that the activity of the DCNN captures the essential characteristics of biological object recognition not only in space and time, but also in the frequency domain. These results also demonstrate the potential that modern artificial intelligence algorithms have in advancing our understanding of the brain.\n\nSignificance StatementRecent advances in the field of artificial intelligence have revealed principles about neural processing, in particular about vision. Previous works have demonstrated a direct correspondence between the hierarchy of human visual areas and layers of deep convolutional neural networks (DCNNs), suggesting that DCNN is a good model of visual object recognition in primate brain. Studying intracranial depth recordings allowed us to extend previous works by assessing when and at which frequency bands the activity of the visual system corresponds to the DCNN. Our key finding is that signals in gamma frequencies along the ventral visual pathway are aligned with the layers of DCNN. Gamma frequencies play a major role in transforming visual input to coherent object representations.

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bioRxiv2017

VREX: An Open-Source Toolbox for Creating 3D Virtual Reality Experiments

BackgroundWe present VREX, a free open-source Unity toolbox for virtual reality research in the fields of experimental psychology and neuroscience.\n\nResultsDifferent study protocols about perception, attention, cognition and memory can be constructed using the toolbox. VREX provides a procedural generation of (interconnected) rooms that can be automatically furnished with a click of a button. VREX includes a menu system for creating and storing experiments with different stages. Researchers can combine different rooms and environments to perform end-to-end experiments including different testing situations and data collection. For fine-tuned control VREX also comes with an editor where all the objects in the virtual room can be manually placed and adjusted in the 3D world.\n\nConclusionsVREX simplifies the generation and setup of complicated VR scenes and experiments for researchers. VREX can be downloaded and easily installed from vrex.mozello.com

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