Biology subjects

Orban, G.

Publications and source records attributed to Orban, G..

10 recordsLinked to original sources

bioRxiv2025

Neural dynamics reveal foraging-like computations in the frontal cortex

The prefrontal cortex has a central role in flexible behavior. Computations for weighing alternatives, committing to an option, integrating evidence, and abandoning an option are dominantly formalized in reinforcement learning (RL). Yet, ecological considerations showcase foraging as a competing account. Identifying how the brain actually solves efficient weighing of options requires the identification of the neural representation of this abstract yet precise computation. We recorded neural populations from three areas of the primate frontal cortex involved in decision making to characterize neural dynamics while animals performed a standard RL task. By combining behavioral and neuron population analysis, we found a neural subspace in midcingulate (MCC) and ventrolateral prefrontal cortex (vLPFC) that precisely tracked value computations. We demonstrated that MCC dynamics showed signatures of foraging-like computation but conflicted with RL requirements. Our study thus provides a detailed neural account of foraging-like flexibility in the primate frontal cortex.

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bioRxiv2025

High-density, Identified Cell Recordings from Motor Cortex of Awake Behaving Macaques using 1024-channel SiNAPS-NHP Probes

Objective/BackgroundAcquiring bioelectric signals from many single neurons in primate brain remains challenging. Chronic implants offer a reasonable channel count ([~]100) but sample only a small, fixed region of the cortex. Acutely inserted electrodes can sample from a wider region by making new penetrations each day. The aim of this study was to develop an active dense CMOS probe and experimental procedures to demonstrate acute large-scale single unit recordings from behaving monkeys. MethodsA single-shank CMOS probe was specifically designed for intracortical macaque recordings. The device is based on SiNAPS technology, with additional multiplexing circuits to minimize output lines. Synchronous sampling at 20 kHz/channel from >2k electrode-pixels is achieved with multiple probe systems. Experiments were performed in behaving macaques, achieving multiple day insertions in the motor cortex. Methods were developed to extract spontaneous spiking times of antidromically-identified neurons. ResultsThe probe (10.7 mm in length, 158 m in width, 50 m in thickness) provides a regular array of 1024 electrodes (14 x 14 m2) arranged in 4 columns with an interelectrode pitch of 30 m. Dural penetration is eased by a small pilot hole; the optimized insertion procedure allows recordings from many different sites. Some cells were identified by antidromic activation as pyramidal tract neurons, which project to the spinal cord. ConclusionThis probe configuration can reach the anterior bank of the central sulcus, which contains many corticospinal cells that connect directly to motoneurons. SignificanceThis study is an important advance in the toolkit of primate neurophysiology.

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bioRxiv2023

Top-down perceptual inference shaping the activity of early visual cortex

Deep discriminative models provide remarkable insights into hierarchical processing in the brain by predicting neural activity along the visual pathway. However, these models differ from biological systems in their computational and architectural properties. Unlike biological systems, they require teaching signals for supervised learning. Moreover, they rely on feed-forward processing of stimuli, which contrasts with the extensive top-down connections in the ventral pathway. Here, we address both issues by developing a hierarchical deep generative model and show that it predicts an extensive set of experimental results in the primary and secondary visual cortices (V1 and V2). We show that the widely documented sensitivity of V2 neurons to textures is a consequence of learning a hierarchical representation of natural images. Further, we show that top-down influences are inherent to hierarchical inference. Hierarchical inference explains neural signatures of top-down interactions and reveals how higher-level representation shapes low-level representations through modulation of response mean and noise correlations in V1.

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bioRxiv2023

Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex during decision making

Attention is a cognitive faculty that selects part of a larger set of percepts, driven by cues such as stimulus saliency, internal goals or priors. The enhancement of the attended representation and inhibition of distractors have been proposed as potential neural mechanisms driving this selection process. Yet, how attention operates when the cue has to be internally constructed from conflicting stimuli, decision rules, and reward contingencies, is less understood. Here we recorded from populations of neurons in the anterior cingulate cortex (ACC), an area implicated in ongoing error monitoring and correction during decision conflicts, in a challenging attention-shifting task. In this task, mice had to attend to the rewarded modality when presented identical auditory and visual stimuli in two contexts without direct external cues. In the ACC, the irrelevant stimulus continuously became less decodable than the relevant stimulus as the trial progressed to the decision point. This contrasted strongly with our previous findings in V1 where both relevant and irrelevant stimuli were equally decodable throughout the trial. Using analytical tools and a recurrent neural network (RNN) model, we found that the linearly independent representation of stimulus modalities in ACC was well suited to context-gated suppression of a stimulus modality. We demonstrated that the feedback structure of lateral connections in the RNN consisted of excitatory interactions between cell ensembles representing the same modality and mutual inhibition between cell ensembles representing distinct stimulus modalities. Using this RNN model showing signatures of context-gated suppression, we predicted that the level of contextual modulation of individual neurons should be correlated with their relative responsiveness to the two stimulus modalities used in the task. We verified this prediction in recordings from ACC neurons but not from recordings from V1 neurons. Therefore, ACC effectively operates on low-dimensional neuronal subspaces to combine stimulus related information with internal cues to drive actions under conflict.

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bioRxiv2023

Paying attention to natural scenes in area V1

Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. Here, we hypothesized that attentional and contextual top-down signals interact in V1, in a manner that primarily benefits the representation of natural visual stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we found that attention enhanced the decodability of stimulus identity from population responses evoked by natural scenes but, critically, not by synthetic stimuli in which higher-order statistical regularities were eliminated. Population analysis revealed that neuronal responses converged to a low dimensional subspace for natural but not for synthetic images. Critically, we determined that the attentional enhancement in stimulus decodability was captured by the dominant low dimensional subspace, suggesting an alignment between the attentional and natural stimulus variance. The alignment was pronounced for late evoked responses but not for early transient responses of V1 neurons, supporting the notion that top-down feedback was required. We argue that attention and perception share top-down pathways, which mediate hierarchical interactions optimized for natural vision.

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bioRxiv2021

Sampling motion trajectories during hippocampal theta sequences

Efficient planning in complex environments requires that uncertainty associated with current inferences and possible consequences of forthcoming actions is represented. Representation of uncertainty has been established in sensory systems during simple perceptual decision making tasks but it remains unclear if complex cognitive computations such as planning and navigation are also supported by probabilistic neural representations. Here we capitalized on gradually changing uncertainty along planned motion trajectories during hippocampal theta sequences to capture signatures of uncertainty representation in population responses. In contrast with prominent theories, we found no evidence of encoding parameters of probability distributions in the momentary population activity recorded in an open-field navigation task in rats. Instead, uncertainty was encoded sequentially by sampling motion trajectories randomly in subsequent theta cycles from the distribution of potential trajectories. Our analysis is the first to demonstrate that the hippocampus is well equipped to contribute to optimal planning by representing uncertainty.

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bioRxiv2021

Continuous multiplexed population representations of task context in the mouse primary visual cortex

Primary visual cortex (V1) neurons integrate motor and multisensory information with visual inputs during sensory processing. However, whether V1 neurons also integrate and encode higher-order cognitive variables is less understood. We trained mice to perform a context-dependent cross-modal decision task where the interpretation of identical audio-visual stimuli depends on task context. We performed silicon probe population recordings of neuronal activity in V1 during task performance and showed that task context (whether the animal should base its decision on visual or auditory stimuli) can be decoded during both intertrial intervals and stimulus presentations. Context and visual stimuli were represented in overlapping populations but were orthogonal in the population activity space. Context representation was not static but displayed distinctive dynamics upon stimulus onset and offset. Thus, activity patterns in V1 independently represent visual stimuli and cognitive variables relevant to task execution.

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bioRxiv2020

Individualised dynamic internal representations from response times

Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead approximate and transient ones are recruited. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times in an implicit visuomotor sequence learning task. We identify two contributors to the internal model: the ideal observer model and a Markov model capturing only immediate temporal dependencies between observations. Individual learning curves revealed internal models initially dominated by the Markov model, which was later traded-off with the ideal observer model. Thus, our results reveal a structured inductive bias that varies across individuals both in strength and persistence but is consistent in overall structure.

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bioRxiv2020

Optimal forgetting: Semantic compression of episodic memories

It has extensively been documented that human memory exhibits a wide range of systematic distortions, which have been associated with resource constraints. Resource constraints on memory can be formalised in the normative framework of lossy compression, however traditional lossy compression algorithms result in qualitatively different distortions to those found in experiments with humans. We argue that the form of distortions is characteristic of relying on a generative model adapted to the environment for compression. We show that this semantic compression framework can provide a unifying explanation of a wide variety of memory phenomena. We harness recent advances in learning deep generative models, that yield powerful tools to approximate generative models of complex data. We use three datasets, chess games, natural text, and hand-drawn sketches, to demonstrate the effects of semantic compression on memory performance. Our model accounts for memory distortions related to domain expertise, gist-based distortions, contextual effects, and delayed recall. Author summaryHuman memory performs surprisingly poorly in many everyday tasks, which have been richly documented in laboratory experiments. While constraints on memory resources necessarily imply a loss of information, it is possible to do well or badly in relation to available memory resources. In this paper we recruit information theory, which establishes how to optimally lose information based on prior and complete knowledge of environmental statistics. For this, we address two challenges. 1, The environmental statistics is not known for the brain, rather these have to be learned over time from limited observations. 2, Information theory does not specify how different distortions of original experiences should be penalised. In this paper we tackle these challenges by assuming that a latent variable generative model of the environment is maintained in semantic memory. We show that compression of experiences through a generative model gives rise to systematic distortions that qualitatively correspond to a diverse range of observations in the experimental literature.

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bioRxiv2017

Stimulus content shapes cortical response statistics

Spike count correlations (SCCs) are ubiquitous in sensory cortices, are characterized by rich structure and arise from structured internal interactions. Yet, most theories of visual perception focus exclusively on the mean responses of individual neurons. Here, we argue that feedback interactions in primary visual cortex (V1) establish the context in which individual neurons process complex stimuli and that changes in visual context give rise to stimulus-dependent SCCs. Measuring V1 population responses to natural scenes in behaving macaques, we show that the fine structure of SCCs is stimulus-specific and variations in response correlations across-stimuli are independent of variations in response means. Moreover, we demonstrate that stimulus-specificity of SCCs in V1 can be directly manipulated by controlling the high-order structure of synthetic stimuli. We propose that stimulus-specificity of SCCs is a natural consequence of hierarchical inference where inferences on the presence of high-level image features modulate inferences on the presence of low-level features.

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