Biology subjects

Bakhtiari, S.

Publications and source records attributed to Bakhtiari, S..

16 recordsLinked to original sources

bioRxiv2026

Task-optimized neural networks reveal distinct contributions of specialized and broader visual learning to neural representations of face familiarity

How neural activity across the ventral visual hierarchy supports face recognition is an open question. A long-standing debate asks whether face processing, particularly in fusiform cortex, relies on face-specific computations or representations shared with broader visual recognition. Here we combine source-resolved magnetoencephalography (MEG) with task-optimized neural networks as controlled computational models of visual experience. Rather than manipulating long-term expertise in human observers, we systematically vary learning objective on the model side-what the networks are trained to recognize-while holding architecture and loss function constant within model comparisons. We then ask which learned representational geometries best align with neural responses, where and when. We measured millisecond-resolved brain-model alignment across V1, lateral occipital cortex (LOC) and fusiform cortex while participants viewed familiar, unfamiliar and scrambled faces. The same stimuli were presented to seven CNN architectures trained for face-identity recognition (FR), object-category recognition (OR) or object categorization including a face category (Dual), alongside untrained controls. Familiarity produced a stage-dependent dissociation: in LOC, familiar faces showed earlier brain-model alignment than unfamiliar faces in the M170 range, an effect most consistent in models trained for face-recognition, whereas broader objectives produced more variable, architecture-dependent peak alignment latencies. In fusiform cortex they showed stronger alignment around the M200 range. This fusiform advantage was not uniquely associated with face-recognition training: dual- and object-trained models showed greater fusiform correspondence than face-trained models. Together, these findings support stage-dependent specialization, with training for face-identity recognition constraining intermediate-stage timing while later fusiform representations remain compatible with representational structure acquired through broader visual computations.

neuroscience↗

bioRxiv2026

Divergent specializations for motion-driven representations in higher lateral and dorsal visual areas

The human visual system integrates both static and dynamic information to support form and shape perception, yet the computational principles underlying the integration of motion for object recognition remain unclear. Artificial neural networks (ANNs) offer a computational framework for developing and testing hypotheses about these principles: if ANNs trained on motion-related tasks develop representations that align with brain activity and support object categorization, this would suggest that the training objectives and architectural constraints of these networks may capture key aspects of motion processing in biological visual systems in general, and motion processing for object recognition, in particular. Here, we investigated this question using "object kinematograms", stimuli in which object form is conveyed solely through motion cues. We measured neural responses of two higher regions of the lateral and the dorsal visual pathways, respectively, with strong sensitivity to dynamic cues from objects: lateral occipitotemporal cortex (LOTbio), and left supramarginal gyrus (SMGlh), as well as primary visual cortex (V1). We compared brain responses to representations extracted from two neural networks: SlowFast, a dual-pathway architecture trained on action recognition that processes slow- and fast-varying visual information with cross-pathway integration, and DorsalNet, a model of the primate dorsal visual pathway trained on embodied self-motion estimation. Representational similarity analysis revealed distinct representational profiles across brain areas, demonstrating functional specialization in motion-based form processing. LOTbio was best characterized by the slow pathway of the SlowFast model, whereas SMGlh showed strong similarity to both models. Critically, we found that representations aligned with brain activity also better supported behavioral function: the full SlowFast model, incorporating both slow and fast pathways, outperformed other models in few-shot categorization of object kinematograms and showed the highest similarity to human perceptual judgments. These findings demonstrate that with appropriate inductive biases, specifically, dual-pathway architectures for multi-scale motion processing and training objectives focused on dynamic visual tasks, ANNs can develop functionally useful representations of motion-defined forms that exhibit better alignment with the visual regions involved in processing dynamic visual signals.

neuroscience↗

bioRxiv2026

Shaped by meaning, weighted by reliability: New insights into multisensory integration

In everyday perception, sensory information is rarely interpreted in isolation. Instead, observers generate expectations about what is likely to occur next, using contextual information to guide decisions, particularly when sensory input is weak, ambiguous, or uncertain. These expectations can facilitate behaviour when they align with incoming evidence, but impose costs when they conflict, shaping how information from different senses contributes to behaviour. However, it remains unclear what kind of multisensory process is engaged when meaning guides decisions, and whether multisensory facilitation and non-independence reflect a single integrative mechanism or distinct routes shaped by context. Here, we examined how semantic context, visual clarity, and the relationship between auditory and visual signals jointly shape behaviour in a semantic correspondence task using dynamic, naturalistic events. Participants judged whether a target matched a preceding concept prime, establishing an expectation at the level of meaning. Targets were auditory-only, visual-only (clear or blurry), or audiovisual (AV), with AV components conveying either the same or different meanings across modalities. AV facilitation was not a general consequence of stimulus redundancy. When the prime accurately predicted the target and auditory and visual signals conveyed the same meaning, degrading visual input increased the contribution of auditory information, leading to faster responses and violations of the race-model inequality. By contrast, when the prime indicated the target but auditory and visual signals conveyed competing meanings, AV stimulation produced robust behavioural costs relative to vision. When the prime did not match the target, congruent and incongruent audiovisual events produced comparable effects, and race-model violations were observed even when auditory and visual signals encoded different concepts, indicating convergence at the level of the response rather than shared perceptual content. Finally, exploratory clustering revealed two participant subgroups with comparable overall response speed but systematically different multisensory profiles when semantic expectations did not allow reliable anticipation of the upcoming event. Together, these findings show that multisensory effects in semantic decisions are shaped by meaning and arise through two functionally distinct routes: one in which reduced sensory reliability promotes joint use of congruent audiovisual information, and another in which perceptually different signals converge on the same response when semantic expectations are not met. HighlightsO_LISemantic expectations shape how multisensory information is used to guide responses. C_LIO_LIReduced sensory reliability enables multisensory facilitation when signals convey the same meaning. C_LIO_LIMultisensory effects arise when sensory signals support the same response. C_LIO_LIIndividuals adopt distinct strategies for resolving uncertainty in multisensory decisions. C_LI

neuroscience↗

bioRxiv2025

The curriculum effect in visual learning: the role of readout dimensionality

Generalization of visual perceptual learning (VPL) to unseen conditions varies across tasks. Previous work suggests that training curriculum may be integral to generalization, yet a theoretical explanation is lacking. We propose an explanatory theory of visual learning generalization and curriculum effects by leveraging an artificial neural network (ANN) model of VPL in comparison with humans. We found that easy-to-hard sequential training improved generalization in both humans and ANNs. However, when easy and hard conditions were interleaved, humans and ANNs showed different behaviours: while ANNs performed worse than with sequential training, humans maintained good performance but with large inter-individual variability. Investigating ANN models trained with different curricula, we demonstrated that models relying on low-dimensional neural populations showed superior generalization. This readout subspace dimensionality was directly determined by curriculum: learners who learned from easy tasks early formed lower-dimensional subspaces and generalized better. Our theory provides a mechanistic framework linking curriculum design to VPL generalization through neural population dimensionality. Author SummaryLearning new skills is fundamental to humans and animals. However, a key challenge in learning is generalization: applying learned skills to new situations not encountered during training. While it is well known that training curriculum affects how well we generalize, the underlying mechanisms remain poorly understood. What makes certain training curricula more effective than others? Why do some learners generalize better than others even with similar training? We developed a computational theory to explain how curriculum design influences generalization in visual learning. Using a combination of human behavioural experiments and artificial neural network modeling, we posit that easy-to-hard training sequences lead learners to focus on fewer, more essential visual features. This creates low-dimensional neural representations that are robust and generalize well to new conditions. Importantly, we found this benefit even extends to learners who spontaneously adopt easy-to-hard strategies on their own, without explicit curriculum design. Our theory provides a mechanistic link between curriculum structure, neural population dynamics, and generalization performance. Moreover, these findings offer practical guidelines for designing effective training programs in education and rehabilitation.

neuroscience↗

bioRxiv2024

Complex Properties of Training Stimuli Affect Brain Alignment in a Deep Network Model of Mouse Visual Cortex

Deep convolutional neural networks are important models of the visual cortex that ac-count relatively well for brain activity and are able to perform ethologically relevant functions. However, it is unknown which combination of factors, such as network ar-chitecture, training objectives, and data best align this family of models with the brain. Here we investigate the statistics of training data. We hypothesized that stimuli that are naturalistic for mice would lead to higher similarity between deep network models and activity in mouse visual cortex. We used a video-game engine to create training datasets in which we varied the naturalism of the environment, the movement statis-tics, and the optics of the modelled eye. The naturalistic environment substantially and consistently led to greater brain similarity, while the other factors had more subtle and area-specific effects. We then hypothesized that differences in brain similarity between the two environments arose due to differences in spatial frequency spectra, distribu-tions of color and orientation, and/or temporal autocorrelations. To test this, we created abstract environments, composed of cubes and spheres, that resembled the naturalis-tic and non-naturalistic environments in these respects. Contrary to our expectations, these factors accounted poorly for differences in brain similarity due to the naturalis-tic and non-naturalistic environments. This suggests that the higher brain similarities we observed after training with the naturalistic environment were due to more complex factors.

neuroscience↗

bioRxiv2024

Membranous translation platforms in the chloroplast of Chlamydomonas reinhardtii

A small genome in chloroplasts encodes polypeptide subunits of the photosynthetic electron transport complexes in the membranes of thylakoid vesicles in the chloroplast stroma. Trans-membrane subunits of these complexes undergo co-translational membrane insertion during their synthesis by ribosomes of the bacterial-like genetic system of this semiautonomous organelle. While thylakoid membranes are sites of translation, evidence in the unicellular alga Chlamydomonas reinhardtii supports translation also on non-canonical membranes in a discrete translation-zone in the chloroplast. To characterize the membranous platforms for translation and the biogenesis of thylakoid membrane, we profiled membranes during chloroplast development, using the yellow-in-the-dark 1 mutant, and carried out proteomic analyses on membranes of interest. The results support roles of two membrane types in preliminary and ongoing stages of translation: a "low-density membrane" and a denser "chloroplast translation membrane", respectively. These roles are based on correlations of the relative levels of each membrane type and the translational status of the chloroplast before, during and after chloroplast differentiation and results of proteomic analyses. Our results support a model of photosynthesis complex biogenesis in a spatiotemporal "assembly line" involving LDM and CTM as sequential stages leading to photosynthetic thylakoid membranes.

cell biology↗

bioRxiv2023

Asymmetric stimulus representations bias visual perceptual learning

The primate visual cortex contains various regions that exhibit specialization for different stimulus properties, such as motion, shape, and color. Within each region there is often further specialization, such that particular stimulus features, such as horizontal and vertical orientations, are overrepresented. These asymmetries are associated with well-known perceptual biases, but little is known about how they influence visual learning. Most theories would predict that learning is optimal, in the sense that it is unaffected by these asymmetries. But other approaches to learning would result in specific patterns of perceptual biases. To distinguish between these possibilities, we trained human observers to discriminate between expanding and contracting motion patterns, which have a highly asymmetrical representation in visual cortex. Observers exhibited biased percepts of these stimuli, and these biases were affected by training in ways that were often suboptimal. We simulated different neural network models and found that a learning rule that involved only adjustments to decision criteria, rather than connection weights, could account for our data. These results suggest that cortical asymmetries influence visual perception and that human observers often rely on suboptimal strategies for learning.

neuroscience↗

bioRxiv2023

AGAP1-associated endolysosomal trafficking abnormalities link gene-environment interactions in a neurodevelopmental disorder with incomplete penetrance

AGAP1 is an Arf1 GAP that regulates endolysosomal trafficking. Damaging variants have been linked to cerebral palsy and autism. We report 3 new individuals with microdeletion variants in AGAP1. Affected individuals have intellectual disability (3/3), autism (3/3), dystonia with axial hypotonia (1/3), abnormalities of brain maturation (1/3), growth impairment (2/3) and facial dysmorphism (2/3). We investigated mechanisms potentially underlying AGAP1 neurodevelopmental impairments using the Drosophila ortholog, CenG1a. We discovered reduced axon terminal size, increased neuronal endosome abundance, and elevated autophagy at baseline. Given potential incomplete penetrance, we assessed gene-environment interactions. We found basal elevation in phosphorylation of the integrated stress-response protein eIF2 and inability to further increase eIF2-P with subsequent cytotoxic stressors. CenG1a-mutant flies have increased lethality from exposure to environmental insults. We propose a model wherein disruption of AGAP1 function impairs endolysosomal trafficking, chronically activating the integrated stress response, and leaving AGAP1-deficient cells susceptible to a variety of second hit cytotoxic stressors. This model may have broader applicability beyond AGAP1 in instances where both genetic and environmental insults co-occur in individuals with neurodevelopmental disorders. Summary statementWe describe 3 additional patients with heterozygous AGAP1 deletion variants and use a loss of function Drosophila model to identify defects in synaptic morphology with increased endosomal sequestration, chronic autophagy induction, basal activation of eIF2-P, and sensitivity to environmental stressors.

genetics↗

bioRxiv2022

Tracking the dynamics of perisaccadic visual signals with magnetoencephalography

Many brain functions are difficult to localize, as they involve distributed networks that reconfigure themselves on short timescales. One example is the integration of oculomotor and visual signals that occurs with each eye movement: The brain must combine motor signals about the eye displacement with retinal signals, to infer the structure of the surrounding environment. Our understanding of this process comes primarily from single-neuron recordings, which are limited in spatial extent, or fMRI measurements, which have poor temporal resolution. We have therefore studied visual processing during eye movements, using magnetoencephalography (MEG), which affords high spatiotemporal resolution. Human subjects performed a task in which they reported the orientation of a visual stimulus while executing a saccade. After removal of eye movement artifacts, time-frequency analysis revealed a signal that propagated in the beta-frequency band from parietal cortex to visual cortex. This signal had the characteristics of perisaccadic "remapping", a neural signature of the integration of oculomotor and visual signals. These results reveal a novel mechanism of visual perception and demonstrate that MEG can provide a useful window into distributed brain functions.

neuroscience↗

bioRxiv2021

Chloroplast-localized translation for protein targeting in Chlamydomonas reinhardtii

Translation is localized within cells to target proteins to their proper locations. We asked whether translation occurs on the chloroplast surface in Chlamydomonas and, if so, whether it is involved in co-translational protein targeting, aligned spatially with localized translation by the bacterial-type ribosomes within this organelle, or both. Our results reveal a domain of the chloroplast envelope which is bound by translating ribosomes. Purified chloroplasts retained ribosomes and mRNAs encoding two chloroplast proteins specifically on this "translation domain", but not a mRNA encoding a cytoplasmic protein. Ribosomes clusters were seen on this domain by electron tomography. Activity of the chloroplast-bound ribosomes is supported by results of the ribopuromycylation and puromycin-release assays. Co-translational chloroplast protein import is supported by nascent polypeptide dependency of the ribosome-chloroplast associations. This cytoplasmic translation domain aligns localized translation by organellar bacterial-type ribosomes in the chloroplast. This juxtaposition the dual translation systems facilitates the targeting and assembly of the polypeptide products. One-Sentence SummaryTranslation is localized to a domain of the chloroplast envelope for co-translational protein targeting in Chlamydomonas.

cell biology↗

bioRxiv2021

Your head is there to move you around: Goal-driven models of the primate dorsal pathway

Neurons in the dorsal visual pathway of the mammalian brain are selective for motion stimuli, with the complexity of stimulus representations increasing along the hierarchy. This progression is similar to that of the ventral visual pathway, which is well characterized by artificial neural networks (ANNs) optimized for object recognition. In contrast, there are no image-computable models of the dorsal stream with comparable explanatory power. We hypothesized that the properties of dorsal stream neurons could be explained by a simple learning objective: the need for an organism to orient itself during self-motion. To test this hypothesis, we trained a 3D ResNet to predict an agents self-motion parameters from visual stimuli in a simulated environment. We found that the responses in this network accounted well for the selectivity of neurons in a large database of single-neuron recordings from the dorsal visual stream of non-human primates. In contrast, ANNs trained on an action recognition dataset through supervised or self-supervised learning could not explain responses in the dorsal stream, despite also being trained on naturalistic videos with moving objects. These results demonstrate that an ecologically relevant cost function can account for dorsal stream properties in the primate brain.

neuroscience↗

bioRxiv2021

The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning

The visual system of mammals is comprised of parallel, hierarchical specialized pathways. Different pathways are specialized in so far as they use representations that are more suitable for supporting specific downstream behaviours. In particular, the clearest example is the specialization of the ventral ("what") and dorsal ("where") pathways of the visual cortex. These two pathways support behaviours related to visual recognition and movement, respectively. To-date, deep neural networks have mostly been used as models of the ventral, recognition pathway. However, it is unknown whether both pathways can be modelled with a single deep ANN. Here, we ask whether a single model with a single loss function can capture the properties of both the ventral and the dorsal pathways. We explore this question using data from mice, who like other mammals, have specialized pathways that appear to support recognition and movement behaviours. We show that when we train a deep neural network architecture with two parallel pathways using a self-supervised predictive loss function, we can outperform other models in fitting mouse visual cortex. Moreover, we can model both the dorsal and ventral pathways. These results demonstrate that a self-supervised predictive learning approach applied to parallel pathway architectures can account for some of the functional specialization seen in mammalian visual systems.

neuroscience↗

bioRxiv2021

Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations

Dynamic latent variable modelling has provided a powerful tool for understanding how populations of neurons compute. For spiking data, such latent variable modelling can treat the data as a set of point-processes, due to the fact that spiking dynamics occur on a much faster timescale than the computational dynamics being inferred. In contrast, for other experimental techniques, the slow dynamics governing the observed data are similar in timescale to the computational dynamics that researchers want to infer. An example of this is in calcium imaging data, where calcium dynamics can have timescales on the order of hundreds of milliseconds. As such, the successful application of dynamic latent variable modelling to modalities like calcium imaging data will rest on the ability to disentangle the deeper- and shallower-level dynamical systems contributions to the data. To-date, no techniques have been developed to directly achieve this. Here we solve this problem by extending recent advances using sequential variational autoencoders for dynamic latent variable modelling of neural data. Our system VaLPACa (Variational Ladders for Parallel Autoencoding of Calcium imaging data) solves the problem of disentangling deeper- and shallower-level dynamics by incorporating a ladder architecture that can infer a hierarchy of dynamical systems. Using some built-in inductive biases for calcium dynamics, we show that we can disentangle calcium flux from the underlying dynamics of neural computation. First, we demonstrate with synthetic calcium data that we can correctly disentangle an underlying Lorenz attractor from calcium dynamics. Next, we show that we can infer appropriate rotational dynamics in spiking data from macaque motor cortex after it has been converted into calcium fluorescence data via a calcium dynamics model. Finally, we show that our method applied to real calcium imaging data from primary visual cortex in mice allows us to infer latent factors that carry salient sensory information about unexpected stimuli. These results demonstrate that variational ladder autoencoders are a promising approach for inferring hierarchical dynamics in experimental settings where the measured variable has its own slow dynamics, such as calcium imaging data. Our new, open-source tool thereby provides the neuroscience community with the ability to apply dynamic latent variable modelling to a wider array of data modalities.

neuroscience↗

bioRxiv2020

Visual perceptual learning generalizes to untrained effectors

Visual perceptual learning (VPL) is an improvement in visual function following training. Although the practical utility of VPL was once thought to be limited by its specificity to the precise stimuli used during training, more recent work has shown that such specificity can be overcome with appropriate training protocols. In contrast, relatively little is known about the extent to which VPL exhibits motor specificity. Indeed, previous studies have shown that training paradigms that require one type of response (e.g., a button press) do not necessarily transfer to those that require a different response (e.g., a mouse movement). In this work, we have examined the effector specificity of VPL by training observers on tasks that maintain the same visual stimuli and task structure, but that require different effectors to indicate the response. We find that, in these conditions, VPL transfers fully between manual and oculomotor responses. These results are consistent with the idea that VPL entails the learning of a decision rule that can generalize across effectors.

neuroscience↗

bioRxiv2019

Influence of stimulus complexity on the specificity of visual perceptual learning

Although the structure and function of the human visual system are determined in large part during early development, there is ample evidence for adult plasticity as well. Such plasticity has important consequences for restoring vision after cortical damage and for improving function in healthy people. Although these applications have shown promising results, they are often limited by pathological specificity: Improvements obtained through perceptual training fail to generalize beyond the trained stimulus feature or location. Efforts to reduce specificity have focused on the design of training tasks, but less is known about the effects of stimulus structure on the specificity of perceptual learning. Here, we leverage physiological findings from the dorsal visual pathway of the primate brain to explore the hypothesis that learning specificity is related to the complexity of the training stimulus. Specifically, because neurons in higher-level structures of the dorsal visual pathway exhibit little stimulus specificity, we reasoned that training with more complex stimuli would reduce the specificity of learning. We trained human observers on stimuli of varying complexity, ranging from simple sinewave gratings to complex optic flow fields. Our results show that training with more complex stimuli reduces specificity for spatial position and stimulus features. Such changes are associated with increased spatial integration. These findings were captured by a computational \"reweighting\" model that decoded the outputs of simulated neurons in areas MT and MST of the primate visual cortex. Our results suggest that the addition of more complex stimuli into perceptual learning paradigms provides a simple and effective way to minimize specificity in learning.

neuroscience↗

bioRxiv2019

The Role of the Subthalamic Nucleus in Inhibitory Control of Oculomotor Behavior in Parkinson’s Disease

The ability to inhibit an inappropriate action in a context is an important part of the human cognitive repertoire, and deficiencies in this ability are common in neurological and psychiatric disorders. An anti-saccade is a simple experimental task within the oculomotor repertoire that can be used to test this ability. The task involves an inhibition of a saccade to the peripheral target (pro-saccade) and generation of a voluntary eye movement toward the mirror position (anti-saccade). Previous studies provide evidence for a possible contribution from the basal ganglia in anti-saccade behavior. However, the precise role of different components in generation of anti-saccade behavior is still uncertain. Parkinsons disease patients with implanted deep brain stimulation (DBS) in subthalamic nucleus (STN) provide us with a unique opportunity to investigate the role of STN in anti-saccade behavior. Previous attempts to show the effect of STN DBS on anti-saccades have produced conflicting observations. For example, the effect of STN DBS on anti-saccade error rate is not yet clear. Part of this inconsistency may be related to differences in dopaminergic states in different studies. Here, we tested Parkinsons disease patients on anti- and pro-saccade tasks ON and OFF STN DBS and ON and OFF dopaminergic medication. We made three main observations. First, STN DBS increases the anti-saccade error rate while patients are OFF dopamine replacement therapy. Second, there is an interaction between dopamine replacement therapy and STN DBS. More specifically, L-dopa reduces the effect of STN DBS on anti-saccade error rate. Third, STN DBS can induce different effects on pro- and anti-saccades in different patients. These observations provide evidence for an important role for the STN in the circuitry underlying context-dependent modulation of visuomotor action selection.

neuroscience↗