Biology subjects

Guclu, U.

Publications and source records attributed to Guclu, U..

11 recordsLinked to original sources

bioRxiv2025

Coping strategy dynamics and resilience profiles after early life stress revealed by behavioral sequencing

Animal models can provide valuable insights into the mechanisms underlying stress-related disorders. Yet, significant translational challenges persist, as laboratory behavioral assays are often reductionistic, with limited attention to ethologically relevant behavioral diversity. Recent advances in high-throughput pose-estimation tools and computational ethology methods are addressing this limitation by enhancing the resolution and validity of behavioral phenotyping. In this context, it is known that early life stress (ELS) reshapes how animals handle subsequent threats later in life, but the fine-scale dynamics and ethological details of this shift remain elusive. To overcome this, we combined a deep-learning pose-estimation pipeline (DeepLabCut) with a supervised freezing classifier (SimBA) and an unsupervised behavioral motifs identification platform (keypoint MoSeq) to study in detail the diversity and dynamics of behavior in an auditory fear-conditioning (FC) paradigm in two independent cohorts of adult male mice that were exposed to ELS through the limited bedding and nesting (LBN) paradigm. We first validated the blunted freezing response after ELS in a supervised manner using SimBA. Next, keypoint MoSeq segmented the same pose-estimation data into ethologically meaningful motifs over time. When compared to control animals, ELS offspring showed an altered FC response, reduced behavioral entropy and limited diversity in their behavioral repertoire. Such response was characterized by longer active-behavior bouts and more recurrent transitions between states, indicating a more stereotyped and predictable response. Multidimensional scaling of time-binned behavioral vectors and distance metrics identified a resilient subpopulation within the ELS group that displayed a control-like behavioral profile, characterized by a steeper increase in freezing behavior during the FC task and a more diverse behavioral repertoire with reduced recurrence of stereotyped actions, less frequent and shorter active bouts and prolonged passive responses. Overall, our findings suggest that ELS shifts the balance between passive and active coping strategies and that resilience is marked by a less stereotypical yet more diverse and flexible behavioral response to a subsequent stressful demand. Finally, we further validated the unsupervised behavioral motifs with a predictive model that identified distinctive kinematic features of these responses, which could be used to build new behavioral classifiers that can be applied in other behavioral paradigms. These results demonstrate the potential of computational ethology to dissect complex behavioral patterns and improve our understanding of individual stress responses. By combining supervised and unsupervised behavioral analysis tools, we can deepen our understanding of the latent structure of stress behavior and identify objective markers of vulnerability and resilience.

neuroscience↗

bioRxiv2025

Bayesian optimization of cortical neuroprosthetic vision using perceptual feedback

The challenge in cortical neuroprosthetic vision is determining the optimal, safe stimulation patterns for the visual cortex in order to evoke the desired perception in blind individuals--specifically, light perceptions known as phosphenes. Currently, clinical studies gain insights into the perceptual characteristics of the perceived phosphenes by asking for descriptions of provided stimulation protocols. However, the huge parameter space for multi-electrode stimulation settings makes it difficult to draw conclusions about the optimality of the stimulation patterns that lead to well-perceived phosphenes. A systematic search in the parameter space of the electrical stimulation is needed to achieve good perception. Bayesian optimization (BO) is a framework for finding optimal parameters efficiently. Using the patients scoring of the perception as feedback, a model of the patients response based on iteratively generated stimulation protocols can be built to maximize perception quality. A patient implanted with an intracortical 96-channel microelectrode array in their visual cortex was tested by iteratively presenting stimulation protocols, generated via BO for the first and random generation (RG) for the second experiment. Whereas standard BO methods do not scale well to problems with over a dozen inputs, we propose to optimize a set of 40 electrode currents using trust region-based BO. The generated protocols determine which electrodes are concurrently stimulated from the set and with how much current from a range of 0-50 {micro}A, on a maximum total current constraint of 500 {micro}A. The patient provided feedback for each stimulation based on their liking of the perception quality on a Likert scale, where a score of 7 indicated the highest quality and 0 no perception. In the BO experiment, the patient perception quality ratings gradually converged on higher values compared to the RG experiment. Similarly, gradually higher total current values were chosen by BO, in line with the observed preference of patients for higher currents due to brighter phosphenes. Finally, the electrodes that were observed to be more effective in producing phosphene perception in previous studies were gradually chosen more by BO also with the allocation of higher current values. This study demonstrates the power of BO in converging to optimal stimulation protocols based on patient feedback, providing a more efficient search for stimulation parameters for clinical studies.

bioengineering↗

bioRxiv2024

PAM: Predictive attention mechanism for neural decoding of visual perception

In neural decoding, reconstruction seeks to create a literal image from information in brain activity, typically achieved by mapping neural responses to a latent representation of a generative model. A key challenge in this process is understanding how information is processed across visual areas to effectively integrate their neural signals. This requires an attention mechanism that selectively focuses on neural inputs based on their relevance to the task of reconstruction -- something conventional attention models, which capture only input-input relationships, cannot achieve. To address this, we introduce predictive attention mechanisms (PAMs), a novel approach that learns task-driven "output queries" during training to focus on the neural responses most relevant for predicting the latents underlying perceived images, effectively allocating attention across brain areas. We validate PAM with two datasets: (i) B2G, which contains GAN-synthesized images, their original latents and multiunit activity data; (ii) Shen-19, which includes real photographs, their inverted latents and functional magnetic resonance imaging data. Beyond achieving state-of-theart reconstructions, PAM offers a key interpretative advantage through the availability of (i) attention weights, revealing how the models focus was distributed across visual areas for the task of latent prediction, and (ii) values, capturing the stimulus information decoded from each area.

neuroscience↗

bioRxiv2023

Gaze-contingent processing improves mobility performance and visual orientation in simulated head-steered prosthetic vision

The enabling technology of visual prosthetics for the blind is making rapid progress. However, there are still uncertainties regarding the functional outcomes, which can depend on many design choices in the development. In visual prostheses with a head-mounted camera, a particularly challenging question is how to deal with the gaze-locked visual percept associated with spatial updating conflicts in the brain. A recently proposed compensation strategy is gaze-contingent image processing with eyetracking, which enables natural visual scanning and reestablished spatial updating based on eye movements. The current study evaluates the benefits of gaze-contingent processing versus gaze-locked and gaze-ignored simulations in the context of mobility and orientation, using a simulated prosthetic vision paradigm with sighted subjects. Compared to gaze-locked vision, gaze-contingent processing was found to improve the speed in all experimental tasks, as well as the subjective quality of vision. Similar or further improvements were found in a control condition that ignores gaze-depended effects, a simulation that is unattainable in the clinical reality. Our results suggest that gaze-locked vision and spatial updating conflicts can be debilitating for complex visually-guided activities of daily living such as mobility and orientation. Therefore, for prospective users of head-steered prostheses with an unimpaired oculomotor system, the inclusion of a compensatory eye-tracking system is strongly endorsed.

bioengineering↗

bioRxiv2022

Biologically plausible phosphene simulation forthe differentiable optimization of visual corticalprostheses

Blindness affects millions of people around the world, and is expected to become increasingly prevalent in the years to come. For some blind individuals, a promising solution to restore a form of vision are cortical visual prostheses, which convert camera input to electrical stimulation of the cortex to bypass part of the impaired visual system. Due to the constrained number of electrodes that can be implanted, the artificially induced visual percept (a pattern of localized light flashes, or phosphenes) is of limited resolution, and a great portion of the fields research attention is devoted to optimizing the efficacy, efficiency, and practical usefulness of the encoding of visual information. A commonly exploited method is the non-invasive functional evaluation in sighted subjects or with computational models by making use of simulated prosthetic vision (SPV) pipelines. Although the SPV literature has provided us with some fundamental insights, an important drawback that researchers and clinicians may encounter is the lack of realism in the simulation of cortical prosthetic vision, which limits the validity for real-life applications. Moreover, none of the existing simulators address the specific practical requirements for the electrical stimulation parameters. In this study, we developed a PyTorch-based, fast and fully differentiable phosphene simulator. Our simulator transforms specific electrode stimulation patterns into biologically plausible representations of the artificial visual percepts that the prosthesis wearer is expected to see. The simulator integrates a wide range of both classical and recent clinical results with neurophysiological evidence in humans and non-human primates. The implemented pipeline includes a model of the retinotopic organisation and cortical magnification of the visual cortex. Moreover, the quantitative effect of stimulation strength, duration, and frequency on phosphene size and brightness as well as the temporal characteristics of phosphenes are incorporated in the simulator. Our results demonstrate the suitability of the simulator for both computational applications such as end-to-end deep learning-based prosthetic vision optimization as well as behavioural experiments. The modular approach of our work makes it ideal for further integrating new insights in artificial vision as well as for hypothesis testing. In summary, we present an open-source, fully differentiable, biologically plausible phosphene simulator as a tool for computational, clinical and behavioural neuroscientists working on visual neuroprosthetics.

neuroscience↗

bioRxiv2020

End-to-end optimization of prosthetic vision

Neural prosthetics may provide a promising solution to restore visual perception in some forms of blindness. The restored prosthetic percept is rudimentary compared to normal vision and can be optimized with a variety of image preprocessing techniques to maximize relevant information transfer. Extracting the most useful features from a visual scene is a non-trivial task and optimal preprocessing choices strongly depend on the context. Despite rapid advancements in deep learning, research currently faces a difficult challenge in finding a general and automated preprocessing strategy that can be tailored to specific tasks or user requirements. In this paper we present a novel deep learning approach that explicitly addresses this issue by optimizing the entire process of phosphene generation in an end-to-end fashion. The proposed model is based on a deep auto-encoder architecture and includes a highly adjustable simulation module of prosthetic vision. In computational validation experiments we show that such an approach is able to automatically find a task-specific stimulation protocol. The presented approach is highly modular and could be extended to dynamically optimize prosthetic vision for everyday tasks and requirements of the end-user.

bioengineering↗

bioRxiv2020

Hyperrealistic neural decoding: Linear reconstruction of face stimuli from fMRI measurements via the GAN latent space

Neural decoding can be conceptualized as the problem of mapping brain responses back to sensory stimuli via a feature space. We introduce (i) a novel experimental paradigm which uses well-controlled yet highly naturalistic stimuli with a priori known feature representations and (ii) an implementation thereof for HYPerrealistic reconstruction of PERception (HYPER) of faces from brain recordings. To this end, we embrace the use of generative adversarial networks (GANs) at the earliest step of our neural decoding pipeline by acquiring fMRI data as subjects perceive face images synthesized by the generator network of a GAN. We show that the latent vectors used for generation effectively capture the same defining stimulus properties as the fMRI measurements. As such, GAN latent vectors can be used as features underlying the perceived images that can be predicted for (re-)generation, leading to the most accurate reconstructions of perception to date.

neuroscience↗

bioRxiv2019

DeepRF: Ultrafast population receptive field mapping with deep learning

Population receptive field (pRF) mapping is an important asset for cognitive neuroscience. The pRF model is used for estimating retinotopy, defining functional localizers and to study a vast amount of cognitive tasks. In a classic pRF, the cartesian location and receptive field size are modeled as a 2D Gaussian kernel in visual space and are estimated by optimizing the fit between observed responses and predicted responses. In the standard framework this is achieved using an iterative gradient descent algorithm. This optimization is time consuming because the number of pRFs to fit (e.g., fMRI voxels) is typically large. This computation time increases further with the complexity of the pRF model (e.g., adding HRF parameters, surround suppression and uncertainty measures). Here, we introduce DeepRF, which uses deep convolutional neural networks to estimate pRFs. We compare the performance of DeepRF with that of the conventional method using a synthetic dataset for which the ground truth is known and an empirical dataset. We show that DeepRF achieves state-of-the-art performance while being more than 3 orders of magnitude faster than the conventional method. This enables easier and faster modeling of more complex pRF models, resolving an important limitation of the conventional approach.

neuroscience↗

bioRxiv2019

Neural System Identification with Cortical Information Flow

Cortical information flow (CIF) is a new framework for system identification in neuroscience. CIF models represent neural systems as coupled brain regions that each embody neural computations. These brain regions are coupled to observed data specific to that region. Neural computations are estimated via stochastic gradient descent. We show using a large-scale fMRI dataset that, in this manner, we can estimate models that learn meaningful neural computations. Our framework is general in the sense that it can be used in conjunction with any (combination of) neural recording techniques. It is also scalable, providing neuroscientists with a principled approach to make sense of the high-dimensional neural datasets.

neuroscience↗

bioRxiv2018

Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity

Estimating causal connectivity between spiking neurons from measured spike sequences is one of the main challenges of systems neuroscience. In this paper we introduce two nonparametric Bayesian methods for spike-membrane and spikespike causal connectivity based on Gaussian process regression. For spike-spike connectivity, we derive a new semi-analytic variational approximation of the response functions of a non-linear dynamical model of interconnected neurons. This semi-analytic method exploits the tractability of GP regression when the membrane potential is observed. The resulting posterior is then marginalized analytically in order to obtain the posterior of the response functions given the spike sequences alone. We validate our methods on both simulated data and real neuronal recordings.

neuroscience↗

bioRxiv2017

CNN-based Encoding and Decoding of Visual Object Recognition in Space and Time

Deep convolutional neural networks (CNNs) have been put forward as neurobiologically plausible models of the visual hierarchy. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively. Here, we addressed this question by combining CNN-based encoding models with magnetoencephalography (MEG). Human participants passively viewed 1000 images of objects while MEG signals were acquired. We modelled their high temporal resolution source-reconstructed cortical activity with CNNs, and observed a feedforward sweep across the visual hierarchy between 75-200 ms after stimulus onset. This spatiotemporal cascade was captured by the network layer representations, where the increasingly abstract stimulus representation in the hierarchical network model was reflected in different parts of the visual cortex, following the visual ventral stream. We further validated the accuracy of our encoding model by decoding stimulus identity in a left-out validation set of viewed objects, achieving state-of-the-art decoding accuracy.

neuroscience↗