Biology subjects

Brands, A. M.

Publications and source records attributed to Brands, A. M..

9 recordsLinked to original sources

bioRxiv2025

Temporal adaptation aids object recognition in deep convolutional neural networks in suboptimal viewing scenario's

The primate visual system excels in recognizing objects under challenging viewing scenarios. A neural mechanism that is thought to play a key role in this ability is rapid temporal adaptation, or the adjustment of neurons activity based on recent history. To understand how temporal adaptation may support object recognition, previous work has incorporated a variety of temporal feedback mechanisms in deep convolutional neural networks (DCNN) and explored how these mechanisms affect object recognition performance. While multiple adaptation mechanisms have been shown to impact model behavior, it remains unclear how the origin (intrinsic or recurrent) and the way the temporal feedback is integrated (additive or multiplicative) affects object recognition. Here, we compare the impact of four different temporal adaptation mechanisms on object recognition using three different task designs, including object recognition under either noise or occlusion, and in the context of novelty detection. Our results show that the effectiveness of temporal adaptation mechanisms for robust object recognition depends on the task and dataset. For objects embedded in noise, intrinsic adaptation excels with simple, high-contrast inputs, while recurrent mechanisms perform better with complex, low-contrast inputs, highlighting their focus on different visual features. Under dynamic occlusion, recurrent adaptation mechanisms exhibit a more progressive increase in performance over time, suggesting they better maintain object coherence when parts are obscured. For novelty detection, recurrent mechanisms show higher performance compared to intrinsic adaptation mechanisms, suggesting that recurrence aids in detecting global changes caused by the presentation of new objects. All together, these findings suggest that robust object recognition likely requires multiple temporal adaptation strategies in parallel to handle the diverse challenges of naturalistic visual settings.

neuroscience↗

bioRxiv2025

Catecholamine Precursor Modulation of Human Exploration:Evidence From a Large Gender-Balanced Sample

The catecholamine precursor tyrosine has been linked to improved cognitive performance, but investigations into decision-making and reinforcement learning processes known to be under catecholamine control are sparse. We examined the impact of a single dose of Tyrosine (2g) on reinforcement learning and exploration in a large (n=63) gender-balanced sample in a within-subjects preregistered study. Reinforcement learning performance was improved under Tyrosine, and computational modeling revealed that this performance increase was due to a stabilization of choice behavior reflected in increased value-driven exploitation. Further non-preregistered modeling analyses confirmed that accounting for higher-order perseveration substantially improved model fit, and substantiated the observation of increased value-driven exploitation under Tyrosine. Furthermore, it revealed a more fine-grained computational impact of Tyrosine, showing attenuated effects of directed exploration and value-independent perseveration. Supplementation with Tyrosine therefore improved reinforcement learning performance by stabilizing choice patterns in the service of optimizing reward accumulation. Results confirm that Tyrosine supplementation modulates specific computational mechanisms thought to be under catecholamine control.

neuroscience↗

bioRxiv2025

Scene segmentation processes drive EEG-DCNN alignment

Visual processing in biological and artificial neural networks has been extensively studied through the lens of object recognition. While deep convolutional neural networks (DCNNs) have demonstrated hierarchical feature extraction similar to biological systems (DiCarlo and Cox, 2007; Yamins and DiCarlo, 2016), recent findings reveal a growing discrepancy: DCNNs with higher object categorization accuracy paradoxically show worse performance at predicting neural responses (Xu and Vaziri-Pashkam, 2021; Linsley et al., 2023). Using a large-scale human electroencephalography (EEG) dataset (n=10, 82,160 trials), we investigate whether this discrepancy arises because human neural EEG signals predominantly reflect scene segmentation processes rather than high-level, category-specific object representations. We trained DCNNs to perform object recognition using visual diets ([~]1 million training images across 292 object categories) with systematically varying scene segmentation demands: objects-only (pre-segmented), background-silhouette (explicit boundaries), original/background-only images (requiring full segmentation). Despite substantial differences in categorization accuracy (27-53%), all trained models showed remarkably uniform encoding performance, with peak correlations with neural data at [~]0.1s post-stimulus. Layer-wise analysis revealed a significant negative correlation between categorization accuracy and encoding performance, with earlier network layers better predicting EEG responses than deeper layers specialized for object categorization. This dissociation suggests that EEG signals primarily reflect fundamental scene parsing mechanisms rather than object-specific representations, explaining the growing discrepancy between DCNNs increasing categorization performance but deteriorating neural prediction performance. Significance StatementThis research provides a novel perspective on human electroencephalography (EEG) signals during visual processing through systematic manipulation of scene segmentation demands in deep neural networks. Using a large-scale dataset of 82,160 EEG trials and 20 trained DCNNs, we demonstrate that EEG responses primarily reflect early visual processing involved in breaking down and organizing visual scenes (scene segmentation/parsing) rather than high-level object recognition. This finding helps explain previously observed discrepancies between DCNNs categorization performance and neural prediction accuracy, suggesting that improving models ability to segment scenes, rather than simply recognizing isolated objects, may better align artificial and biological visual processing.

neuroscience↗

bioRxiv2024

Deep predictive coding networks partly capture neural signatures of short-term temporal adaptation in human visual cortex

Predictive coding is a leading theory of cortical function which posits that the brain continually makes predictions of incoming sensory stimuli using a hierarchical network of top-down and bottom-up connections. This theory is supported by prior work showing that PredNet, a deep learning network designed according to predictive coding principles, exhibits several characteristics of neural responses commonly observed in primate visual cortex. However, one ubiquitous neural phenomenon that has not yet been investigated is short-term visual adaptation: the adjustment of neural responses over time when exposed to static visual inputs that are either prolonged or directly repeated. Here, we examine whether PredNet exhibits two neural signatures of temporal adaptation previously observed in intracranial recordings of human participants viewing prolonged and repeated stimuli (Brands et al., 2024). We find that, like human visual cortex, PredNet adapts to static images, evidenced by subadditive temporal response summation: a non-linear accumulation of response magnitudes when prolonging stimulus durations, which results from neurally plausible transient-sustained dynamics in the unit activation time courses. However, PredNet activations also show a systematic response to stimulus offsets, which is absent in the human neural data. For repeated stimuli, PredNet shows slight response suppression for any two images presented in quick succession, but no repetition suppression, a comparatively stronger response reduction for identical than for non-identical image pairs that is robustly observed throughout human visual cortex. We show that these results are stable across multiple training datasets and two different types of loss computation. Lastly, in both PredNet and the neural data, we find a relationship between temporal adaptation and visual input properties, showing that temporally sustained activity is enhanced for more complex scenes containing clutter. All together, these results suggest that the emergent temporal dynamics in the PredNet only partly align with neural data and are linked to low-level properties of the visual input rather than high-level predictions arising from top-down processes.

neuroscience↗

bioRxiv2024

Contrast-dependent response modulation in convolutional neural networks captures behavioral and neural signatures of visual adaptation

Human perception is robust under challenging conditions, for example when sensory inputs change over time. Temporal adaptation in the form of reduced responses to repeated external stimuli is ubiquitously observed in the brain, yet it remains unclear how repetition suppression aids recognition of novel inputs. To clarify this, we collected behavioural and electrocorticography (EEG) measurements while human participants categorized objects embedded in visual noise patterns after first viewing these patterns in isolation, inducing adaptation to the noise stimulus. We furthermore manipulated the availability of object information in the visual input by varying the contrast of the noise-embedded objects. Our results provide convergent behavioral, neural and computational evidence of a benefit of temporal adaptation on sensory representations. Adapting to a noise pattern resulted in overall faster object recognition and better recognition of objects as object contrast increased. These adaptation-induced behavioral improvements were accompanied by more pronounced contrast-dependent modulation of object-evoked EEG responses, and better decoding of object information from EEG activity. To identify potential neural computations mediating the benefits of temporal adaptation on object recognition, we equipped task-optimized deep convolutional neural networks (DCNNs) with different candidate mechanisms to adjust network activations over time. DCNNs with intrinsic adaptation mechanisms, such as additive suppression, best captured contrast-dependent human performance benefits, whilst also showing improved object decoding as a result of adaptation. Finally, adaptation effects in networks that use temporal divisive normalization, a biologically-plausible canonical neural computation, were most robust to spatial shifts, suggesting that temporal adaptation via divisive normalization aids stable representations of time-varying visual inputs. Overall, our results demonstrate how temporal adaptation improves sensory representations and identify candidate neural computations mediating these effects. Author summaryRobust perception is essential for the human brain to detect, process, and act upon new sensory inputs. Temporal adaptation is believed to play a key role in robust sensory processing by allowing neurons to continuously adjust their responses to previous inputs in order to optimize the processing of future inputs. Here, we show that temporal adaptation aids visual object recognition by improving neural representations of object contrast and object category. By emulating temporal adaptation in deep convolutional neural network models with different computational mechanisms, we identify candidate neural computations mediating benefits of temporal adaptation on sensory processing.

neuroscience↗

bioRxiv2024

A meta reinforcement learning account of behavioral adaptation to volatility in recurrent neural networks

Natural environments often exhibit various degrees of volatility, ranging from slowly changing to rapidly changing contingencies. How learners adapt to changing environments is a central issue in both reinforcement learning theory and psychology. For example, learners may adapt to changes in volatility by increasing learning if volatility increases, and reducing it if volatility decreases. Computational neuroscience and neural network modeling work suggests that this adaptive capacity may result from a meta-reinforcement learning process (implemented for example in the prefrontal cortex), where past experience endows the system with the capacity to rapidly adapt to environmental conditions in new situations. Here we provide direct evidence for a meta-reinforcement learning account of adaptation to environmental volatility. Recurrent neural networks (RNNs) were trained on a restless four-armed bandit reinforcement learning problem under three different training regimes (low volatility training only, medium volatility training only, or meta-volatility training across a range of volatilities). We show that, in contrast to RNNs trained in the low volatility regime, RNNs trained under the medium or meta-volatility regimes exhibited a superior adaption to environmental volatility. This was reflected in a better performance, and computational modeling of the networks behavior revealed a more adaptive adjustment of learning and exploration to varying levels of volatility during test. Results extend the meta-RL account to volatility adaptation and confirm past experience as a crucial factor of adaptive decision-making.

neuroscience↗

bioRxiv2024

Ambulatory physiological measures obtained under naturalistic urban mobility conditions have acceptable reliability

Ambulatory assessment methods in psychology and clinical neuroscience are powerful research tools for collecting data outside of the laboratory. These methods encompass physiological, behavioral, and self-report measures obtained while individuals navigate in real-world environments, thereby increasing the ecological validity of experimental approaches. Despite the recent increase in applications of ambulatory physiology, data on the reliability of these measures is still limited. To address this issue, twenty-six healthy participants (N = 15 female, 18-34 years) completed an urban walking route (2.1 km, 30 min walking duration, temperature M = 19.8{degrees} degree Celsius, Range = 12{degrees}-37{degrees} degrees Celsius) on two separate testing days, while GPS-location and ambulatory physiological measures (cardiovascular and electrodermal activity) were continuously recorded. Bootstrapped test-retest reliabilities of single measures and aggregate scores derived via principal component analysis (PCA) were computed. The first principal component (PC#1) accounted for 39% to 45% of variance across measures. PC#1 scores demonstrated an acceptable test-retest reliability (r = .60) across testing days, exceeding the reliabilities of most individual measures (heart rate: r = .53, heart rate variability: r = .50, skin conductance level: r = .53, no. of skin conductance responses: r = .28, skin conductance response amplitude: r = .60). Results confirm that ambulatory physiological measures recorded during naturalistic navigation in urban environments exhibit acceptable test-retest reliability, in particular when compound scores across physiological measures are analyzed, a prerequisite for applications in (clinical) psychology and digital health. Author summaryPsychophysiological assessments have been predominantly limited to controlled laboratory settings, leaving the reliability of field measurements unclear. In this study, we conducted a proof-of-concept investigation in N=26 healthy participants navigating the same urban route on two separate days. Cardiovascular and electrodermal activity were continuously recorded and combined with GPS-based location tracking. Psychophysiological measurements obtained under naturalistic urban mobility conditions showed acceptable test-retest reliability, in particular when multiple measures where combined into a compound score via principal component analysis. Shedding light on the reliability of ambulatory assessments in urban environments emphasizes the potential for psychophysiological measurements to contribute valuable insights beyond the constraints of traditional laboratory settings.

physiology↗

bioRxiv2023

Temporal dynamics of neural adaptation across human visual cortex

Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventraland lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses across the human visual hierarchy and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences. Author summaryNeural responses in visual cortex adapt over time, with reduced responses to prolonged and repeated stimuli. Here, we examine how adaptation patterns differ across the visual hierarchy in neural responses recorded from human visual cortex with high temporal and spatial precision. To identify possible neural computations underlying short-term adaptation, we fit the response time courses using a temporal divisive normalization model. The model accurately predicts prolonged and repeated responses in lower and higher visual areas, and reveals differences in temporal adaptation across the visual hierarchy and stimulus categories. Our model suggests that differences in adaptation patterns result from differences in divisive normalization dynamics. Our findings shed light on how information is integrated in the brain on a millisecond-time scale and offer an intuitive framework to study the emergence of neural dynamics across brain areas and stimuli.

neuroscience↗

bioRxiv2023

Signatures of heuristic-based directed exploration in two-step sequential decision task behaviour.

Processes formalized in classic Reinforcement Learning (RL) theory, such as model-based (MB) control and exploration strategies have proven fertile in cognitive and computational neuroscience, as well as computational psychiatry. Dysregulations in MB control and exploration and their neurocomputational underpinnings play a key role across several psychiatric disorders. Yet, computational accounts mostly study these processes in isolation. The current study extended standard hybrid models of a widely-used sequential RL-task (two-step task; TST) employed to measure MB control. We implemented and compared different computational model extensions for this task to quantify potential exploration mechanisms. In two independent data sets spanning two different variants of the task, an extension of a classical hybrid RL model with a heuristic-based exploration mechanism provided the best fit, and revealed a robust positive effect of directed exploration on choice probabilities in stage one of the task. Posterior predictive checks further showed that the extended model reproduced choice patterns present in both data sets. Results are discussed with respect to implications for computational psychiatry and the search for neurocognitive endophenotypes.

neuroscience↗