Biology subjects

Hogendoorn, H.

Publications and source records attributed to Hogendoorn, H..

16 recordsLinked to original sources

bioRxiv2024

Emergence of Sparse Coding, Balance and Decorrelation from a Biologically-Grounded Spiking Neural Network Model of Learning in the Primary Visual Cortex

1Many computational studies attempt to address the question of information representation in biological neural networks using an explicit optimization based on an objective function. These approaches begin with principles of information representation that are expected to be found in the network and from which learning rules can be derived. This study approaches the question from the opposite direction; beginning with a model built upon the experimentally observed properties of neural responses, homeostasis, and synaptic plasticity. The known properties of information representation are then expected to emerge from this substrate. A spiking neural model of the primary visual cortex (V1) was investigated. Populations of both inhibitory and excitatory leaky integrate-and-fire neurons with recurrent connections were provided with spiking input from simulated ON and OFF neurons of the lateral geniculate nucleus. This network was provided with natural image stimuli as input. All synapses underwent learning using spike-timing-dependent plasticity learning rules. A homeostatic rule adjusted the weights and thresholds of each neuron based on target homeostatic spiking rates and mean synaptic input values. These experimentally grounded rules resulted in a number of the expected properties of information representation. The network showed a temporally sparse spike response to inputs and this was associated with a sparse code with Gabor-like receptive fields. The network was balanced at both slow and fast time scales; increased excitatory input was balanced by increased inhibition. This balance was associated with decorrelated firing that was observed as population sparseness. This population sparseness was both the cause and result of the decorrelation of receptive fields. These observed emergent properties (balance, temporal sparseness, population sparseness, and decorrelation) indicate that the network is implementing expected principles of information processing: efficient coding, information maximization (infomax), and a lateral or single-layer form of predictive coding. These emergent features of the network were shown to be robust to randomized jitter of the values of key simulation parameters.

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bioRxiv2024

Neural evidence for action-related somatosensory predictions

The tactile consequences of self-initiated movements are thought to be predicted by a forward model, yet the precise neural implementation of these predictions remains unclear. In non- motor contexts, expectations are thought to activate sensory neurons tuned towards the expected stimulus. This acts as a predictive template against which afferent sensory input is compared. It is unclear whether forward model predictions have a similar neural instantiation. Here we employed time-resolved multivariate decoding on human electroencephalography (EEG) during self-generated movements to examine the content of predictive neural activity. Human participants performed index finger movements which were predictably paired with a vibration to either the index or ring finger of the opposite, passive hand. On some trials the tactile stimulus was unexpectedly omitted. Results revealed above-chance finger decoding in the pre-movement period supporting a predictive representation of expected stimulation location. As the movement approached, this predictive activity became similar to late-stage processing of a physical tactile stimulus. On omission trials, we found that despite the absence of afferent input, finger location could be decoded [~]120 ms after expected stimulus onset. This shows a stimulus-specific omission response. Together these findings indicate that self-generated movement pre-activates neurons tuned towards expected tactile consequences.

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bioRxiv2024

The representation of stimulus features during stable fixation and active vision

Predictive updating of an objects spatial coordinates from its retinotopic pre-saccadic to post-saccadic position contributes to stable visual perception. However, whether object features are predictively represented at the remapped location remains contested. Many previous studies showing evidence of feature remapping neglect the spatially invariant representation of features in the visual system. For example, feature-based attention boosts attended features across the entire visual field, potentially contributing to the maintenance of stimulus features across saccades. We set out to characterise the spatiotemporal dynamics of feature processing during stable fixation and active vision. To do so, we applied multivariate decoding methods to electroencephalography (EEG) data collected while participants viewed brief visual stimuli. Stimuli appeared at different locations across the visual field at either high or low spatial frequency (SF). During fixation, classifiers were trained to decode SF presented at one parafoveal location and cross-tested on SF from either the same, adjacent or more peripheral locations. When training and testing on the same location, SF was classified shortly after stimulus onset ([~]80 ms). Decoding of SF at locations farther from the trained location emerged later ([~]150-300 ms), with decoding latency modulated by eccentricity. This analysis provides a detailed time course for the spread of feature information across the visual field. Next, we investigated how active vision impacts the emergence of SF information. In the presence of a saccade, the decoding time of peripheral SF at parafoveal locations was accelerated, indicating predictive anticipation of SF due to the saccade. Crucially however, this predictive effect was not limited to the specific remapped location. Rather, peripheral SF was correctly classified, at an accelerated time course, at all parafoveal positions. This indicates a spatially coarse remapping of stimulus features during active vision, likely enabling a smooth transition on saccade landing. Significance StatementMaintaining a continuous representation of object features across saccades is vital for stable vision. In order to characterise the spatiotemporal dynamics of stimulus feature representation in the brain, we presented stimuli at a high and low spatial frequency at multiple locations across the visual field. Applying EEG-decoding methods we tracked the neural representation of spatial frequency during both stable fixation and active vision. Using this approach, we provide a detailed time course for the spread of feature information across the visual field during fixation. In addition, when a saccade is imminent, we discovered that the spread of feature information is expedited such that access to peripheral spatial frequency is accelerated.

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bioRxiv2024

No evidence that individual alpha frequency (IAF) represents a mechanism underlying motion-position illusions

Motion-Position Illusions (MPIs) involve the position of an object being misperceived in the context of motion (i.e. when the object contains motion, is surrounded by motion, or is moving). A popular MPI is the flash-lag effect, where a static object briefly presented in spatiotemporal alignment with a moving object, is perceived in a position behind the moving object. Recently, Cottier et al. (2023) observed that there are stable individual differences in the magnitude of these illusions, and possibly even their direction. To investigate the possible neural correlates of these individual differences, the present study explored whether a trait-like component of brain activity, individual alpha frequency (IAF), could predict individual illusion magnitude. Previous reports have found some correlations between IAF and perceptual tasks. Participants (N=61) viewed the flash-lag effect (motion and luminance), Frohlich effect, flash-drag effect, flash-grab effect, motion-induced position shift, twinkle-goes effect, and the flash-jump effect. In a separate session, five minutes of eyes-open and eyes-closed resting state EEG data was recorded. Correlation analyses revealed no evidence for a correlation between IAF and the magnitude of any MPIs. Overall, these results suggest that IAF does not represent a mechanism underlying MPIs, and that no single shared mechanism underlies these effects. This suggests that discrete sampling at alpha frequency is unlikely to be responsible for any of these illusions.

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bioRxiv2024

Progressive multi-stage extrapolation of predictable motion in human visual cortex

Neural processing of sensory information takes time. Consequently, to estimate the current state of the world, the brain must rely on predictive processes - for example, extrapolating the motion of a ball to determine its probable present position. Some evidence implicates early (pre-cortical) processing in extrapolation, but it remains unclear whether extrapolation continues during later-stage (cortical) processing, where further delays accumulate. Moreover, the majority of such evidence relies on invasive neurophysiological techniques in animals, with accurate characterisation of extrapolation effects in the human brain currently lacking. Here, we address these issues by demonstrating how precise probabilistic maps can be constructed from human EEG recordings. Participants (N = 18, 2 sessions) viewed a stimulus moving along a circular trajectory while EEG was recorded. Using LDA classification, we extracted maps of stimulus location over time and found evidence of a forwards temporal shift occurring across temporally distinct processing stages. This accelerated emergence of position representations indicates extrapolation occurring at multiple stages of processing, with representations progressively shifted closer to real-time. We further show evidence of representational overshoot during early-stage processing following unexpected changes to an objects trajectory, and demonstrate that the observed dynamics can emerge without supervision in a simulated neural network via spike-timing-dependent plasticity.

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bioRxiv2024

When visual attention is divided in the flash-lag effect

The flash-lag effect (FLE) occurs when a flashs position appears delayed relative to a continuously moving object, even though both are physically aligned. While several studies have demonstrated that reduced attention increases FLE magnitude, the precise mechanism underlying these attention-dependent effects remains elusive. In this study, we investigated the influence of visual attention on the FLE by manipulating the level of attention allocated to multiple stimuli moving simultaneously in different locations. Participants were cued to either focus on one moving stimulus or split their attention among two, three, or four moving stimuli presented in different quadrants. We measured trial-wise FLE to explore potential changes in the magnitude of perceived displacement and its trial-to-trial variability under different attention conditions. Our results reveal that FLE magnitudes were significantly larger when attention was divided among multiple stimuli compared to when attention was focused on a single stimulus, suggesting that divided attention considerably augments the perceptual illusion. However, FLE variability, measured as the coefficient of variation, did not differ between conditions, indicating that the consistency of the illusion is unaffected by divided attention. We discuss the interpretations and implications of our findings in the context of widely accepted explanations of the FLE within a dynamic environment.

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bioRxiv2023

Decoding Remapped Spatial Information in the Peri-Saccadic Period

It has been suggested that, prior to a saccade, visual neurons predictively respond to stimuli that will fall in their receptive fields after completion of the saccade. This saccadic remapping process is thought to compensate for the shift of the visual world across the retina caused by eye movements. To map the timing of this predictive process in the brain, we recorded neural activity using electroencephalography (EEG) during a saccade task. Participants made saccades between two fixation points while covertly attending to oriented gratings briefly presented at various locations on the screen. Data recorded during trials in which participants maintained fixation were used to train classifiers on stimuli in different positions. Subsequently, data collected during saccade trials were used to test for the presence of remapped stimulus information at the post-saccadic retinotopic location in the peri-saccadic period, providing unique insight into when remapped information becomes available. We found that the stimulus could be decoded at the remapped location [~]180 ms post-stimulus onset, but only when the stimulus was presented 100-200 ms before saccade onset. Within this range, we found that the timing of remapping was dictated by stimulus onset rather than saccade onset. We conclude that presenting the stimulus immediately before the saccade allows for optimal integration of the corollary discharge signal with the incoming peripheral visual information, resulting in a remapping of activation to the relevant post-saccadic retinotopic neurons. Significance StatementEach eye movement leads to a shift of the visual world across the retina, such that the visual input before and after the eye movement do not match. Despite this, we perceive the visual world as stable. A predictive mechanism known as saccadic remapping is thought to contribute to this stability. We use a saccade task with time-resolved EEG decoding to obtain a fine-grained analysis of the temporal dynamics of the saccadic remapping process. Probing different stimulus-saccade latencies and an array of stimulus locations, we identify when remapped information becomes available in the visual cortex. We describe a critical window in which feedforward visual information and the preparatory motor signals interact to allow for predictive remapping of a stimulus.

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bioRxiv2022

Position representations of moving objects align with real-time position in the early visual response

AO_SCPLOWBSTRACTC_SCPLOWWhen localising a moving object, the brain receives outdated sensory information about its position, due to the time required for neural transmission and processing. The brain may overcome these fundamental delays through predictively encoding the position of moving objects using information from their past trajectories. In the present study, we evaluated this proposition using multivariate analysis of high temporal resolution electroencephalographic data. We tracked neural position representations of moving objects at different stages of visual processing, relative to the real-time position of the object. During early stimulus-evoked activity, position representations of moving objects were activated substantially earlier than the equivalent activity evoked by unpredictable flashes, aligning the earliest representations of moving stimuli with their real-time positions. These findings indicate that the predictability of straight trajectories enables full compensation for the neural delays accumulated early in stimulus processing, but that delays still accumulate across later stages of cortical processing.

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bioRxiv2021

Decoding continuous variables from EEG data using linear support vector regression (SVR) analysis with the Decision Decoding Toolbox (DDTBOX)

BackgroundMultivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative approach is Support Vector Regression (SVR), which uses single-trial data to predict continuous variables of interest. New MethodIn this tutorial-style paper, we demonstrate how SVR is implemented in the Decision Decoding Toolbox (DDTBOX). To illustrate in more detail how results depend on specific toolbox settings and data features, we report results from two simulation studies resembling real EEG data, and one real ERP-data set, in which we predicted continuous variables across a range of analysis parameters. ResultsAcross all studies, we demonstrate that SVR is effective for analysis windows ranging from 2 ms - 100 ms, and relatively unaffected by temporal averaging. Prediction is still successful when only a small number of channels encode true information, and the analysis is robust to temporal jittering of the relevant information in the signal. Comparison with existing MethodsOur result show that SVR as implemented in DDTBOX can reliably predict continuous, more nuanced variables, which may not be well-captured by classification analysis. ConclusionsIn sum, we demonstrate that linear SVR is a powerful tool for the investigation of single-trial EEG data in relation to continuous variables, and we provide practical guidance for users.

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bioRxiv2021

Decoding explicit and implicit representations of health and taste attributes of foods in the human brain

Obesity has become a significant problem word-wide and is strongly linked to poor food choices. Even in healthy individuals, taste perceptions often drive dietary decisions more strongly than healthiness. This study tested whether health and taste representations can be directly decoded from brain activity, both when explicitly considered, and when implicitly processed for decision-making. We used multivariate support vector regression for event-related potentials (as measured by the electroencephalogram) occurring in the first second of food cue processing to predict ratings of tastiness and healthiness. In Experiment 1, 37 healthy participants viewed images of various foods and explicitly rated their tastiness and healthiness, whereas in Experiment 2, 89 healthy participants indicated their desire to consume snack foods, with no explicit instruction to consider tastiness or healthiness. In Experiment 1 both attributes could be decoded, with taste information being available earlier than health. In Experiment 2, both dimensions were also decodable, and their significant decoding preceded the decoding of decisions (i.e., desire to consume the food). However, in Experiment 2, health representations were decodable earlier than taste representations. These results suggest that health information is activated in the brain during the early stages of dietary decisions, which is promising for designing obesity interventions aimed at quickly activating health awareness.

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bioRxiv2021

Motion extrapolation in the flash-lag effect depends on perceived, rather than physical speed

In the flash-lag effect (FLE), a flash in spatiotemporal alignment with a moving object is misperceived as lagging behind the moving object. One proposed explanation for this illusion is based on predictive motion extrapolation of trajectories. In this interpretation, the diverging effects of velocity on the perceived position of the moving object suggest that FLE might be based on the neural representation of perceived, rather than physical, velocity. By contrast, alternative explanations based on differential latency or temporal averaging would predict that the FLE does not rely on such a representation of perceived velocity. Here we examined whether the FLE is sensitive to illusory changes in perceived speed that result in changes to perceived velocity, while physical speed is constant. The perceived speed of the moving object was manipulated using revolving wedge stimuli with variable pattern textures (Experiment 1) and luminance contrast (Experiment 2). The motion extrapolation interpretation would predict that the changes in FLE magnitude should correspond to the changes in the perceived speed of the moving object. In the current study, two experiments demonstrated that perceived speed and FLE magnitude increased in the dynamic pattern relative to the static pattern conditions, and that the same effect was found in the low contrast compared to the high contrast conditions. These results showed that manipulations of texture and contrast that are known to alter judgments of perceived speed also modulate perceived position. We interpret this as a consequence of motion extrapolation mechanisms and discuss possible explanations for why we observed no cross-effect correlation.

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bioRxiv2020

Predictive visual motion extrapolation emerges spontaneously and without supervision from a layered neural network with spike-timing-dependent plasticity

The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localisation of a moving object. One way this problem might be solved is extrapolation: using an objects past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing dependent plasticity. We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Due to the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalisation that arises when human observers are required to localise a moving object relative to a flashed static object (the flash-lag effect). Significance StatementOur ability to track and respond to rapidly changing visual stimuli, such as a fast moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object in order to predict its current position, despite the delays that result from neural transmission. Here we show how the neural circuits underlying this ability can be learned through spike-timing dependent synaptic plasticity, and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.

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bioRxiv2020

Visual mismatch responses index surprise signalling but not expectation suppression

The ability to distinguish between commonplace and unusual sensory events is critical for efficient learning and adaptive behaviour. This has been investigated using oddball designs in which sequences of often-appearing (i.e. expected) stimuli are interspersed with rare (i.e. surprising) deviants. Resulting differences in electrophysiological responses following surprising compared to expected stimuli are known as visual mismatch responses (VMRs). VMRs are thought to index co-occurring contributions of stimulus repetition effects, expectation suppression (that occurs when ones expectations are fulfilled), and expectation violation (i.e. surprise) responses; however, these different effects have been conflated in existing oddball designs. To better isolate and quantify effects of expectation suppression and surprise, we adapted an oddball design based on Fast Periodic Visual Stimulation (FPVS) that controls for stimulus repetition effects. We recorded electroencephalography (EEG) while participants (N=48) viewed stimulation sequences in which a single face identity was periodically presented at 6 Hz. Critically, one of two different face identities (termed oddballs) appeared as every 7th image throughout the sequence. The presentation probabilities of each oddball image within a sequence varied between 10-90%, such that participants could form expectations about which oddball face identity was more likely to appear within each sequence. We also included expectation neutral 50% probability sequences, whereby consistently biased expectations would not be formed for either oddball face identity. We found that VMRs indexed surprise responses, and effects of expectation suppression were absent. That is, ERPs were more negative-going at occipitoparietal electrodes for surprising compared to neutral oddballs, but did not differ between expected and neutral oddballs. Surprising oddball-evoked ERPs were also highly similar across the 10-40% appearance probability conditions. Our findings indicate that VMRs which are not accounted for by repetition effects are best described as an all-or-none surprise response, rather than a minimisation of prediction error responses associated with expectation suppression. Highlights- We used a recently-developed oddball design that controls for repetition effects - We found effects of surprise but not expectation suppression on ERPs - Surprise responses did not vary by stimulus appearance probability

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bioRxiv2020

A neural representation of invisibility: when stimulus-specific neural activity negatively correlates with conscious experience

Although visual awareness of an object typically increases neural responses, we identify a neural response that increases prior to perceptual disappearances, and that scales with the amount of invisibility reported during perceptual filling-in. These findings challenge long-held assumptions regarding the neural correlates of consciousness and entrained visually evoked potentials, by showing that the strength of stimulus-specific neural activity can encode the conscious absence of a stimulus. Significance StatementThe focus of attention and the contents of consciousness frequently overlap. Yet what happens if this common correlation is broken? To test this, we asked human participants to attend and report on the invisibility of four visual objects which seemed to disappear, yet actually remained on screen. We found that neural activity increased, rather than decreased, when targets became invisible. This coincided with measures of attention that also increased when stimuli disappeared. Together, our data support recent suggestions that attention and conscious perception are distinct and separable. In our experiment, neural measures more strongly follow attention.

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bioRxiv2020

Decoding Predictions and Violations of Object Position and Category in Time-resolved EEG

Classic models of predictive coding propose that sensory systems use information retained from prior experience to predict current sensory input. Any mismatch between predicted and current input (prediction error) is then fed forward up the hierarchy leading to a revision of the prediction. We tested this hypothesis in the domain of object vision using a combination of multivariate pattern analysis and time-resolved electroencephalography. We presented participants with sequences of images that stepped around fixation in a predictable order. On the majority of presentations, the images conformed to a consistent pattern of position order and object category order, however, on a subset of presentations the last image in the sequence violated the established pattern by either violating the predicted category or position of the object. Contrary to classic predictive coding when decoding position and category we found no differences in decoding accuracy between predictable and violation conditions. However, consistent with recent extensions of predictive coding, exploratory analyses showed that a greater proportion of predictions was made to the forthcoming position in the sequence than to either the previous position or the position behind the previous position suggesting that the visual system actively anticipates future input as opposed to just inferring current input.

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bioRxiv2018

Predictive coding with neural transmission delays: a real-time temporal alignment hypothesis

Hierarchical predictive coding is an influential model of cortical organization, in which sequential hierarchical layers are connected by feedback connections carrying predictions, as well as feedforward connections carrying prediction errors. To date, however, predictive coding models have neglected to take into account that neural transmission itself takes time. For a time-varying stimulus, such as a moving object, this means that feedback predictions become misaligned with new sensory input. We present an extended model implementing both feed-forward and feedback extrapolation mechanisms that realigns feedback predictions to minimize prediction error. This realignment has the consequence that neural representations across all hierarchical stages become aligned in real-time. Using visual motion as an example, we show that the model is neurally plausible, that it is consistent with evidence of extrapolation mechanisms throughout the visual hierarchy, that it predicts several known motion-position illusions, and that it provides a solution to the temporal binding problem.

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