scholarly journals Decoding the future from past experience: learning shapes predictions in early visual cortex

2015 ◽  
Vol 113 (9) ◽  
pp. 3159-3171 ◽  
Author(s):  
Caroline D. B. Luft ◽  
Alan Meeson ◽  
Andrew E. Welchman ◽  
Zoe Kourtzi
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Sequence Structure ◽  
Neural Populations ◽  
Early Visual Cortex ◽  
Temporal Structures ◽  
Future Events ◽  
Sensory Predictions ◽  
Actual Stimulus ◽  
The Brain

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.

Predictability-dependent encoding of statistical regularities in the early visual cortex

2022 ◽  
Author(s):  
Andrea Kóbor ◽  
Karolina Janacsek ◽  
Petra Hermann ◽  
Zsofia Zavecz ◽  
Vera Varga ◽  
...  
Keyword(s):  
Visual Cortex ◽  
The Other ◽  
Block Type ◽  
Statistical Knowledge ◽  
Early Visual Cortex ◽  
Statistical Regularities ◽  
Visual Cortical ◽  
Increased Activity ◽  
The Brain ◽  
Subsequent Task

Previous research recognized that humans could extract statistical regularities of the environment to automatically predict upcoming events. However, it has remained unexplored how the brain encodes the distribution of statistical regularities if it continuously changes. To investigate this question, we devised an fMRI paradigm where participants (N = 32) completed a visual four-choice reaction time (RT) task consisting of statistical regularities. Two types of blocks involving the same perceptual elements alternated with one another throughout the task: While the distribution of statistical regularities was predictable in one block type, it was unpredictable in the other. Participants were unaware of the presence of statistical regularities and of their changing distribution across the subsequent task blocks. Based on the RT results, although statistical regularities were processed similarly in both the predictable and unpredictable blocks, participants acquired less statistical knowledge in the unpredictable as compared with the predictable blocks. Whole-brain random-effects analyses showed increased activity in the early visual cortex and decreased activity in the precuneus for the predictable as compared with the unpredictable blocks. Therefore, the actual predictability of statistical regularities is likely to be represented already at the early stages of visual cortical processing. However, decreased precuneus activity suggests that these representations are imperfectly updated to track the multiple shifts in predictability throughout the task. The results also highlight that the processing of statistical regularities in a changing environment could be habitual.

scholarly journals Sociality and Interaction Envelope Organize Visual Action Representations

2019 ◽  
Author(s):  
Leyla Tarhan ◽  
Talia Konkle
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Action Observation ◽  
The Body ◽  
Whole Body ◽  
Action Perception ◽  
Social Content ◽  
Wide Range ◽  
The Brain

Humans observe a wide range of actions in their surroundings. How is the visual cortex organized to process this diverse input? Using functional neuroimaging, we measured brain responses while participants viewed short videos of everyday actions, then probed the structure in these responses using voxel-wise encoding modeling. Responses were well fit by feature spaces that capture the body parts involved in an action and the action’s targets (i.e. whether the action was directed at an object, another person, the actor, and space). Clustering analyses revealed five large-scale networks that summarized the voxel tuning: one related to social aspects of an action, and four related to the scale of the interaction envelope, ranging from fine-scale manipulations directed at objects, to large-scale whole-body movements directed at distant locations. We propose that these networks reveal the major representational joints in how actions are processed by visual regions of the brain.Significance StatementHow does the brain perceive other people’s actions? Prior work has established that much of the visual cortex is active when observing others’ actions. However, this activity reflects a wide range of processes, from identifying a movement’s direction to recognizing its social content. We investigated how these diverse processes are organized within the visual cortex. We found that five networks respond during action observation: one that is involved in processing actions’ social content, and four that are involved in processing agent-object interactions and the scale of the effect that these actions have on the world (its “interaction envelope”). Based on these findings, we propose that sociality and interaction envelope size are two of the major features that organize action perception in the visual cortex.

scholarly journals Perceived and mentally rotated contents are differentially represented in cortical depth of V1

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Polina Iamshchinina ◽  
Daniel Kaiser ◽  
Renat Yakupov ◽  
Daniel Haenelt ◽  
Alessandro Sciarra ◽  
...  
Keyword(s):  
Visual Cortex ◽  
High Resolution ◽  
Mental Rotation ◽  
Primary Visual Cortex ◽  
External Input ◽  
General Strategy ◽  
Cortical Layers ◽  
Early Visual Cortex ◽  
Feedback Connections ◽  
The Brain

AbstractPrimary visual cortex (V1) in humans is known to represent both veridically perceived external input and internally-generated contents underlying imagery and mental rotation. However, it is unknown how the brain keeps these contents separate thus avoiding a mixture of the perceived and the imagined which could lead to potentially detrimental consequences. Inspired by neuroanatomical studies showing that feedforward and feedback connections in V1 terminate in different cortical layers, we hypothesized that this anatomical compartmentalization underlies functional segregation of external and internally-generated visual contents, respectively. We used high-resolution layer-specific fMRI to test this hypothesis in a mental rotation task. We found that rotated contents were predominant at outer cortical depth bins (i.e. superficial and deep). At the same time perceived contents were represented stronger at the middle cortical bin. These results identify how through cortical depth compartmentalization V1 functionally segregates rather than confuses external from internally-generated visual contents. These results indicate that feedforward and feedback manifest in distinct subdivisions of the early visual cortex, thereby reflecting a general strategy for implementing multiple cognitive functions within a single brain region.

scholarly journals Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

2020 ◽  
Author(s):  
Paul Triebkorn ◽  
Joelle Zimmermann ◽  
Leon Stefanovski ◽  
Dipanjan Roy ◽  
Ana Solodkin ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Large Scale ◽  
Brain Activity ◽  
Brain Network ◽  
Model Parameters ◽  
Network Modelling ◽  
Neural Populations ◽  
Global Coupling ◽  
The Brain

AbstractUsing The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters: conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain.Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.

scholarly journals A Scalable and Modular Automated Pipeline for Stitching of Large Electron Microscopy Datasets

2021 ◽  
Author(s):  
Gayathri Mahalingam ◽  
Russel Torres ◽  
Daniel Kapner ◽  
Eric T Trautman ◽  
Tim Fliss ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Visual Cortex ◽  
High Throughput ◽  
Serial Section ◽  
Large Scale ◽  
Software Pipeline ◽  
Array Tomography ◽  
Automated Quality Control ◽  
Mouse Visual Cortex ◽  
The Brain

Serial section Electron Microscopy can produce high throughput imaging of large biological specimen volumes. The high-resolution images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. A high fidelity volume assembly is required to correctly reconstruct neural anatomy and synaptic connections. It involves seamless 2D stitching of the images within a serial section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP(Assembly Stitching and Alignment Pipeline) that is scalable and parallelized to work with distributed systems. The pipeline is built on top of the Render [18] services used in the volume assembly of the brain of adult Drosophila melanogaster [2]. It achieves high throughput by operating on the meta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. The modularity of ASAP allows for easy adaptation to new algorithms without significant changes to the workflow. The software pipeline includes a complete set of tools to do stitching, automated quality control, 3D section alignment, and rendering of the assembled volume to disk. We also implemented a workflow engine that executes the volume assembly workflow in an automated fashion triggered following the transfer of raw data. ASAP has been successfully utilized for continuous processing of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex [1, 25]. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

scholarly journals The geometry of masking in neural populations

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Dario L. Ringach
Keyword(s):  
Visual Cortex ◽  
Cortical Neurons ◽  
Dimensional Space ◽  
Geometric Analysis ◽  
Geometric Approach ◽  
Population Level ◽  
Contextual Modulation ◽  
Neural Populations ◽  
Early Visual Cortex ◽  
Low Dimensional

Abstract The normalization model provides an elegant account of contextual modulation in individual neurons of primary visual cortex. Understanding the implications of normalization at the population level is hindered by the heterogeneity of cortical neurons, which differ in the composition of their normalization pools and semi-saturation constants. Here we introduce a geometric approach to investigate contextual modulation in neural populations and study how the representation of stimulus orientation is transformed by the presence of a mask. We find that population responses can be embedded in a low-dimensional space and that an affine transform can account for the effects of masking. The geometric analysis further reveals a link between changes in discriminability and bias induced by the mask. We propose the geometric approach can yield new insights into the image processing computations taking place in early visual cortex at the population level while coping with the heterogeneity of single cell behavior.

scholarly journals Scaling of information in large neural populations reveals signatures of information-limiting correlations

2020 ◽  
Author(s):  
MohammadMehdi Kafashan ◽  
Anna Jaffe ◽  
Selmaan N. Chettih ◽  
Ramon Nogueira ◽  
Iñigo Arandia-Romero ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Stimulus ◽  
Brain Area ◽  
Correlated Noise ◽  
Neuronal Populations ◽  
Total Information ◽  
Neural Populations ◽  
The Brain ◽  
Stimulus Direction

AbstractHow is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into information-limiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations.

scholarly journals Decoding Multivoxel Representations of Affective Scenes in Retinotopic Visual Cortex

2020 ◽  
Author(s):  
Ke Bo ◽  
Siyang Yin ◽  
Yuelu Liu ◽  
Zhenhong Hu ◽  
Sreenivasan Meyyapan ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Event Related Potential ◽  
Affective Content ◽  
Neural Representations ◽  
Visual Areas ◽  
Early Visual Cortex ◽  
Decoding Accuracy

AbstractThe perception of opportunities and threats in complex scenes represents one of the main functions of the human visual system. In the laboratory, its neurophysiological basis is often studied by having observers view pictures varying in affective content. This body of work has consistently shown that viewing emotionally engaging, compared to neutral, pictures (1) heightens blood flow in limbic structures and frontoparietal cortex, as well as in anterior ventral and dorsal visual cortex, and (2) prompts an increase in the late positive event-related potential (LPP), a scalp-recorded and time-sensitive index of engagement within the network of aforementioned neural structures. The role of retinotopic visual cortex in this process has, however, been contentious, with competing theoretical notions predicting the presence versus absence of emotion-specific signals in retinotopic visual areas. The present study used multimodal neuroimaging and machine learning to address this question by examining the large-scale neural representations of affective pictures. Recording EEG and fMRI simultaneously while observers viewed pleasant, unpleasant, and neutral affective pictures, and applying multivariate pattern analysis to single-trial BOLD activities in retinotopic visual cortex, we identified three robust findings: First, unpleasant-versus-neutral decoding accuracy, as well as pleasant-versus-neutral decoding accuracy, were well above chance level in all retinotopic visual areas, including primary visual cortex. Second, the decoding accuracy in ventral visual cortex, but not in early visual cortex or dorsal visual cortex, was significantly correlated with LPP amplitude. Third, effective connectivity from amygdala to ventral visual cortex predicted unpleasant-versus-neutral decoding accuracy, and effective connectivity from ventral frontal cortex to ventral visual cortex predicted pleasant-versus-neutral decoding accuracy. These results suggest that affective pictures evoked valence-specific multivoxel neural representations in retinotopic visual cortex and that these multivoxel representations were influenced by reentry signals from limbic and frontal brain regions.

scholarly journals Rethinking brain-wide interactions through multi-region “network of networks” models

2020 ◽  
Author(s):  
Matthew Perich ◽  
Kanaka Rajan
Keyword(s):  
Recent Work ◽  
Neural Control ◽  
Large Scale ◽  
Brain Regions ◽  
Interconnected Network ◽  
Neural Basis ◽  
Theoretical Approaches ◽  
Neural Populations ◽  
The Brain ◽  
Network Of Networks

The neural control of behavior is distributed across many functionally and anatomically distinct brain regions even in small nervous systems. While classical neuroscience models treated these regions as a set of hierarchically isolated nodes, the brain comprises a recurrently interconnected network in which each region is intimately modulated by many others. Uncovering these interactions is now possible through experimental techniques that access large neural populations from many brain regions simultaneously. Harnessing these large-scale datasets, however, requires new theoretical approaches. Here, we review recent work to understand brain-wide interactions using multi-region "network of networks" models and discuss how they can guide future experiments. We also emphasize the importance of multi-region recordings, and posit that studying individual components in isolation will be insufficient to understand the neural basis of behavior.

scholarly journals The balanced and introspective brain

2017 ◽  
Vol 14 (130) ◽  
pp. 20160994 ◽  
Author(s):  
P. A. Robinson
Keyword(s):  
Cortical Neurons ◽  
Large Scale ◽  
Transfer Functions ◽  
Brain Activity ◽  
Great Majority ◽  
Feedback Loops ◽  
Brain Hemispheres ◽  
Neural Field Theory ◽  
Neural Populations ◽  
The Brain

Transfers of large-scale neural activity into, within and between corticothalamic neural populations and brain hemispheres are analysed using time-integrated transfer functions and state parameters obtained from neural field theory for a variety of arousal states. It is shown that the great majority of activity results from feedbacks within the corticothalamic system, including significant transfer between hemispheres, but only a small minority arises via net input from the external world, with the brain thus in a near-critical, highly introspective state. Notably, the total excitatory and inhibitory influences on cortical neurons are balanced to within a few per cent across arousal states. Strong negative intrahemispheric feedforward exists to the cortex, and even larger interhemispheric positive feedforward, but these are modified by feedback loops to yield near-critical positive overall gain. The results underline the utility of transfer functions for the analysis of brain activity.

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