scholarly journals Dynamic divisive normalization circuits explain and predict change detection in monkey area MT

2021 ◽  
Vol 17 (11) ◽  
pp. e1009595
Author(s):  
Udo A. Ernst ◽  
Xiao Chen ◽  
Lisa Bohnenkamp ◽  
Fingal Orlando Galashan ◽  
Detlef Wegener
Keyword(s):  
Change Detection ◽  
Temporal Dynamics ◽  
Stimulus Change ◽  
Area Mt ◽  
Mt Neurons ◽  
Selective Area ◽  
Linear Behavior ◽  
Divisive Normalization ◽  
Current Activation ◽  
The Brain

Sudden changes in visual scenes often indicate important events for behavior. For their quick and reliable detection, the brain must be capable to process these changes as independently as possible from its current activation state. In motion-selective area MT, neurons respond to instantaneous speed changes with pronounced transients, often far exceeding the expected response as derived from their speed tuning profile. We here show that this complex, non-linear behavior emerges from the combined temporal dynamics of excitation and divisive inhibition, and provide a comprehensive mathematical analysis. A central prediction derived from this investigation is that attention increases the steepness of the transient response irrespective of the activation state prior to a stimulus change, and irrespective of the sign of the change (i.e. irrespective of whether the stimulus is accelerating or decelerating). Extracellular recordings of attention-dependent representation of both speed increments and decrements confirmed this prediction and suggest that improved change detection derives from basic computations in a canonical cortical circuitry.

scholarly journals A simple canonical circuitry for non-stationary normalization by inhibition to explain and predict change detection in monkey area MT

2020 ◽  
Author(s):  
Udo Ernst ◽  
Xiao Chen ◽  
Lisa Bohnenkamp ◽  
Fingal Orlando Galashan ◽  
Detlef Wegener
Keyword(s):  
Change Detection ◽  
Temporal Dynamics ◽  
Stimulus Change ◽  
Formal Analysis ◽  
Area Mt ◽  
Mt Neurons ◽  
Selective Area ◽  
Linear Behavior ◽  
Current Activation ◽  
The Brain

AbstractSudden changes in visual scenes often indicate important events for behavior. For their quick and reliable detection, the brain must be capable to process these changes as independent as possible from its current activation state. In motion-selective area MT, neurons respond to instantaneous speed changes with pronounced transients, often far exceeding the expected response as derived from their speed tuning profile. We here show that this complex, non-linear behavior emerges from the combined temporal dynamics of excitation and divisive inhibition, and provide a comprehensive formal analysis. A central prediction derived from this investigation is that attention increases the steepness of the transient response irrespective of the activation state prior to a stimulus change, and irrespective of the sign of the change. Extracellular recordings of attention-dependent representation of both speed increments and decrements confirmed this prediction and suggest that improved change detection derives from basic computations in a canonical cortical circuitry.

Temporal Dynamics of Direction Tuning in Motion-Sensitive Macaque Area MT

2005 ◽  
Vol 93 (4) ◽  
pp. 2104-2116 ◽  
Author(s):  
János A. Perge ◽  
Bart G. Borghuis ◽  
Roger J. E. Bours ◽  
Martin J. M. Lankheet ◽  
Richard J. A. van Wezel
Keyword(s):  
Temporal Dynamics ◽  
Random Sequence ◽  
Nonlinear Effects ◽  
Direction Selectivity ◽  
Second Phase ◽  
Motion Direction ◽  
Area Mt ◽  
Mt Neurons ◽  
Static Nonlinearity ◽  
Preferred Direction

We studied the temporal dynamics of motion direction sensitivity in macaque area MT using a motion reverse correlation paradigm. Stimuli consisted of a random sequence of motion steps in eight different directions. Cross-correlating the stimulus with the resulting neural activity reveals the temporal dynamics of direction selectivity. The temporal dynamics of direction selectivity at the preferred speed showed two phases along the time axis: one phase corresponding to an increase in probability for the preferred direction at short latencies and a second phase corresponding to a decrease in probability for the preferred direction at longer latencies. The strength of this biphasic behavior varied between neurons from weak to very strong and was uniformly distributed. Strong biphasic behavior suggests optimal responses for motion steps in the antipreferred direction followed by a motion step in the preferred direction. Correlating spikes to combinations of motion directions corroborates this distinction. The optimal combination for weakly biphasic cells consists of successive steps in the preferred direction, whereas for strongly biphasic cells, it is a reversal of directions. Comparing reverse correlograms to combinations of stimuli to predictions based on correlograms for individual directions revealed several nonlinear effects. Correlations for successive presentations of preferred directions were smaller than predicted, which could be explained by a static nonlinearity (saturation). Correlations to pairs of (nearly) opposite directions were larger than predicted. These results show that MT neurons are generally more responsive when sudden changes in motion directions occur, irrespective of the preferred direction of the neurons. The latter nonlinearities cannot be explained by a simple static nonlinearity at the output of the neuron, but most likely reflect network interactions.

scholarly journals Stability and flexibility in multisensory sampling: insights from perceptual illusions

2019 ◽  
Vol 121 (5) ◽  
pp. 1588-1590 ◽  
Author(s):  
Luca Casartelli
Keyword(s):  
Temporal Dynamics ◽  
Multisensory Processing ◽  
General Idea ◽  
Perceptual Illusions ◽  
The Brain ◽  
Multisensory Processes

Neural, oscillatory, and computational counterparts of multisensory processing remain a crucial challenge for neuroscientists. Converging evidence underlines a certain efficiency in balancing stability and flexibility of sensory sampling, supporting the general idea that multiple parallel and hierarchically organized processing stages in the brain contribute to our understanding of the (sensory/perceptual) world. Intriguingly, how temporal dynamics impact and modulate multisensory processes in our brain can be investigated benefiting from studies on perceptual illusions.

scholarly journals The brain timewise: how timing shapes and supports brain function

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140170 ◽  
Author(s):  
Riitta Hari ◽  
Lauri Parkkonen
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Generative Models ◽  
Network Activity ◽  
Brain Diseases ◽  
Specific Information ◽  
Non Invasive ◽  
The Brain

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.

Encoding of three-dimensional structure-from-motion by primate area MT neurons

Nature ◽  
10.1038/33688 ◽  
1998 ◽  
Vol 392 (6677) ◽  
pp. 714-717 ◽  
Author(s):  
David C. Bradley ◽  
Grace C. Chang ◽  
Richard A. Andersen
Keyword(s):  
Structure From Motion ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Area Mt ◽  
Three Dimensional Structure ◽  
Mt Neurons

Area MT Neurons Respond to Visual Motion Distant From Their Receptive Fields

2005 ◽  
Vol 94 (6) ◽  
pp. 4156-4167 ◽  
Author(s):  
Daniel Zaksas ◽  
Tatiana Pasternak
Keyword(s):  
Time Course ◽  
Visual Motion ◽  
Cognitive Task ◽  
Receptive Fields ◽  
Direction Selectivity ◽  
Task Demands ◽  
Visual Signals ◽  
Area Mt ◽  
Mt Neurons ◽  
Stimulus Offset

Neurons in cortical area MT have localized receptive fields (RF) representing the contralateral hemifield and play an important role in processing visual motion. We recorded the activity of these neurons during a behavioral task in which two monkeys were required to discriminate and remember visual motion presented in the ipsilateral hemifield. During the task, the monkeys viewed two stimuli, sample and test, separated by a brief delay and reported whether they contained motion in the same or in opposite directions. Fifty to 70% of MT neurons were activated by the motion stimuli presented in the ipsilateral hemifield at locations far removed from their classical receptive fields. These responses were in the form of excitation or suppression and were delayed relative to conventional MT responses. Both excitatory and suppressive responses were direction selective, but the nature and the time course of their directionality differed from the conventional excitatory responses recorded with stimuli in the RF. Direction selectivity of the excitatory remote response was transient and early, whereas the suppressive response developed later and persisted after stimulus offset. The presence or absence of these unusual responses on error trials, as well as their magnitude, was affected by the behavioral significance of stimuli used in the task. We hypothesize that these responses represent top-down signals from brain region(s) accessing information about stimuli in the entire visual field and about the behavioral state of the animal. The recruitment of neurons in the opposite hemisphere during processing of behaviorally relevant visual signals reveals a mechanism by which sensory processing can be affected by cognitive task demands.

Area 17 lesions deactivate area MT in owl monkeys

1992 ◽  
Vol 9 (3-4) ◽  
pp. 399-407 ◽  
Author(s):  
Jon H. Kaas ◽  
Leah A. Krubitzer
Keyword(s):  
Visual Stimuli ◽  
Receptive Fields ◽  
Area 17 ◽  
Area Mt ◽  
Mt Neurons ◽  
Visual Hemifield ◽  
Cortex Area ◽  
Owl Monkeys ◽  
Evoked Activity ◽  
Visual Area Mt

AbstractThe middle temporal visual area, MT, is one of three major targets of the primary visual cortex, area 17, in primates. We assessed the contribution of area 17 connections to the responsiveness of area MT neurons to visual stimuli by first mapping the representation of the visual hemifield in MT of anesthetized owl monkeys with microelectrodes, ablating an electrophysiologically mapped part of area 17, and then immediately remapping MT. Before the lesions, neurons at recording sites throughout MT responded vigorously to moving slits of light and other visual stimuli. In addition, the relationship of receptive fields to recording sites revealed a systematic representation of the contralateral visual hemifield in MT, as reported previously for owl monkeys and other primates. The immediate effect of removing part of the retinotopic map in area 17 by gentle aspiration was to selectively deactivate the corresponding part of the visuotopic map in MT. Lesions of dorsomedial area 17 representing central and paracentral vision of the lower visual quadrant deactivated neurons in caudomedial MT formerly having receptive fields in the central and paracentral lower visual quadrant. Most neurons at recording sites throughout other parts of MT had normal levels of responsiveness to visual stimuli, and receptive-field locations that closely matched those before the lesion. However, neurons at a few sites along the margin of the deactivated zone of cortex had receptive fields that were slightly displaced from the region of vision affected by the lesion into other parts of the visual field, suggesting some degree of plasticity in the visual hemifield representation in MT. Subsequent histological examination of cortex confirmed that the lesions were confined to area 17 and the recordings were in MT. The results indicate that the visually evoked activity of neurons in MT of owl monkeys is highly dependent on inputs relayed directly or indirectly from area 17.

scholarly journals Using the past to estimate sensory uncertainty

2019 ◽  
Author(s):  
Ulrik Beierholm ◽  
Tim Rohe ◽  
Ambra Ferrari ◽  
Oliver Stegle ◽  
Uta Noppeney
Keyword(s):  
Temporal Dynamics ◽  
Visual Signals ◽  
Perceptual Inference ◽  
Reliability Estimates ◽  
Uncertainty Estimates ◽  
Classical Models ◽  
Past Experiences ◽  
Combine Information ◽  
Sensory Uncertainty ◽  
The Brain

AbstractTo form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.In a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discountingOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

Sign in / Sign up

Export Citation Format

Share Document