Area MSTd Research Articles

Vestibular Inputs to Human Motion-Sensitive Visual Cortex

Two crucial sources of information available to an organism when moving through an environment are visual and vestibular stimuli. Macaque cortical area MSTd processes visual motion, including cues to self-motion arising from optic flow and also receives information about self-motion from the vestibular system. In humans, whether human MST (hMST) receives vestibular afferents is unknown. We have combined 2 techniques, galvanic vestibular stimulation and functional MRI (fMRI), to show that hMST is strongly activated by vestibular stimulation in darkness, whereas adjacent area MT is unaffected. The activity cannot be explained in terms of somatosensory stimulation at the electrode site. Vestibular input appears to be confined to the anterior portion of hMST, suggesting that hMST as conventionally defined may contain 2 subregions. Vestibular activity was also seen in another area previously implicated in processing visual cues to self-motion, namely the cingulate sulcus visual area (CSv), but not in visual area V6. The results suggest that cross-modal convergence of cues to self-motion occurs in both hMST and CSv.

Direction and speed tuning to visual motion in cortical areas MT and MSTd during smooth pursuit eye movements

When tracking a moving target in the natural world with pursuit eye movement, our visual system must compensate for the self-induced retinal slip of the visual features in the background to enable us to perceive their actual motion. We previously reported that the speed of the background stimulus in space is represented by dorsal medial superior temporal (MSTd) neurons in the monkey cortex, which compensate for retinal image motion resulting from eye movements when the direction of the pursuit and background motion are parallel to the preferred direction of each neuron. To further characterize the compensation observed in the MSTd responses to the background motion, we recorded single unit activities in cortical areas middle temporal (MT) and MSTd, and we selected neurons responsive to a large-field visual stimulus. We studied their responses to the large-field stimulus in the background while monkeys pursued a moving target and while fixated a stationary target. We investigated whether compensation for retinal image motion of the background depended on the speed of pursuit. We also asked whether the directional selectivity of each neuron in relation to the external world remained the same even during pursuit and whether compensation for retinal image motion occurred irrespective of the direction of the pursuit. We found that the majority of the MSTd neurons responded to the visual motion in space by compensating for the image motion on the retina resulting from the pursuit regardless of pursuit speed and direction, whereas most of the MT neurons responded in relation to the genuine retinal image motion.

The spiking irregularity of MSTd neurons depends on visual and oculomotor variables

Event Abstract Back to Event The spiking irregularity of MSTd neurons depends on visual and oculomotor variables Lukas Brostek1, 2*, Ulrich Büttner1, 3, Michael J. Mustari4 and Stefan Glasauer1, 2, 3 1 LMU, Clinical Neurosciences, Germany 2 BCCN Munich, Germany 3 LMU, Integrated Center for Research and Treatment of Vertigo, Germany 4 University of Washington, Washington National Primate Research Center, United States Neuronal activity in macaque cortical area MSTd is driven by retinal stimuli, as almost all neurons respond to moving large-field patterns. At the same time, the spiking activity in MSTd is modulated by an internally generated signal related to the monkey’s eye movements. This combination of both visual motion and eye movement related activity makes MSTd an ideal system for analyzing neuronal activity in dependence on different stimulus dimensions. In this work we analyzed the inter- and intra-trial variability of spiking activity by measuring Fano factor (FF), squared coefficient of variation (CV²) and local variation (LV). Two different paradigms were used: fixation with visual stimulation and optokinetic response to a moving large-field stimulus. For the first condition our results complied with a recent neurophysiological study reporting stimulus-related decline in neuronal variability. The second condition, however, revealed opposing behavior. We found that both stimulus variables, retinal image velocity and eye velocity, differentially affected neuronal variability. Spiking irregularity decreased when image velocity increased and eye velocity was kept low, but increased with increasing eye velocity and low image velocity. This finding might reflect a specificity of the local network structure or could be related to the variability characteristics of the input signals. Nevertheless, the observed behaviour may also have functional importance reflecting additional timing-based coding independent of the neuron's mean firing rate. Acknowledgements This work was supported by the German Federal Ministry of Education and Research Grants 01GQ0440 (BCCN), 01EO0901 (IFB), and National Institutes of Health Grants EY06069, RR000166. Keywords: eye movement control, Fano factor, MST, neuronal variability, spiking irregularity, Visual System Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Poster Topic: neural encoding and decoding (please use "neural coding and decoding" as keyword) Citation: Brostek L, Büttner U, Mustari MJ and Glasauer S (2011). The spiking irregularity of MSTd neurons depends on visual and oculomotor variables. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00228 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 Aug 2011; Published Online: 04 Oct 2011. * Correspondence: Mr. Lukas Brostek, LMU, Clinical Neurosciences, München, Germany, Lukas.Brostek@lrz.uni-muenchen.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Lukas Brostek Ulrich Büttner Michael J Mustari Stefan Glasauer Google Lukas Brostek Ulrich Büttner Michael J Mustari Stefan Glasauer Google Scholar Lukas Brostek Ulrich Büttner Michael J Mustari Stefan Glasauer PubMed Lukas Brostek Ulrich Büttner Michael J Mustari Stefan Glasauer Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

An information-theoretic approach for evaluating probabilistic tuning functions of single neurons

Neuronal tuning functions can be expressed by the conditional probability of observing a spike given any combination of explanatory variables. However, accurately determining such probabilistic tuning functions from experimental data poses several challenges such as finding the right combination of explanatory variables and determining their proper neuronal latencies. Here we present a novel approach of estimating and evaluating such probabilistic tuning functions, which offers a solution for these problems. By maximizing the mutual information between the probability distributions of spike occurrence and the variables, their neuronal latency can be estimated, and the dependence of neuronal activity on different combinations of variables can be measured. This method was used to analyze neuronal activity in cortical area MSTd in terms of dependence on signals related to eye and retinal image movement. Comparison with conventional feature detection and regression analysis techniques shows that our method offers distinct advantages, if the dependence does not match the regression model.

Area MSTd Neurons Encode Visual Stimuli in Eye Coordinates During Fixation and Pursuit

Visual signals generated by self-motion are initially represented in retinal coordinates in the early parts of the visual system. Because this information can be used by an observer to navigate through the environment, it must be transformed into body or world coordinates at later stations of the visual-motor pathway. Neurons in the dorsal aspect of the medial superior temporal area (MSTd) are tuned to the focus of expansion (FOE) of the visual image. We performed experiments to determine whether focus tuning curves in area MSTd are represented in eye coordinates or in screen coordinates (which could be head, body, or world-centered in the head-fixed paradigm used). Because MSTd neurons adjust their FOE tuning curves during pursuit eye movements to compensate for changes in pursuit and translation speed that distort the visual image, the coordinate frame was determined while the eyes were stationary (fixed gaze or simulated pursuit conditions) and while the eyes were moving (real pursuit condition). We recorded extracellular responses from 80 MSTd neurons in two rhesus monkeys (Macaca mulatta). We found that the FOE tuning curves of the overwhelming majority of neurons were aligned in an eye-centered coordinate frame in each of the experimental conditions [fixed gaze: 77/80 (96%); real pursuit: 77/80 (96%); simulated pursuit 74/80 (93%); t-test, P < 0.05]. These results indicate that MSTd neurons represent heading in an eye-centered coordinate frame both when the eyes are stationary and when they are moving. We also found that area MSTd demonstrates significant eye position gain modulation of response fields much like its posterior parietal neighbors.

Decoding of MSTd Population Activity Accounts for Variations in the Precision of Heading Perception

Humans and monkeys use both vestibular and visual motion (optic flow) cues to discriminate their direction of self-motion during navigation. A striking property of heading perception from optic flow is that discrimination is most precise when subjects judge small variations in heading around straight ahead, whereas thresholds rise precipitously when subjects judge heading around an eccentric reference. We show that vestibular heading discrimination thresholds in both humans and macaques also show a consistent, but modest, dependence on reference direction. We used computational methods (Fisher information, maximum likelihood estimation, and population vector decoding) to show that population activity in area MSTd predicts the dependence of heading thresholds on reference eccentricity. This dependence arises because the tuning functions for most neurons have a steep slope for directions near straight forward. Our findings support the notion that population activity in extrastriate cortex limits the precision of both visual and vestibular heading perception.

Role of area MSTd in cue integration for heading discrimination: II. Analysis of correlations between neural responses and perceptual decisions

Neurons in area MSTd exhibit spatial tuning in response to optic flow and during inertial motion in darkness. To better understand MSTd's role in heading perception, we studied correlations between single-unit responses and perceptual choices during a fine heading discrimination task. Monkeys reported their heading relative to straight ahead in a Vestibular condition (inertial motion only), a Visual condition (optic flow only), and a Combined condition (congruent inertial motion and optic flow). To assess functional coupling between MSTd responses and perceptual decisions, we computed ‘choice probabilities’ (CPs), which characterize trial-to-trial covariation between neural responses and the animal's choices. Overall, CPs were strongest under the Vestibular condition, in which ∼25% of MSTd neurons showed significant positive effects and the mean CP (0.55) was significantly > 0.5 (p<0.001). Under the Visual condition, ∼16% of neurons showed significant CPs with similar numbers of positive and negative effects. The mean CP (0.51) was not significantly different from chance (p=0.2). Interestingly, ‘congruent’ MSTd neurons with matched visual and vestibular tuning preferences tended to be positively correlated with choices in the Visual condition (mean CP=0.56, p=0.002), whereas neurons with opposite tuning preferences tended to be negatively correlated (mean CP=0.45, p=0.04). Our findings suggest that vestibular signals in area MSTd contribute to heading perception. In addition, the Visual CP data suggest that visual responses of MSTd neurons are read out differently depending on the congruency of visual and vestibular tuning preferences. This highlights the importance of studying multi-sensory integration for understanding heading perception.

Role of area MSTd in cue integration for heading discrimination: I. Comparison of neuronal and psychophysical sensitivity to visual and vestibular cues

Robust perception of heading involves integration of visual and non-visual (e.g., vestibular) cues. Area MSTd is thought to be involved in heading perception, as neurons in this area are sensitive to global patterns of optic flow as well as translation in darkness. To examine how visual and vestibular signals in MSTd contribute to heading perception, we recorded single-unit responses during a fine heading discrimination task. Heading direction was varied in small steps around straight forward in the horizontal plane. The task was performed in a virtual reality system and heading was defined in 3 ways: 1) inertial motion only (Vestibular condition); 2) optic flow only (Visual condition); and 3) congruent combination of inertial motion and optic flow (Combined condition). Stimuli were smooth motion trajectories with a Gaussian velocity profile. Psychophysical thresholds averaged ∼2° in the Vestibular condition. Thresholds in the Visual condition were well below 1° for coherent motion, but were adjusted to match vestibular thresholds by reducing motion coherence. The most sensitive MSTd neurons had thresholds close to behavior, but the average neuron was much less sensitive than the monkey in both single-cue conditions. In the Combined condition, psychophysical thresholds were significantly improved compared to the single-cue conditions. Thresholds for ‘congruent’ MSTd neurons with matched visual and vestibular tuning preferences were significantly improved under cue combination, whereas thresholds for neurons with opposite tuning preferences were not. We conclude that selective pooling of responses of ‘congruent’ MSTd neurons may contribute to cue integration for heading perception.

Noise correlations in area MSTd are weakened in animals trained to perform a discrimination task

Noise correlations in area MSTd are weakened in animals trained to perform a discrimination task

Neural correlates of dynamic sensory cue re-weighting in macaque area MSTd

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Evaluating Neuronal Tuning Functions by Mutual Information Maximization

Event Abstract Back to Event Evaluating Neuronal Tuning Functions by Mutual Information Maximization Lukas Brostek1, 2*, Thomas Eggert3, Seiji Ono4, Michael J. Mustari4, Ulrich Büttner1, 2 and Stefan Glasauer1, 2, 5 1 Ludwig-Maximilians-Universität, Clinical Neurosciences, Germany 2 Ludwig-Maximilians-Universität, Bernstein Center for Computational Neuroscience, Germany 3 Ludwig-Maximilians-Universität, Deptartment of Neurology, Germany 4 University of Washington, Washington National Primate Research Center, United States 5 Ludwig-Maximilians-Universität, Integrated Center for Research and Treatment of Vertigo, Germany A neuronal tuning function describes the rate of spiking activity in a neuron depending on one or multiple variables. It can be expressed by the conditional probability of observing a spike given any combination of the variables. Using Bayes' theorem, this probability can be estimated by the joint probability of spike and variable combinations and the a-priori probability of the variables (Fig. 1). Both are available experimentally.A difficulty in the analysis of neuronal data is the estimation of proper latency values between any of the variables and the neuronal activity. Appropriate estimation of neuronal latencies is important, since the choice of these latency values has great influence on the tuning function.A common approach to the problem of latency estimation is regression analysis using a linear, quadratic, or any other model. To overcome the limitations of model-based system identification we developed an information-theoretic approach. By adjusting the latencies to maximize the mutual information between the probability distribution of the variables and that of the spike occurrence, the dependence of the spike on the input variables is maximized as well.This new approach allows the estimation of neuronal latencies free of model assumptions. It was used to analyze the dependence of neuronal activity in cortical area MSTd on signals related to movement of the eye and retinal image movement (position/velocity/acceleration). The results show that the neuronal activity in this area is non-linearly related to combinations of the considered eye movement and retinal image movement variables. The estimated latencies agree well with results based on other approaches. Compared to commonly used methods, this new approach is very robust to noise and correlations in the input variables and can be applied to every kind of stimuli design. Fig. 1: Bayesian approach for 2D tuning function determination demonstrated for MSTd data. (A) Probability mass function pV(v) of the occurrence of combinations of mutual independent variables image and eye velocity. (B) Joint probability mass function pV,S(v,s) of coincident variable and spike occurrence, considering the estimated neuronal latencies of our mutual information maximization approach. (C) Dividing pV,S(v, s) by pV(v) yields the conditional probability pS|V(s|v) of observing a spike given any combination of the variables. Figure 1 Acknowledgements Funded by BMBF (BCCN 01GQ0440, IFB 01EO0901). Keywords: computational neuroscience Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010. Presentation Type: Presentation Topic: Bernstein Conference on Computational Neuroscience Citation: Brostek L, Eggert T, Ono S, Mustari MJ, Büttner U and Glasauer S (2010). Evaluating Neuronal Tuning Functions by Mutual Information Maximization. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00039 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Sep 2010; Published Online: 23 Sep 2010. * Correspondence: Mr. Lukas Brostek, Ludwig-Maximilians-Universität, Clinical Neurosciences, Munich, Germany, Lukas.Brostek@lrz.uni-muenchen.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Lukas Brostek Thomas Eggert Seiji Ono Michael J Mustari Ulrich Büttner Stefan Glasauer Google Lukas Brostek Thomas Eggert Seiji Ono Michael J Mustari Ulrich Büttner Stefan Glasauer Google Scholar Lukas Brostek Thomas Eggert Seiji Ono Michael J Mustari Ulrich Büttner Stefan Glasauer PubMed Lukas Brostek Thomas Eggert Seiji Ono Michael J Mustari Ulrich Büttner Stefan Glasauer Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A model of neural mechanisms in monocular transparent motion perception

Transparent motion is perceived when multiple motions are presented in the same part of visual space that move in different directions or with different speeds. Several psychophysical as well as physiological experiments have studied the conditions under which motion transparency occurs. Few computational mechanisms have been proposed that allow to segregate multiple motions. We present a novel neural model which investigates the necessary mechanisms underlying initial motion detection, the required representations for velocity coding, and the integration and segregation of motion stimuli to account for the perception of transparent motion. The model extends a previously developed architecture for neural computations along the dorsal pathway, particularly, in cortical areas V1, MT, and MSTd. It emphasizes the role of feedforward cascade processing and feedback from higher to earlier processing stages for selective feature enhancement and tuning. Our results demonstrate that the model reproduces several key psychophysical findings in perceptual motion transparency using random dot stimuli. Moreover, the model is able to process transparent motion as well as opaque surface motion in real-world sequences of 3-d scenes. As a main thesis, we argue that the perception of transparent motion relies on the representation of multiple velocities at one spatial location; however, this feature is necessary but not sufficient to perceive transparency. It is suggested that the activations simultaneously representing multiple activities are subsequently integrated by separate mechanisms leading to the segregation of different overlapping segments.

Responses of MSTd and MT Neurons during Smooth Pursuit Exhibit Similar Temporal Frequency Dependence on Retinal Image Motion

When our eyes are in constant motion, the world around us remains perceptually stable; although eye movements produce slips of the visual scene on our retinae. In our previous study, we suggested that visual motion in space is served by neurons, which compensate retinal-image motion due to pursuit eye movements, in the dorsal part of the medial superior temporal (MSTd) area. Additionally, neurons in the middle temporal (MT) area respond to retinal-image motion. In the present study, to further elucidate the visual properties of MSTd/MT neurons, we investigated the neuronal response to the motion of checkerboard patterns (CBPs) in addition to the random-dot pattern used in the previous study. We found that neuronal responses in both areas decreased regardless of fixation or pursuit when the temporal frequency of the CBPs exceeded 20 Hz on the retina. Our results support the idea that pursuit-speed compensation observed in area MSTd might be formed by the reception of retina-based visual information from MT neurons because both areas MT and MSTd were dependent on retina-based information during pursuit eye movements.

Does the Middle Temporal Area Carry Vestibular Signals Related to Self-Motion?

Recent studies have described vestibular responses in the dorsal medial superior temporal area (MSTd), a region of extrastriate visual cortex thought to be involved in self-motion perception. The pathways by which vestibular signals are conveyed to area MSTd are currently unclear, and one possibility is that vestibular signals are already present in areas that are known to provide visual inputs to MSTd. Thus, we examined whether selective vestibular responses are exhibited by single neurons in the middle temporal area (MT), a visual motion-sensitive region that projects heavily to area MSTd. We compared responses in MT and MSTd to three-dimensional rotational and translational stimuli that were either presented using a motion platform (vestibular condition) or simulated using optic flow (visual condition). When monkeys fixated a visual target generated by a projector, half of MT cells (and most MSTd neurons) showed significant tuning during the vestibular rotation condition. However, when the fixation target was generated by a laser in a dark room, most MT neurons lost their vestibular tuning whereas most MSTd neurons retained their selectivity. Similar results were obtained for free viewing in darkness. Our findings indicate that MT neurons do not show genuine vestibular responses to self-motion; rather, their tuning in the vestibular rotation condition can be explained by retinal slip due to a residual vestibulo-ocular reflex. Thus, the robust vestibular signals observed in area MSTd do not arise through inputs from area MT.

Neuronal responses in the cortical area MSTd during smooth pursuit and ocular following eye movements

The motion sensitive medial superior temporal (MST) area has been implicated in a variety of functions such as smooth pursuit (SP) generation, self-motion perception, and coding of visual target motion. For self-motion perception, the dorsal part of MST (MSTd) is crucial [1], e.g., neurons in MSTd respond to optic flow simulating selfmotion. However, about 35% of MSTd neurons also respond to smooth pursuit eye movements, which is difficult to reconcile in the context of self-motion perception. Here, we examined neuronal responses of 55 pursuit-sensitive MSTd neurons recorded in two awake macaque monkeys [2], and related these responses to a computational model of the smooth pursuit system [3].

Neural mechanisms of visual motion integration and ego-motion estimation - a modelling investigation

Event Abstract Back to Event Neural mechanisms of visual motion integration and ego-motion estimation - a modelling investigation Problem During locomotion, temporally varying light patterns of the ambient optic array impinge the retina to generate the so called optical flow. Characteristic global patterns of optical flow are produced by different forms of self-motion (Gibson, The perception of the visual world, 1950). How is the steering and spatial navigation through a complex environment controlled by the visual processing of optical flow? Neural analysis of flow is mainly realized in areas V1, MT, MSTd, and MST along the dorsal pathway in visual cortex. Yet, the interaction of cells in the different areas and the interaction with form representations in the ventral pathway remains an ongoing research topic. In particular, it is unclear how the visual processes adapt and how they generate incrementally more complex representations as a function of navigational steering behaviours. Methods We present a dynamic model of primate motion analysis to solve navigational tasks and discuss how more complex and distributed representations are recruited when more detailed information is required, e.g., to avoid moving obstacles in cluttered scenes. Unlike previous approaches our model combines mechanisms of optical flow detection and integration with mechanisms of ego-motion estimation. The proposed model consists of model areas V1, MT and MST, to simultaneously integrate and segregate motion into different components, and is augmented by a heading map which is fed by MSTd responses. Initial processing detects raw motion from spatio-temporal correlations utilizing a bank of Gabor filters. These responses are integrated in area MT to build a distributed velocity representation of speed and direction (Rodman and Albright, Vision Research, 27, 1987). Local center-surround interactions allow the representation of multiple motions at one spatial location in MT. Activity is integrated by cells over large spatial regions of the visual field (Duffy and Wurtz, J. of Neurophysiology, 65, 1991; Graziano et al., J. of Neuroscience, 14, 1994). Unlike previous approaches, the model selectively integrates different speed channels with similar direction selectivity to robustly estimate heading direction independent of scene depth (Perrone and Stone, Vision Research, 34, 1994). On the other hand, different speeds are integrated to segregate different object motions, unlike (Mingolla et al., J. of Vision [Abstracts], 8, 2008). Results and Conclusion Model simulations demonstrate how the model performs in navigational tasks in which a simulated agent approaches a target. It is demonstrated how mechanisms of motion detection in model V1 and MT integrate and segregate distinct object motions and their direction. Systematic errors in heading estimation occur when translational observer motion is overlaid by rotations or when independently moving objects disturb the motion pattern (Royden et al., Vision Research, 34, 1994). Motion of independent objects can be indicated by the response of motion contrast cells. The model has been probed with synthetic as well as real sequences such as those acquired by a camera mounted in a moving car in different traffic scenarios. Future work will focus on the question how the distributed representations influence the decision-making to control the behavioural strategies during locomotion. Supported by EU-Project 027198(DiM) and Graduate School Univ.Ulm. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Neural mechanisms of visual motion integration and ego-motion estimation - a modelling investigation. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.090 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Feb 2009; Published Online: 02 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Neural response latency of smooth pursuit responsive neurons in cortical area MSTd

Event Abstract Back to Event Neural response latency of smooth pursuit responsive neurons in cortical area MSTd Lukas Brostek1*, Seiji Ono2, Michael J. Mustari2, Ulrich Buttner1 and Stefan Glasauer1 1 Bernstein Center for Computational Neuroscience, Germany 2 Yerkes National Primate Research Center, United States Lesion and microstimulation studies in primate cortices have shown that the medial superior temporal (MST) area is involved in the control of smooth pursuit (SP) eye movements. The lateral part of MST (MSTl) has been implicated in the coding of visual target motion [1] used to drive pursuit responses. The role of the dorsal part of MST (MSTd) in the generation of SP eye movements is highly disputed, even though about one third of MSTd neurons show strong neuronal responses to visual pursuit stimuli. Experimental evidence, for example by blanking of the target, suggested that these responses contain an extraretinal component. It has therefore been suggested that the pursuit-related neurons in MSTd may code an estimate of gaze velocity [2].Computational models of SP control posit that an efference copy of the ocular motor command is used to generate an estimate of eye velocity via an internal plant model. The estimate of target motion is constructed by adding the retinal error velocity to this signal. Simulations of our dual pathway SP control model [3] show that for stability reasons the delay of the estimated eye velocity signal with respect to the eye motor command should approach the sum of latencies in the primary retinal feedback loop, i.e., the latency between target motion and eye movement, which exhibits multi-trial mean values between 100 and 150 ms. Indeed, we recently showed that on average eye velocity related neuronal signals in MSTd lag behind eye motion with a remarkably similar latency [4]. Thus, SP-related neurons in MSTd may code an estimate of eye velocity suited to reconstruct target motion in MSTl.Based on these observations, we hypothesized that if SP-related neurons carry a signal derived from an efference copy, then the delay of SP-related neurons must be related to the eye movement latency on a trial-to-trial basis. This relation could either be a constant delay or a linear relation reflecting the actual variation of the eye movement latency. We examined the responses of pursuit-sensitive MSTd neurons to step-ramp pursuit (laser spot, 4 target velocities, max. 30°/s) recorded in two awake macaque monkeys. The latency of eye movement and the delay of neuronal response with respect to target motion onset were determined for each trial.The neuronal latency with respect to target onset correlated significantly with eye movement latency, thus supporting our hypothesis of an efferent copy origin of the MSTd signal. Further analysis showed that the neuronal latency lagged behind eye movement onset by a constant value of 100 to 150 ms, and did not reflect trial-to-trial variations in eye movement latency. Thus, the delay mechanism between the efference copy of the eye motor command and the estimate of eye velocity works independently of the variable latency in pursuit eye movement onset.

A neural model of motion gradient detection for visual navigation

Event Abstract Back to Event A neural model of motion gradient detection for visual navigation Florian Raudies1*, Stefan Ringbauer1 and Heiko Neumann1 1 University of Ulm, Institute of Neural Information Processing, Germany Problem: Spatial navigation based on visual input (Fajen and Warren, TICS, 4, 2000) is important for tasks like steering towards a goal or collision avoidance of stationary as well as independently moving objects (IMOs), respectively. Such observer movement induces global motion patterns while obstacles and IMOs lead to local disturbances in the optical flow. How is this information about flow changes used to support navigation and what are the neural mechanisms which produce this functionality? Method:A biologically inspired model is proposed to estimate and integrate optical flow from a spatio-temporal sequence of images. This model employs a log-polar velocity space, where optical flow is represented using a population code (Raudies & Neumann, Neurocomp, 2008). By extending the model proposed in (Ringbauer et al., ICANN, 2007), motion gradients are locally calculated with respect to the flow direction (tangential) on the basis of population encoded optical flow. Gradients themselves are encoded in a population of responses for angular and speed differences which were independent of the underlying flow direction (Tsotsos, CVIU, 100, 2005). For motion prediction, estimated motion is modified according to the gradient responses and is fed back into the motion processing loop. Local flow changes estimated in model area MT are further integrated in model area MSTd to represent global motion patterns (Graziano, J. of Neuroscience, 14, 1994). Results:The proposed model was probed with several motion sequences, such as the flowergarden sequence (http://www-bcs.mit.edu/people/jyawang/demos/garden-layer/orig-seq.html) which contains motion parallax at different spatial scales. It is shown that motion parallax occurs in conjunction with occlusions and disocclusions, e.g. when the foreground is moving faster than the background. Employing motion gradients, disocclusions are detected as locations of local acceleration and occlusions as deceleration in model area MT (supplementary Fig.1). More complex configurations occur at motion boundaries of an IMO. A sequence is investigated which contains a rectangular IMO in front of a wall which is observed during slightly sidewards deflected forward movement. As in the flowergarden sequence local occlusions and disocclusions are detected at vertical boundaries of the IMO in model area MT. Additionally, not only the discriminating speed is encoded by the gradients but also the angular difference. Thus, gradients encode how different parts of foreground and background are moving relative to each other. Moreover, model area MST signals a global motion pattern of expansion as an indicator of spatial observer forward motion (supplementary Fig. 2). Conclusion:The role of motion gradients in navigation is twofold: (i) at model area MT local motion changes (e.g. accelerations/decelerations) are detected indicating obstacle or IMO boundaries while (ii) at model area MST global motion patterns (e.g. expansion) are encoded. If an IMO is present in the input sequence, this leads to the occurrence of motion gradients always; however, motion gradients are also detected in cases if no IMO is present, e.g. at depth discontinuities. Acknowledgments:Supported by BMBF 01GW0763(BPPL); Grad.School Univ.Ulm. Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009. Presentation Type: Poster Presentation Topic: Abstracts Citation: Raudies F, Ringbauer S and Neumann H (2009). A neural model of motion gradient detection for visual navigation. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.018 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 25 Aug 2009; Published Online: 25 Aug 2009. * Correspondence: Florian Raudies, University of Ulm, Institute of Neural Information Processing, Ulm, Germany, florian.raudies@uni-ulm.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Florian Raudies Stefan Ringbauer Heiko Neumann Google Florian Raudies Stefan Ringbauer Heiko Neumann Google Scholar Florian Raudies Stefan Ringbauer Heiko Neumann PubMed Florian Raudies Stefan Ringbauer Heiko Neumann Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Decision making and perceptual learning for speed discrimination

Event Abstract Back to Event Decision making and perceptual learning for speed discrimination Problem Perceptual learning in visual tasks can improve the performance of decision making (Dosher et al., Psychological Review, 112, 2005). Instead of static inputs we investigate motion stimuli, particularly perceptual learning for motion speed discrimination and how improved performance can be transferred between different motion patterns. We utilize configurations of moving random dot patterns simultaneously displayed in four quadrants (central fixation) where in one quadrant a coherent motion pattern (target) was displayed while the others contain random motion. In a 2AFC task of two subsequent displays a decision needs to be made about which presentation contained the faster coherent stimulus speed. Through learning (using one target pattern) subjects can significantly improve their discrimination performance. The goal is to evaluate whether performance is still improved when the coherent motion pattern is changed. Methods We propose a neural model of motion perception which consists of a hierarchy of areas to represent the main cortical processing stages along the dorsal pathway, namely V1, MT, and MSTd (Bayerl & Neumann, Neural Computation, 16, 2004; Ringbauer et al., LNCS 4669, 2007). Optical flow is detected in area V1 and integrated in area MT by speed and direction sensitive neurons. Global motion patterns are spatially integrated subsequently in model area MSTd cells which are sensitive to rotational, radial, and laminar motion patterns (Graziano et al., J. of Neuroscience, 14, 1994). Model MSTd cells project to dorso-lateral area LIP where cells temporally accumulate responses to judge and discriminate different motion configurations (Hanks et al., Nature Neuroscience, 9, 2006). In our model speed activity is integrated over all directions generating direction independent speed profiles for different quadrants. Such responses are spatially integrated over two presentation phases with different speed parameterizations. Activities of model MSTd pattern cells allow to identify the target quadrant together with a confidence measure. Speed-selective responses are integrated separately with a bias on high speeds and are each fed forward to decision units in model LIP neurons. Here, a recurrent competitive field of neuron pools exists tuned for different speeds (Grossberg & Pilly, Vision Research, 48, 2008). The decision depends on both the input stimulus and the strength of mutual inhibition between decision cells each controlled by a weighted difference of the two speed profiles. These weights result from a trial by trial adaptation of the characteristics of the speed differences to support and enhance the decision making process. Results and Conclusion After several trials of training the model shows a decreased reaction time. Small speed differences can be discriminated more accurately even if the target pattern has been changed against another that has not been probed during training. Thus the model predicts that improved speed discrimination performance after perceptual learning using a selected motion pattern can be transferred to other patterns. A psychophysical experiment with human participants using the same visual stimuli is currently being developed to compare resulting data with the model predictions and to adapt the model parameters in accordance to the experimental findings. Funding: BMBF 01GW0763(BPPL); Grad.School Univ.Ulm Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Decision making and perceptual learning for speed discrimination. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.342 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 10 Feb 2009; Published Online: 10 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Clustering of Self-Motion Selectivity and Visual Response Properties in Macaque Area MSTd

Neurons in the dorsal subdivision of the medial superior temporal area (MSTd) show directionally selective responses to both visual (optic flow) and vestibular stimuli that correspond to translational or rotational movements of the subject. Previous work has shown that MSTd neurons are clustered within the cortex according to their directional preferences for optic flow, suggesting that there may be a topographic mapping of self-motion vectors in MSTd. If MSTd provides a multisensory representation of self-motion information, then MSTd neurons may also be expected to show clustering according to their directional preferences for vestibular signals, but this has not been tested previously. We have examined clustering of vestibular signals by comparing the tuning of isolated single units (SUs) with the undifferentiated multiunit (MU) activity of several neighboring neurons recorded from the same microelectrode. We find that directional preferences for both translational and rotational vestibular stimuli, like those for optic flow, are clustered within area MSTd. MU activity often shows significant tuning for vestibular stimuli, although this MU selectivity is generally weaker for translation than for rotation. When directional tuning is observed in MU activity, the direction preference generally agrees closely with that of a simultaneously recorded SU. We also examined clustering of visual receptive field properties in MSTd by analyzing receptive field maps obtained using a reverse-correlation technique. We find that both the local directional preferences and overall spatial receptive field profiles are well clustered in MSTd. Overall, our findings have implications for how visual and vestibular signals regarding self-motion may be decoded from populations of MSTd neurons.