- Amiram Grinvald, Edmund E. Lieke, Ron D. Frostig, Rina Hildesheim. Cortical Point-Spread Function and Long-Range Lateral Interactions Revealed by Real-Time Optical Imaging of Macaque Monkey Primary Visual Cortex. Journal of Neuroscience, 14(5):2545--68, 1994, abstract
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- Amiram Grinvald, D. Shoham, A. Shmuel, D. Glaser, I. Vanzetta, E. Shtoyerman, H. Slovin, A. Sterkin. In-vivo Optical imaging of cortical architecture and dynamics.. In Modern Techniques in Neuroscience Research, U. Windhorst and H. Johansson (Editors) Springer Verlag, 2001 abstract
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- Dirk Jancke, Frédéric Chavane, Shmuel Naaman, Amiram Grinvald. Imaging cortical correlates of illusion in early visual cortex. Nature, 428:423--6, 2004, abstract
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- Aaditya V. Rangan, David Cai, David W. McLaughlin. Modeling the spatiotemporal cortical activity associated with the line-motion illusion in primary visual cortex, URL . Proceedings of the National Academy of Sciences USA, 102(52):18793---800, 2005 abstract
Our large-scale computational model of the primary visual cortex that incorporates orientation-specific, long-range couplings with slow NMDA conductances operates in a fluctuating dynamic state of intermittent desuppression (IDS), which captures the behavior of coherent spontaneous cortical activity, as revealed by in vivo optical imaging based on voltage-sensitive dyes. Here, we address the functional significance of the IDS cortical operating points by investigating our model cortex response to the Hikosaka line- motion illusion (LMI) stimulus---a cue of a quickly flashed station- ary square followed a few milliseconds later by a stationary bar. As revealed by voltage-sensitive dye imaging, there is an intriguing similarity between the cortical spatiotemporal activity in response to (i) the Hikosaka LMI stimulus and (ii) a small moving square. This similarity is believed to be associated with the preattentive illusory motion perception. Our numerical cortex produces similar spatio- temporal patterns in response to the two stimuli above, which are both in very good agreement with experimental results. The essential network mechanisms underpinning the LMI phenomenon in our model are (i) the spatiotemporal structure of the LMI input as sculpted by the lateral geniculate nucleus, (ii) a priming effect of the long-range NMDA-type cortical coupling, and (iii) the NMDA conductance--voltage correlation manifested in the IDS state. This mechanism in our model cortex, in turn, suggests a physiological underpinning for the LMI-associated patterns in the visual cortex of anaesthetized cat. ,
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- David Cai, Aaditya V. Rangan, David W. McLaughlin. Architectural and synaptic mechanisms underlying coherent spontaneous activity in V1. Proceedings of the National Academy of Sciences USA, 102(16):5868--73, 2005, abstract
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- Laurent Perrinet, Jens Kremkow, Frédéric Barthélemy, Guillaume S. Masson, Frédéric Chavane. Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework. In FENS, 2006 abstract
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- Alexandre Reynaud, Frédéric Barthélemy, Ivo Vanzetta, Guillaume S. Masson, Frédéric Chavane. Input-output transformation in the visuo-oculomotor loop: comparison of real-time optical imaging recording in V1 to the ocular following response to center-surround stimulation.. In FENS, 2006 abstract
With human oculomotor recordings, we have previously given evidence for an orientation selective suppressive effect of the surround on the contrast gain control of local stimuli (Barth\'elemy et al 2005). To determine the role of V1 horizontal connectivity in this phenomenon, we compared, in awake behaving monkeys, V1 population activity recorded with voltage-sensitive dye optical imaging with the oculomotor response. Local drifting gratings at various contrasts evoked very similar responses between both recordings. When a surround counter-phased grating is added, the response at the cortical representation of the central region showed an additional offset and a reduction of the amplitude modulation to the different contrasts. This additional offset is most probably due to subthreshold activation by horizontal propagation from the surround representation (Bringuier et al 1999). If we take into account the subthreshold response to surround-only stimulation, optical activity becomes quite analogous to oculomotor response: both recordings showed identical linearization of the contrast tuning curve and a contrast gain reduction in presence of a surround. However, the orientation of the surround stimulus does not affect V1 activity at the representation of the central stimulus although oculomotor response does (at least in human). Studying input and output of the visuo-oculomotor loop allowed us to extrapolate the role of extrastriate visual areas in oculomotor response. At this point, these results may suggest that horizontal connectivity of V1 influences contrast perception but orientation selective suppression occurs in upper level areas.
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- Alexandre Reynaud, Frédéric Barthélemy, Ivo Vanzetta, Guillaume S. Masson, Frédéric Chavane. Input-output transformation in the visuo-oculomotor loop: comparison of real-time optical imaging recording in V1 to the ocular following response to center-surround stimulation. Italian Archives of Biology, 145:251--62, 2007 abstract
With human oculomotor recordings, we have previously given evidence for an orientation selective suppressive effect of the surround on the contrast gain control of local stimuli (Barth\'elemy et al 2005). To determine the role of V1 horizontal connectivity in this phenomenon, we compared, in awake behaving monkeys, V1 population activity recorded with voltage-sensitive dye optical imaging with the oculomotor response. Local drifting gratings at various contrasts evoked very similar responses between both recordings. When a surround counter-phased grating is added, the response at the cortical representation of the central region showed an additional offset and a reduction of the amplitude modulation to the different contrasts. This additional offset is most probably due to subthreshold activation by horizontal propagation from the surround representation (Bringuier et al 1999). If we take into account the subthreshold response to surround-only stimulation, optical activity becomes quite analogous to oculomotor response: both recordings showed identical linearization of the contrast tuning curve and a contrast gain reduction in presence of a surround. However, the orientation of the surround stimulus does not affect V1 activity at the representation of the central stimulus although oculomotor response does (at least in human). Studying input and output of the visuo-oculomotor loop allowed us to extrapolate the role of extrastriate visual areas in oculomotor response. At this point, these results may suggest that horizontal connectivity of V1 influences contrast perception but orientation selective suppression occurs in upper level areas.
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- Yuzhi Chen, Wilson S. Geisler, Eyal Seidemann. Optimal temporal decoding of neural population responses in a reaction-time visual detection task.. Journal of Neurophysiology, 99(3):1366--79, 2008 abstract
Behavioral performance in detection and discrimination tasks is likely to be limited by the quality and nature of the signals carried by populations of neurons in early sensory cortical areas. Here we used voltage-sensitive dye imaging (VSDI) to directly measure neural population responses in the primary visual cortex (V1) of monkeys performing a reaction-time detection task. Focusing on the temporal properties of the population responses, we found that V1 responses are consistent with a stimulus-evoked response with amplitude and latency that depend on target contrast and a stimulus-independent additive noise with long-lasting temporal correlations. The noise had much lower amplitude than the ongoing activity reported previously in anesthetized animals. To understand the implications of these properties for subsequent processing stages that mediate behavior, we derived the Bayesian ideal observer that specifies how to optimally use neural responses in reaction time tasks. Using the ideal observer analysis, we show that 1) the observed temporal correlations limit the performance benefit that can be attained by accumulating V1 responses over time, 2) a simple temporal decorrelation operation with time-lagged excitation and inhibition minimizes the detrimental effect of these correlations, 3) the neural information relevant for target detection is concentrated in the initial response following stimulus onset, and 4) a decoder that optimally uses V1 responses far outperforms the monkey in both speed and accuracy. Finally, we demonstrate that for our particular detection task, temporal decorrelation followed by an appropriate running integrator can approach the speed and accuracy of the optimal decoder.,
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- Laurent Perrinet, Alexandre Reynaud, Frédéric Chavane, Guillaume S. Masson. Inferring monkey ocular following responses from V1 population dynamics using a probabilistic model of motion integration. In Vision Science Society, 2009 abstract
Short presentation of a large moving pattern elicits an ocular following response that exhibits many of the properties attributed to low-level motion processing such as spatial and temporal integration, contrast gain control and divisive interaction between competing motions. Similar mechanisms have been demonstrated in V1 cortical activity in response to center-surround gratings patterns measured with real-time optical imaging in awake monkeys (see poster of Reynaud et al., VSS09). Based on a previously developed Bayesian framework, we have developed an optimal statistical decoder of such an observed cortical population activity as recorded by optical imaging. This model aims at characterizing the statistical dependance between early neuronal activity and ocular responses and its performance was analyzed by comparing this neuronal read-out and the actual motor responses on a trial-by-trial basis. First, we show that relative performance of the behavioral contrast response function is similar to the best estimate obtained from the neural activity. In particular, we show that the latency of ocular response increases with low contrast conditions as well as with noisier instances of the behavioral task as decoded by the model. Then, we investigate the temporal dynamics of both neuronal and motor responses and show how motion information as represented by the model is integrated in space to improve population decoding over time. Lastly, we explore how a surrounding velocity non congruous with the central excitation information shunts the ocular response and how it is topographically represented in the cortical activity.,
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- Alexandre Reynaud, Frédéric Chavane, Guillaume S. Masson. Cortical origin of contrast response function contextual modulation in V1 population activity measured with voltage-sensitive dye imaging. In Vision Science Society, 2009, abstract
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- Laurent Perrinet, Nicole Voges, Jens Kremkow, Guillaume S. Masson. Decoding center-surround interactions in population of neurons for the ocular following response, URL . In Proceedings of COSYNE, 2009, 2009 abstract
Short presentation of a large moving pattern elicits an Ocular Following Response (OFR) that exhibits many of the properties attributed to low-level motion processing such as spatial and temporal integration, contrast gain control and divisive interaction between competing motions. Similar mechanisms have been demonstrated in V1 cortical activity in response to center-surround gratings patterns measured with real-time optical imaging in awake monkeys. More recent experiments of OFR have used disk gratings and bipartite stimuli which are optimized to study the dynamics of center-surround integration. We quantified two main characteristics of the global spatial integration of motion from an intermediate map of possible local translation velocities: (i) a finite optimal stimulus size for driving OFR, surrounded by an antagonistic modulation and (ii) a direction selective suppressive effect of the surround on the contrast gain control of the central stimuli [Barthelemy06,Barthelemy07].In fact, the machinery behind the visual perception of motion and the subsequent sensorimotor transformation is confronted to uncertainties which are efficiently resolved in the primate's visual system. We may understand this response as an ideal observer in a probabilistic framework by using Bayesian theory [Weiss02] and we extended in the dynamical domain the ideal observer model to simulate the spatial integration of the different local motion cues within a probabilistic representation. We proved that this model is successfully adapted to model the OFR for the different experiments [Perrinet07neurocomp], that is for different levels of noise with full field gratings, with disks of various sizes and also for the effect of a flickering surround. However, another \emphad hoc inhibitory mechanism has to be added in this model to account for suppressive effects of the surround.We explore here an hypothesis where this could be understood as the effect of a recurrent prediction of information in the velocity map. In fact, in previous models, the integration step assumes independence of the local information while natural scenes are very predictable: Due to the rigidity and inertia of physical objects in visual space, neighboring local spatiotemporal information is redundant and one may introduce this \empha priori knowledge of the statistics of the input in the ideal observer model. We implement this in a realistic model of a layer representing velocities in a map of cortical columns, where predictions are implemented by lateral interactions within the cortical area. First, raw velocities are estimated locally from images and are propagated to this area in a feed-forward manner. Using this velocity map, we progressively learn the dependance of local velocities in a second layer of the model. This algorithm is cyclic since the prediction is using the local velocities which are themselves using both the feed-forward input and the prediction: We control the convergence of this process by measuring results for different learning rate. Results show that this simple model is sufficient to disambiguate characteristic patterns such as the Barber-Pole illusion. Due to the recursive network which is modulating the velocity map, it also explains that the representation may exhibit some memory, such as when an object suddenly disappears or when presenting a dot followed by a line (line-motion illusion).Finally, we applied this model that was tuned over a set of natural scenes to gratings of increasing sizes. We observed first that the feed-forward response as tuned to neurophysiological data gave lower responses at higher eccentricities, and that this effect was greater for higher grating frequencies. Then, we observed that depending on the size of the disk and on its spatial frequency, the recurrent network of lateral interactions Lastly, we explore how a surrounding velocity non congruous with the central excitation information shunts the ocular response and how it is topographically represented in the cortical activity. ,
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