BrainScaleS (2011/2014)

BrainScaleS (2011/2014)

List of publications that were funded by European Union’s project Number FP7-269921, “BrainScales”.

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Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.

Posts

We held a CodeJam 22nd-24th June 2010, in Marseille.

Publications

Talks

We stand at a point in history where our phones have become smart but lack a feature which prevails in most forms of living intelligence: vision. The ability to see is indeed an essential facet of intelligence which is developed in an autonomous manner even in young human infants. I will focus here on a particular problem: how do we estimate motion in a visual image? I will explain why for this problem, it is crucial to understand how the visual system might overcome temporal delays and will demonstrate at different levels of description —from probabilistic models to neuromorphic hardware— a surprising solution: The visual system models the world and uses the eye to probe this model.

The question how the visual system is able to create a coherent representation of a rapidly changing environment in the presence of neural delays is not fully resolved. In this paper we use an abstract probabilistic framework and a spiking neural network (SNN) implementation to investigate the role of motion-based prediction in estimating motion trajectories with delayed information sampling. In particular, we investigate how anisotropic diffusion of information may explain the development of anticipatory response in neural populations to an approaching stimulus. Inspired by a mechanism proposed by Nijhawan [2009], we use a Bayesian particle filter framework and introduce a diagonal motion-based prediction model which extrapolates the estimated response to delayed stimulus in the direction of the trajectory. In the SNN implementation, we have used anisotropic recurrent connections between excitatory cells as mechanism for motion-extrapolation. Consistent with recent experimental data collected in extracellular recordings of macaque primary visual cortex [Benvenuti et al 2011], we have simulated different trajectory lengths and have explored how anticipatory response may be dependent to the information accumulated along the trajectory. We show that both our probabilistic framework and the SNN can replicate the experimental data qualitatively. Most importantly, we highlight requirements for the development of a trajectory-dependent anticipatory response, and in particular the anisotropic nature of the diagonal motion extrapolation mechanism.

Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed as fast as possible. One unsolved question is to understand how the brain pools motion information to give an efficient response. I will present computational models of motion detection in the visual system that try to answer this question. First, sensory information is grabbed by pooling the information from different sensory neurons. This pooling is modulated by the precision of information and we will present some recent model-based behavioural results. Then I will focus on a novel model of motion-based prediction that allows to track objects on smooth trajectories. This model gives an economical description of neural mechanisms associated with the processing underlying motion detection. Finally, we will propose an exploratory hypothesis such that eye movements may be understood as the prospective response to this dynamical sensory response knowing oculomotor constraints such as delays. This line of research aims at showing that through the convergent use of models, electrophysiology or behavioural responses, the study of motion detection is an essential tool in our understanding of neural computations.

This session aims at presenting new ideas that emerged during the first years of BrainScaleS. Indeed, the collaborations that were initiated within the consortium led to the creation of novel tools as planned in the proposal but also some of which were unforeseen, like the Motion Clouds that we presented previously. We present here some prototypical and inspiring examples of such collaborative work on: 1) tool chains from experimental (Davison), computational (Antolik) or integrative (Petrovici) perspectives, 2) original methods inspired by novel types of analysis for propagating waves (Schmidt, Muller) or by novel magnetrodes (Pannetier Lecoeur).

Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed as fast as possible. One unsolved question is to understand how the brain pools motion information to give an efficient response. I will present computational models of motion detection in the visual system that try to answer this question. First, sensory information is grabbed by pooling the information from different sensory neurons. This pooling is modulated by the precision of information and we will present some recent model-based behavioural results. Then I will focus on a novel model of motion-based prediction that allows to track objects on smooth trajectories. This model gives an economical description of neural mechanisms associated with the processing underlying motion detection. Finally, we will propose an exploratory hypothesis such that eye movements may be understood as the prospective response to this dynamical sensory response knowing oculomotor constraints such as delays. This line of research aims at showing that through the convergent use of models, electrophysiology or behavioural responses, the study of motion detection is an essential tool in our understanding of neural computations.

Oriented edges in images of natural scenes tend to be aligned in collinear or co-circular arrangements, with lines and smooth curves more common than other possible arrangements of edges (Geisler et al., Vis Res 41:711-24, 2001). The visual system appears to take advantage of this prior information, and human contour detection and grouping performance is well predicted by such an \“association field\” (Field et al., Vis Res 33:173-93, 1993). One possible candidate substrate for implementing an association field in mammals is the set of long-range lateral connections between neurons in the primary visual cortex (V1), which could act to facilitate detection of contours matching the association field, and/or inhibit detection of other contours (Choe and Miikkulainen, Biol Cyb 90:75-88, 2004). To fill this role, the lateral connections would need to be orientation specific and aligned along contours, and indeed such an arrangement has been found in tree shrew primary visual cortex (Bosking et al., J Neurosci 17:2112-27, 1997). However, it is not yet known whether these patterns develop as a result of visual experience, or are simply hard-wired to be appropriate for the statistics of natural scenes. To investigate this issue, we examined the properties of the visual environment of laboratory animals, to determine whether the observed connection patterns are more similar to the statistics of the rearing environment or of a natural habitat. Specifically, we analyzed the cooccurence statistics of edge elements in images of natural scenes, and compared them to corresponding statistics for images taken from within the rearing environment of the animals in the Bosking et al. (1997) study. We used a modified version of the algorithm from Geisler et al. (2001), with a more general edge extraction algorithm that uses sparse coding to avoid multiple responses to a single edge. Collinearity and co-circularity results for natural images replicated qualitatively the results from Geisler et al. (2001), confirming that prior information about continuations appeared consistently in natural images. However, we find that the largely man-made environment in which these animals were reared has a significantly higher probability of collinear edge elements. We thus predict that if the lateral connection patterns are due to visual experience, the patterns in wild-raised tree shrews would be very different from those measured by Bosking et al. (1997), with shorter-range correlations and less emphasis on collinear continuations. This prediction can be tested in future experiments on matching groups of animals reared in different environments.

In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent global percept. This is well demonstrated for the detection of motion in the aperture problem: as luminance of an elongated line is symmetrical along its axis, tangential velocity is ambiguous when measured locally. Here, we develop the hypothesis that motion-based predictive coding is sufficient to infer global motion. Our implementation is based on a context-dependent diffusion of a probabilistic representation of motion. We observe in simulations a progressive solution to the aperture problem similar to psychophysics and behavior. We demonstrate that this solution is the result of two underlying mechanisms. First, we demonstrate the formation of a tracking behavior favoring temporally coherent features independently of their texture. Second, we observe that incoherent features are explained away while coherent information diffuses progressively to the global scale. Most previous models included ad-hoc mechanisms such as end-stopped cells or a selection layer to track specific luminance-based features. Here, we have proved that motion-based predictive coding, as it is implemented in this functional model, is sufficient to solve the aperture problem. This simpler solution may give insights in the role of prediction underlying a large class of sensory computations.

Oriented edges in images of natural scenes tend to be aligned in collinear or co-circular arrangements, with lines and smooth curves more common than other possible arrangements of edges (Geisler et al., Vis Res 41:711-24, 2001). The visual system appears to take advantage of this prior information, and human contour detection and grouping performance is well predicted by such an \“association field\” (Field et al., Vis Res 33:173-93, 1993). One possible candidate substrate for implementing an association field in mammals is the set of long-range lateral connections between neurons in the primary visual cortex (V1), which could act to facilitate detection of contours matching the association field, and/or inhibit detection of other contours (Choe and Miikkulainen, Biol Cyb 90:75-88, 2004). To fill this role, the lateral connections would need to be orientation specific and aligned along contours, and indeed such an arrangement has been found in tree shrew primary visual cortex (Bosking et al., J Neurosci 17:2112-27, 1997). However, it is not yet known whether these patterns develop as a result of visual experience, or are simply hard-wired to be appropriate for the statistics of natural scenes. To investigate this issue, we examined the properties of the visual environment of laboratory animals, to determine whether the observed connection patterns are more similar to the statistics of the rearing environment or of a natural habitat. Specifically, we analyzed the cooccurence statistics of edge elements in images of natural scenes, and compared them to corresponding statistics for images taken from within the rearing environment of the animals in the Bosking et al. (1997) study. We used a modified version of the algorithm from Geisler et al. (2001), with a more general edge extraction algorithm that uses sparse coding to avoid multiple responses to a single edge. Collinearity and co-circularity results for natural images replicated qualitatively the results from Geisler et al. (2001), confirming that prior information about continuations appeared consistently in natural images. However, we find that the largely man-made environment in which these animals were reared has a significantly higher probability of collinear edge elements. We thus predict that if the lateral connection patterns are due to visual experience, the patterns in wild-raised tree shrews would be very different from those measured by Bosking et al. (1997), with shorter-range correlations and less emphasis on collinear continuations. This prediction can be tested in future experiments on matching groups of animals reared in different environments.