Bayesian-Modeling | Laurent Perrinet

From the retina to action: Dynamics of predictive processing in the visual system

Wed, 01 Jan 2020 00:00:00 +0000

Find the text at https://laurentperrinet.github.io/Perrinet20PredictiveProcessing/
The source code of the text is available at https://github.com/laurentperrinet/Perrinet20PredictiveProcessing This chapter is available as part of the book “The Philosophy and Science of Predictive Processing” : List of Contributors :
Preface: The Brain as a Prediction Machine, Anil Seth
Introduction, Dina Mendonça, Manuel Curado & Steven S. Gouveia
Part I: Predictive Processing: Philosophical Approaches

Predictive Processing and Representation: How Less Can Be More, Erik Myin and Thomas van Es
A Humean Challenge to Predictive Coding, Colin Klein
Are Markov Blankets Real and Does it Matter?, Richard Menary and Alexander J. Gillett
Predictive Processing and Metaphysical Views of the Self, Robert Clowes and Klaus Gärtner

Part II: Predictive Processing: Cognitive Science and Neuroscientific Approaches

From the Retina to Action: Dynamics of Predictive Processing in the Visual System, Laurent Perrinet
Predictive Processing and Consciousness: Prediction Fallacy and its Spatiotemporal Resolution, Steven S. Gouveia
The Many Faces of Attention: Why Precision Optimization is not Attention, Sina Fazelpour and Madeleine Ransom
Predictive Processing: Does it Compute?, Chris Thornton

Part III: Predictive Processing: Mental Health

The Predictive Brain, Conscious Experience and Brain-related Conditions, Lisa Feldman Barrett and Lorena Chanes
Disconnection and Diaschisis: Active Inference in Neuropsychology, Thomas Parr and Karl Friston
The Phenomenology and Predictive Processing of Time in Depression, Zachariah Neemeh and Shaun Gallagher
Why Use Predictive Processing to Explain Psychopathology? The Case of Anorexia Nervosa, Jakob Hohwy and Stephen Gadsby

Afterword, Manuel Curado

Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures

Wed, 21 Nov 2018 00:00:00 +0000

The flash-lag effect as a motion-based predictive shift

Thu, 26 Jan 2017 00:00:00 +0000

Press release

Visual illusions: their origin lies in prediction

Flash-Lag Effect. When a visual stimulus moves along a continuous trajectory, it may be seen ahead of its veridical position with respect to an unpredictable event such as a punctuate flash. This illusion tells us something important about the visual system: contrary to classical computers, neural activity travels at a relatively slow speed. It is largely accepted that the resulting delays cause this perceived spatial lag of the flash. Still, after several decades of debates, there is no consensus regarding the underlying mechanisms.

Researchers from the Timone Institute of Neurosciences bring a new theoretical hypothesis on a visual illusion discovered at the beginning of the 20th century. This illusion remained misunderstood while it poses fundamental questions about how our brains represent events in space and time. This study published on January 26, 2017 in the journal PLOS Computational Biology, shows that the solution lies in the predictive mechanisms intrinsic to the neural processing of information.

Visual illusions are still popular: in a quasi-magical way, they can make objects appear where they are not expected… They are also excellent opportunities to question the constraints of our perceptual system. Many illusions are based on motion, such as the flash-lag effect. Observe a luminous dot that moves along a rectilinear trajectory. If a second light dot is flashed very briefly just above the first, the moving point will always be perceived in front of the flash while they are vertically aligned.

Fig 2. Diagonal Markov chain. In the current study, the estimated state vector z = {x, y, u, v} is composed of the 2D position (x and y) and velocity (u and v) of a (moving) stimulus. (A) First, we extend a classical Markov chain using Nijhawan’s diagonal model in order to take into account the known neural delay τ: At time t, information is integrated until time t − τ, using a Markov chain and a model of state transitions p(zt|zt−δt) such that one can infer the state until the last accessible information p(zt−τ|I0:t−τ). This information can then be “pushed” forward in time by predicting its trajectory from t − τ to t. In particular p(zt|I0:t−τ) can be predicted by the same internal model by using the state transition at the time scale of the delay, that is, p(zt|zt−τ). This is virtually equivalent to a motion extrapolation model but without sensory measurements during the time window between t − τ and t. Note that both predictions in this model are based on the same model of state transitions. (B) One can write a second, equivalent “pull” mode for the diagonal model. Now, the current state is directly estimated based on a Markov chain on the sequence of delayed estimations. While being equivalent to the push-mode described above, such a direct computation allows to more easily combine information from areas with different delays. Such a model implements Nijhawan’s “diagonal model”, but now motion information is probabilistic and therefore, inferred motion may be modulated by the respective precisions of the sensory and internal representations. (C) Such a diagonal delay compensation can be demonstrated in a two-layered neural network including a source (input) and a target (predictive) layer [44]. The source layer receives the delayed sensory information and encodes both position and velocity topographically within the different retinotopic maps of each layer. For the sake of simplicity, we illustrate only one 2D map of the motions (x, v). The integration of coherent information can either be done in the source layer (push mode) or in the target layer (pull mode). Crucially, to implement a delay compensation in this motion-based prediction model, one may simply connect each source neuron to a predictive neuron corresponding to the corrected position of stimulus (x + v ⋅ τ, v) in the target layer. The precision of this anisotropic connectivity map can be tuned by the width of convergence from the source to the target populations. Using such a simple mapping, we have previously shown that the neuronal population activity can infer the current position along the trajectory despite the existence of neural delays.

Processing visual information takes time and even if these delays are remarkably short, they are not negligible and the nervous system must compensate them. For an object that moves predictably, the neural network can infer its most probable position taking into account this processing time. For the flash, however, this prediction can not be established because its appearance is unpredictable. Thus, while the two targets are aligned on the retina at the time of the flash, the position of the moving object is anticipated by the brain to compensate for the processing time: it is this differentiated treatment that causes the flash-lag effect. The researchers show that this hypothesis also makes it possible to explain the cases where this illusion does not work: for example if the flash appears at the end of the moving dot’s trajectory or if the target reverses its path in an unexpected way. In this work, the major innovation is to use the accuracy of information in the dynamics of the model. Thus, the corrected position of the moving target is calculated by combining the sensory flux with the internal representation of the trajectory, both of which exist in the form of probability distributions. To manipulate the trajectory is to change the precision and therefore the relative weight of these two information when they are optimally combined in order to know where an object is at the present time. The researchers propose to call parodiction (from the ancient Greek paron, the present) this new theory that joins Bayesian inference with taking into account neuronal delays.

Fig 5. Histogram of the estimated positions as a function of time for the dMBP model. Histograms of the inferred horizontal positions (blueish bottom panel) and horizontal velocity (reddish top panel), as a function of time frame, from the dMBP model. Darker levels correspond to higher probabilities, while a light color corresponds to an unlikely estimation. We highlight three successive epochs along the trajectory, corresponding to the flash initiated, standard (mid-point) and flash terminated cycles. The timing of the flashes are respectively indicated by the dashed vertical lines. In dark, the physical time and in green the delayed input knowing τ = 100 ms. Histograms are plotted at two different levels of our model in the push mode. The left-hand column illustrates the source layer that corresponds to the integration of delayed sensory information, including the prior on motion. The right-hand illustrates the target layer corresponding to the same information but after the occurrence of some motion extrapolation compensating for the known neural delay τ.

Despite the simplicity of this solution, parodiction has elements that may seem counter-intuitive. Indeed, in this model, the physical world is considered “hidden”, that is to say, it can only be guessed by our sensations and our experience. The role of visual perception is then to deliver to our central nervous system the most likely information despite the different sources of noise, ambiguity and time delays. According to the authors of this publication, the visual treatment would consist in a “simulation” of the visual world projected at the present time, even before the visual information can actually modulate, confirm or cancel this simulation. This hypothesis, which seems to belong to “science fiction”, is being tested with more detailed and biologically plausible hierarchical neural network models that should allow us to better understand the mysteries underlying our perception. Visual illusions have still the power to amaze us!

check_out further results on introducing anisotropies in the FLE

Estimating and anticipating a dynamic probabilistic bias in visual motion direction

Sun, 01 Jan 2017 00:00:00 +0000

see a write-up in “Humans adapt their anticipatory eye movements to the volatility of visual motion properties”

Anticipating a moving target: role of vision and reinforcement

Thu, 01 Jan 2015 00:00:00 +0000

Eye tracking a self-moved target with complex hand-target dynamics

Thu, 01 Jan 2015 00:00:00 +0000

Active inference, eye movements and oculomotor delays

Tue, 16 Dec 2014 00:00:00 +0000

Active Inference, tracking eye movements and oculomotor delays

Tracking eye movements face a difficult task: they have to be fast while they suffer inevitable delays. If we focus on area MT of humans for instance as it is crucial for detecting the motion of visual objects, sensory information coming to this area is already lagging some 35 milliseconds behind operational time – that is, it reflects some past information. Still the fastest action that may be done there is only able to reach the effector muscles of the eyes some 40 milliseconds later – that is, in the future. The tracking eye movement system is however able to respond swiftly and even to anticipate repetitive movements (e.g. Barnes et al, 2000 – refs in manuscript). In that case, it means that information in a cortical area is both predicted from the past sensory information but also anticipated to give an optimal response in the future. Even if numerous models have been described to model different mechanisms to account for delays, no theoretical approach has tackled the whole problem explicitly. In several areas of vision research, authors have proposed models at different levels of abstractions from biomechanical models, to neurobiological implementations (e.g. Robinson, 1986) or Bayesian models. This study is both novel and important because – using a neurobiologically plausible hierarchical Bayesian model – it demonstrates that using generalized coordinates to finesse the prediction of a target’s motion, the model can reproduce characteristic properties of tracking eye movements in the presence of delays. Crucially, the different refinements to the model that we propose – pursuit initiation, smooth pursuit eye movements, and anticipatory response – are consistent with the different types of tracking eye movements that may be observed experimentally.

(A) This figure reports the response of predictive processing during the simulation of pursuit initiation, using a single sweep of a visual target, while compensating for sensory motor delays. Here, we see horizontal excursions of oculomotor angle (red line). One can see clearly the initial displacement of the target that is suppressed by action after a few hundred milliseconds. Additionally, we illustrate the effects of assuming wrong sensorimotor delays on pursuit initiation. Under pure sensory delays (blue dotted line), one can see clearly the delay in sensory predictions, in relation to the true inputs. With pure motor delays (blue dashed line) and with combined sensorimotor delays (blue line) there is a failure of optimal control with oscillatory fluctuations in oculomotor trajectories, which may become unstable. (B) This figure reports the simulation of smooth pursuit when the target motion is hemi-sinusoidal, as would happen for a pendulum that would be stopped at each half cycle left of the vertical (broken black lines in the lower-right panel). We report the horizontal excursions of oculomotor angle. The generative model used here has been equipped with a second hierarchical level that contains hidden states, modeling latent periodic behavior of the (hidden) causes of target motion. With this addition, the improvement in pursuit accuracy apparent at the onset of the second cycle of motion is observed (pink shaded area), similar to psychophysical experimentss.

Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network

Sun, 06 Jul 2014 00:00:00 +0000

Based on
Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation.
PDF Cite Pdf arXiv Doi
see follow-up on motion extrapolation:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2013). Motion-based prediction explains the role of tracking in motion extrapolation. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up on the flash-lag effect:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2017). The flash-lag effect as a motion-based predictive shift. PLoS Computational Biology.
Cite Slides DOI Press Code URL HAL

Figure 4: Rasterplot of input and output spikes. The raster plot from excitatory neurons is ordered according to their position. Each input spike is a blue dot and each output spike is a black dot. While input is scattered during blanking periods (Figure 1), the network output shows shows some tuned activity during the blank (compare with the activity before visual stimulation). To decode such patterns of activity we used a maximum-likelihood estimation technique based on the tuning curve of the neurons.

Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network

Fri, 25 Apr 2014 00:00:00 +0000

see Kaplan and al, 2014

Axonal delays and on-time control of eye movements

Fri, 10 Jan 2014 00:00:00 +0000

Anisotropic connectivity implements motion-based prediction in a spiking neural network

Tue, 17 Sep 2013 00:00:00 +0000

Based on
Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation.
PDF Cite Pdf arXiv Doi
see follow-up on motion extrapolation:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2013). Motion-based prediction explains the role of tracking in motion extrapolation. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up on the flash-lag effect:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2017). The flash-lag effect as a motion-based predictive shift. PLoS Computational Biology.
Cite Slides DOI Press Code URL HAL

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Motion-based prediction and development of the response to an 'on the way' stimulus

Tue, 01 Jan 2013 00:00:00 +0000

Based on
Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation.
PDF Cite Pdf arXiv Doi
see follow-up on motion extrapolation:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2013). Motion-based prediction explains the role of tracking in motion extrapolation. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up on the flash-lag effect:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2017). The flash-lag effect as a motion-based predictive shift. PLoS Computational Biology.
Cite Slides DOI Press Code URL HAL

Smooth Pursuit and Visual Occlusion: Active Inference and Oculomotor Control in Schizophrenia

Fri, 26 Oct 2012 00:00:00 +0000

Grabbing, tracking and sniffing as models for motion detection and eye movements

Fri, 27 Jan 2012 00:00:00 +0000

Motion-based prediction is sufficient to solve the aperture problem

Thu, 12 Jan 2012 17:00:00 +0000

See a followup in Perrinet et al, 2012

Motion-based prediction is sufficient to solve the aperture problem

Sun, 01 Jan 2012 00:00:00 +0000

See a followup in Perrinet et al, 2012

Motion-based prediction is sufficient to solve the aperture problem

Sun, 01 Jan 2012 00:00:00 +0000

The estimation of the motion of an elongated, slanted segment (here moving horizontally to the right) on a limited area (such as the receptive field of a neuron) leads to ambiguous velocity measurements compared to physical motion: it’s the aperture problem. We represent as arrows the velocity vectors that are most likely detected by a motion energy model; hue indicates direction angle. Introducing predictive coding resolves the aperture problem.

Figure 1: (A) The estimation of the motion of an elongated, slanted segment (here moving horizontally to the right) on a limited area (such as the dotted circle) leads to ambiguous velocity measurements compared to physical motion: it’s the aperture problem. We represent as arrows the velocity vectors that are most likely detected by a motion energy model; hue indicates direction angle. Due to the limited size of receptive fields in sensory cortical areas (such as shown by the dotted white circle), such problem is faced by local populations of neurons that visually estimate the motion of objects. (A-inset) On a polar representation of possible velocity vectors (the cross in the center corresponds to the null velocity, the outer circle corresponding to twice the amplitude of physical speed), we plot the empirical histogram of detected velocity vectors. This representation gives a quantification of the aperture problem in the velocity domain: At the onset of motion detection, information is concentrated along an elongated constraint line (white=high probability, black=zero probability). (B) We use the prior knowledge that in natural scenes, motion as defined by its position and velocity is following smooth trajectories. Quantitatively, it means that velocity is approximately conserved and that position is transported according to the known velocity. We show here such a transition on position and velocity (respectively $x_t$ and $V_t$) from time t to t + dt with the perturbation modeling the smoothness of prediction in position and velocity (respectively $N_x$ and $N_V$). (C) Applying such a prior on a dynamical system detecting motion, we show that motion converges to the physical motion after approximately one spatial period (the line moved by twice its height). (C-Inset) The read-out of the system converged to the physical motion: Motion-based prediction is sufficient to resolve the aperture problem. (D) As observed at the perceptual level [Castet et al., 1993, Pei et al., 2010], size and duration of the tracking angle bias decreased with respect to the height of the line. Height was measured relative to a spatial period (respectively 60%, 40% and 20%). Here we show the average tracking angle red-out from the probabilistic representation as a function of time, averaged over 20 trials (error bars show one standard deviation).

Figure 2: Architecture of the model. The model is constituted by a classical measurement stage and of a predictive coding layer. The measurement stage consists of (A) inferring from two consecutive frames of the input flow, (B) a likelihood distribution of motion. This layer interacts with the predictive layer which consists of (C) a prediction stage that infers from the current estimate and the transition prior the upcoming state estimate and (D) an estimation stage that merges the current prediction of motion with the likelihood measured at the same instant in the previous layer (B).

Figure 3: To explore the state-space of the dynamical system, we simulated motion-based prediction for a simple small dot (size 2.5% of a spatial period) moving horizontally from the left to the right of the screen. We tested different levels of sensory noise with respect to different levels of internal noise, that is, to different values of the strength of prediction. (Right) Results show the emergence of different states for different prediction precisions: a regime when prediction is weak and which shows high tracking error and variability (No Tracking - NT), a phase for intermediate values of prediction strength (as in Figure 1) exhibiting a low tracking error and low variability in the tracking phase (True Tracking - TT) and finally a phase corresponding to higher precisions with relatively efficient mean detection but high variability (False Tracking - FT). We give 3 representative examples of the emerging states at one contrast level (C = 0.1) with starting (red) and ending (blue) points and respectively NT, TT and FT by showing inferred trajectories for each trial. (Left) We define tracking error as the ratio between detected speed and target speed and we plot it with respect to the stimulus contrast as given by the inverse of sensory noise. Error bars give the variability in tracking error as averaged over 20 trials. As prediction strength increases, there is a transition from smooth contrast response function (NT) to more binary responses (TT and FT).

Figure 4: (Top) Prediction implements a competition between different trajectories. Here, we focus on one step of the algorithm by testing different trajectories at three key positions of the segment stimulus: the two edges and the center (dashed circles). Compared to the pure sensory velocity likelihood (left insets in grayscale), prediction modulates response as shown by the velocity vectors (direction coded as hue as in Figure 1) and by the ratio of velocity probabilities (log ratio in bits, right insets). There is no change for the middle of the segment (yellow tone), but trajectories that are predicted out of the line are “explained away” (navy tone) while others may be amplified (orange tone). Notice the asymmetry between both edges, the upper edge carrying a suppressive predictive information while the bottom edge diffuses coherent motion. (Bottom) Finally, the aperture problem is solved due to the repeated application of this spatio-temporal contextual information modulation. To highlight the anisotropic diffusion of information over the rest of the line, we plot as a function of time (horizontal axis) the histogram of the detected motion marginalized over horizontal positions (vertical axis), while detected direction of velocity is given by the distribution of hues. Blueish colors correspond to the direction perpendicular to the diagonal while a green color represents a disambiguated motion to the right (as in Figure 1). The plot shows that motion is disambiguated by progressively explaining away incoherent motion. Note the asymmetry in the propagation of coherent information.

Perceptions as Hypotheses: Saccades as Experiments

Sun, 01 Jan 2012 00:00:00 +0000

This schematic shows the dependencies among various quantities that are assumed when modeling the exchanges of a self organizing system like the brain with the environment. The top panel describes the states of the environment and the system or agent in terms of a probabilistic dependency graph, where connections denote directed dependencies. The quantities are described within the nodes of this graph with exemplar forms for their dependencies on other variables (see main text). Here, hidden and internal states are separated by action and sensory states. Both action and internal states encoding a conditional density minimize free energy, while internal states encoding prior beliefs maximize salience. Both free energy and salience are defined in terms of a generative model that is shown as fictive dependency graph in the lower panel. Note that the variables in the real world and the form of their dynamics are different from that assumed by the generative model; this is why external states are in bold. Furthermore, note that action is a state in the model of the brain but is replaced by hidden controls in the brain’s model of its world. This means that the agent is not aware of action but has beliefs about hidden causes in the world that action can fulfill through minimizing free energy. These beliefs correspond to prior expectations that sensory states will be sampled in a way that optimizes conditional confidence or salience.

Role of motion-based prediction in motion extrapolation

Sun, 01 Jan 2012 00:00:00 +0000

Based on
Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation.
PDF Cite Pdf arXiv Doi
see follow-up on motion extrapolation:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2013). Motion-based prediction explains the role of tracking in motion extrapolation. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up on the flash-lag effect:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2017). The flash-lag effect as a motion-based predictive shift. PLoS Computational Biology.
Cite Slides DOI Press Code URL HAL

Propriétés émergentes d'un modèle de prédiction probabiliste utilisant un champ neural

Sat, 02 Jul 2011 00:00:00 +0000

La finalité de cette manifestation est de permettre à nos chercheurs de se réunir en groupes de travail et en ateliers afin de découvrir la thématique des neurosciences et son interdisciplinarité. La manifestation se tient dans le cadre des activités du laboratoire LAMS, de ABC MATHINFO, du GDRI NeurO et du réseau méditerranéen NeuroMed.

related publication @ SPIE 2008
see this more recent talk @ UCL, London

Pursuing motion illusions: a realistic oculomotor framework for Bayesian inference

Fri, 22 Apr 2011 00:00:00 +0000

See a followup in
Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation.
PDF Cite Pdf arXiv Doi

Role of motion inertia in dynamic motion integration for smooth pursuit

Sat, 01 Jan 2011 00:00:00 +0000

Based on
Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation.
PDF Cite Pdf arXiv Doi
see follow-up on motion extrapolation:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2013). Motion-based prediction explains the role of tracking in motion extrapolation. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up on the flash-lag effect:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2017). The flash-lag effect as a motion-based predictive shift. PLoS Computational Biology.
Cite Slides DOI Press Code URL HAL

Probabilistic models of the low-level visual system: the role of prediction in detecting motion

Fri, 17 Dec 2010 00:00:00 +0000

An event ranging “From Mathematical Image Analysis to Neurogeometry of the Brain” Ladislav Tauc & GDR MSPC neurosciences conference.

related publication from Mina Khoei @ TAUC 2012
see this more recent talk @ UCL, London

Models of low-level vision: linking probabilistic models and neural masses

Fri, 08 Jan 2010 00:00:00 +0000

see this more recent talk @ UCL, London

A recurrent Bayesian model of dynamic motion integration for smooth pursuit

Fri, 01 Jan 2010 00:00:00 +0000

Dynamical emergence of a neural solution for motion integration

Fri, 01 Jan 2010 00:00:00 +0000

Based on
Laurent U Perrinet, Guillaume S Masson (2012). Motion-based prediction is sufficient to solve the aperture problem. Neural Computation.
PDF Cite Pdf arXiv Doi
see follow-up on motion extrapolation:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2013). Motion-based prediction explains the role of tracking in motion extrapolation. Journal of Physiology-Paris.
PDF Cite DOI URL
see follow-up on the flash-lag effect:
Mina A Khoei, Guillaume S Masson, Laurent U Perrinet (2017). The flash-lag effect as a motion-based predictive shift. PLoS Computational Biology.
Cite Slides DOI Press Code URL HAL

Dynamical emergence of a neural solution for motion integration

Fri, 01 Jan 2010 00:00:00 +0000

Probabilistic models of the low-level visual system: the role of prediction in detecting motion

Fri, 01 Jan 2010 00:00:00 +0000

See a followup in Perrinet et al, 2012

Decoding center-surround interactions in population of neurons for the ocular following response

Thu, 01 Jan 2009 00:00:00 +0000

Dynamics of distributed 1D and 2D motion representations for short-latency ocular following

Fri, 01 Feb 2008 00:00:00 +0000

Decoding the population dynamics underlying ocular following response using a probabilistic framework

Tue, 01 Jan 2008 00:00:00 +0000

Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework

Tue, 01 Jan 2008 00:00:00 +0000

What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells

Tue, 01 Jan 2008 00:00:00 +0000

Dynamical Neural Networks: modeling low-level vision at short latencies

Thu, 01 Mar 2007 00:00:00 +0000

Dynamical Neural Networks (DyNNs) are a class of models for networks of neurons where particular focus is put on the role of time in the emergence of functional computational properties. The definition and study of these models involves the cooperation of a large range of scientific fields from statistical physics, probabilistic modelling, neuroscience and psychology to control theory. It focuses on the mechanisms that may be relevant for studying cognition by hypothesizing that information is distributed in the activity of the neurons in the system and that the timing helps in maintaining this information to lastly form decisions or actions. The system responds at best to the constraints of the outside world and learning strategies tune this internal dynamics to achieve optimal performance. This chapter introduces the book. See also:

Bruno Cessac, Emmanuel Daucé, Laurent U Perrinet, Manuel Samuelides (2007). Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision. Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision.
Cite
Bruno Cessac, Emmanuel Daucé, Laurent U Perrinet, Manuel Samuelides (2007). Introduction to Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision. Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision.
Cite DOI URL

Bayesian modeling of dynamic motion integration

Mon, 01 Jan 2007 00:00:00 +0000

Modeling spatial integration in the ocular following response using a probabilistic framework

Mon, 01 Jan 2007 00:00:00 +0000

Visual tracking of ambiguous moving objects: A recursive Bayesian model

Mon, 01 Jan 2007 00:00:00 +0000

Bayesian modeling of dynamic motion integration

Sun, 01 Jan 2006 00:00:00 +0000

See a followup in Perrinet et al, 2012

Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework

Sun, 01 Jan 2006 00:00:00 +0000

See a followup in Perrinet et al, 2012

Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework

Sun, 01 Jan 2006 00:00:00 +0000

related publication @ SPIE 2008
See a followup in Perrinet et al, 2012

Dynamics of motion representation in short-latency ocular following: A two-pathways Bayesian model

Sat, 01 Jan 2005 00:00:00 +0000

Feature detection using spikes : the greedy approach

Thu, 01 Jul 2004 00:00:00 +0000