Spatiotemporal pattern discrimination using predictive dynamic neural fields

Quinton, Jean‐Charles; Girau, Bernard

doi:10.1186/1471-2202-13-s1-o16

Cited by 3 publications

(5 citation statements)

References 2 publications

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“…In order to progressively introduce the components of the ANF model, this section starts from the dynamic neural fields initially proposed by Wilson and Cowan (1973), developed by Amari (1977), and later applied to visual attention directed to moving targets (Rougier & Vitay, 2006). We then introduce the PNF model with a single projection that can bias the dynamics of the neural fields (Quinton & Girau, 2011), then with many projections (Quinton & Girau, 2012), before including eye movements. The increase in model complexity goes hand in hand with extended attentional capabilities, from the detection of fixed targets in the visual field to the generation of tracking eye movements toward a moving target.…”

Section: Computational Modelmentioning

confidence: 99%

“…but simply adopt a non-expected trajectory. It is therefore possible to discriminate and interpolate between trajectories in a nonlinear fashion, thanks to the feedback loop of projections validity (w k ) in the PNF equation (Quinton & Girau, 2012). Finally and as a consequence, the system becomes also robust to the temporary disappearance of the target (e.g.…”

Section: Predictive Neural Field (Pnf)mentioning

confidence: 99%

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A unified dynamic neural field model of goal directed eye movements

Quinton

Goffart

2018

Connection Science

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Primates heavily rely on their visual system, which exploits signals of graded precision based on the eccentricity of the target in the visual field. The interactions with the environment involve actively selecting and focusing on visual targets or regions of interest, instead of contemplating an omnidirectional visual flow. Eye-movements specifically allow foveating targets and track their motion. Once a target is brought within the central visual field, eye-movements are usually classified into catch-up saccades (jumping from one orientation or fixation to another) and smooth pursuit (continuously tracking a target with low velocity). Building on existing dynamic neural field equations, we introduce a novel model that incorporates internal projections to better estimate the current target location (associated to a peak of activity). Such estimate is then used to trigger an eye movement, leading to qualitatively different behaviors depending on the dynamics of the whole oculomotor system: 1) fixational eye-movements due to small variations in the weights of projections when the target is stationary, 2) interceptive and catch-up saccades when peaks build and relax on the neural field, 3) smooth pursuit when the peak stabilizes near the center of the field, the system reaching a fixed point attractor. Learning is nevertheless required for tracking a rapidly moving target, and the proposed model thus replicates recent results in the monkey, in which repeated exercise permits the maintenance of the target within in the central visual field at its current (here-and-now) location, despite the delays involved in transmitting retinal signals to the oculomotor neurons.

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Section: Computational Modelmentioning

confidence: 99%

Section: Predictive Neural Field (Pnf)mentioning

confidence: 99%

A unified dynamic neural field model of goal directed eye movements

Quinton

Goffart

2018

Connection Science

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“…This approach uses population coding and pooling, where a discrete set of orientations and scales are sufficient to encode for continuous changes in the sensory flow. Distributed systems can then rely on their dynamics to interpolate between different scales, so that resulting detectors and descriptors reflect continuous variations (Quinton and Girau, 2012).…”

Section: Motor Outputs and Sensory Inputs To The Architecturementioning

confidence: 99%

How active perception and attractor dynamics shape perceptual categorization: A computational model

2014

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We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent-environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as "evidence" for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.

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“…The origin of mean-field modelling lies in the nineteen seventies, when pioneers like Walter Freeman [4], Wilson & Cowan [5] and Lopes da Silva [6] started to model components of the human cortex with continuous fields. Over the past four decades, mean-field models have been used to study a range of open questions in neuroscience, such as the generation of the alpha rhythm, [8][9][10][11][12][13] Hz oscillations in the EEG (see, e.g., [6,7]), epilepsy (see, e.g., [8,9,10]) and anaesthesia [11]. Also, they are used in models for sensorimotor control, pattern discrimination and target tracking [12].…”

Section: Introductionmentioning

confidence: 99%

“…Over the past four decades, mean-field models have been used to study a range of open questions in neuroscience, such as the generation of the alpha rhythm, 8-13 Hz oscillations in the EEG (see, e.g., [6,7]), epilepsy (see, e.g., [8,9,10]) and anaesthesia [11]. Also, they are used in models for sensorimotor control, pattern discrimination and target tracking [12].…”

Section: Introductionmentioning

confidence: 99%

Open-source tools for dynamical analysis of Liley's mean-field cortex model

Green

2014

Journal of Computational Science

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Mean-field models of the mammalian cortex treat this part of the brain as a two-dimensional excitable medium. The electrical potentials, generated by the excitatory and inhibitory neuron populations, are described by nonlinear, coupled, partial differential equations, that are known to generate complicated spatio-temporal behaviour. We focus on the model by Liley et al. (Network: Comput. Neural Syst. (2002) 13, 67-113). Several reductions of this model have been studied in detail, but a direct analysis of its spatiotemporal dynamics has, to the best of our knowledge, never been attempted before. Here, we describe the implementation of implicit time-stepping of the model and the tangent linear model, and solving for equilibria and timeperiodic solutions, using the open-source library PETSc. By using domain decomposition for parallelization, and iterative solving of linear problems, the code is capable of parsing some dynamics of a macroscopic slice of cortical tissue with a sub-millimetre resolution.

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Spatiotemporal pattern discrimination using predictive dynamic neural fields

Cited by 3 publications

References 2 publications

A unified dynamic neural field model of goal directed eye movements

A unified dynamic neural field model of goal directed eye movements

How active perception and attractor dynamics shape perceptual categorization: A computational model

Open-source tools for dynamical analysis of Liley's mean-field cortex model

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