Optimal Sensorimotor Integration in Recurrent Cortical Networks: A Neural Implementation of Kalman Filters

Denève, Sophie; Duhamel, Jean‐René; Pouget, Alexandre

doi:10.1523/jneurosci.3985-06.2007

Cited by 117 publications

(83 citation statements)

References 73 publications

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“…For the first time, our model combines continuous flow of sensory and predictive information to drive motor behavior, whereas a previous model used Kalman filtering for online prediction only (Shibata et al, 2005). A possible implementation of Kalman filters in the brain has been proposed previously (Denève et al, 2007), but a full model based on Kalman filters that integrates aspects of predictive and reactive motor control had never been developed. Our particular, simple model based on Kalman filters could account for all the main dynamics of predictive and reactive smooth pursuit eye movements without switching between submodels and with the same model parameters.…”

Section: Kalman Filtering To Estimate and Combine Retinal And Extrarementioning

confidence: 99%

Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics

et al. 2013

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The brain makes use of noisy sensory inputs to produce eye, head, or arm motion. In most instances, the brain combines this sensory information with predictions about future events. Here, we propose that Kalman filtering can account for the dynamics of both visually guided and predictive motor behaviors within one simple unifying mechanism. Our model relies on two Kalman filters: (1) one processing visual information about retinal input; and (2) one maintaining a dynamic internal memory of target motion. The outputs of both Kalman filters are then combined in a statistically optimal manner, i.e., weighted with respect to their reliability. The model was tested on data from several smooth pursuit experiments and reproduced all major characteristics of visually guided and predictive smooth pursuit. This contrasts with the common belief that anticipatory pursuit, pursuit maintenance during target blanking, and zero-lag pursuit of sinusoidally moving targets all result from different control systems. This is the first instance of a model integrating all aspects of pursuit dynamics within one coherent and simple model and without switching between different parallel mechanisms. Our model suggests that the brain circuitry generating a pursuit command might be simpler than previously believed and only implement the functional equivalents of two Kalman filters whose outputs are optimally combined. It provides a general framework of how the brain can combine continuous sensory information with a dynamic internal memory and transform it into motor commands.

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Section: Kalman Filtering To Estimate and Combine Retinal And Extrarementioning

confidence: 99%

Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics

et al. 2013

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“…Pathologies that impair the central nervous system (CNS) functions lead to pathophysiological states that can be seen as models for our understanding of how the healthy CNS works. Scientists have also been strenuously trying to figure out different patterns of pathological behavior by means of models 24 . At the same time, CNS diseases being a synonym of pain and suffering for patients and family members, the use of models that select specific disease peculiarities may aid us in the prevention, better management during the course of the disease, and occasionally its cure.…”

Section: Alzheimer Disease As a Model To Understand Memorymentioning

confidence: 99%

Alzheimer's disease and implicit memory

Machado

Cunha

Minc

et al. 2009

Arq. Neuro-Psiquiatr.

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-Specific neuropsychiatric disorders, such as Alzheimer's disease (AD) affect some forms of memory while leaving others relatively intact. In this review, we investigate particularities of the relationship between explicit and implicit memories in AD. It was found that implicit memory is preserved in AD, irrespective of the task used; in other words, there was not interference from explicit memory. In addition, it was verified that is possible through implicit memory compensatory strategies such as, activities of daily living (ADL) to compensate for the explicit memory deficits. In this sense, cognitive rehabilitation (CR) demonstrates reasonable results in the process of compensation of explicit memory deficits. Concluding, the decline in explicit memory suggests that both systems are functionally independent even if the other is compromised. We expect that when explicit memory system is not involved in competition with the implicit system, the final effect of learning is better, because all of the implicit memory capacity is engaged in learning and not in competition with the explicit system. Key WoRDS: activities of daily living, Alzheimer's disease, cognitive rehabilitation, implicit memory. Doença de Alzheimer e memória implícitaResumo -Distúrbios neuropsiquiátricos específicos, tais como a doença de Alzheimer (DA), podem afetar algumas formas de memória enquanto deixam outros relativamente intactos. Nesta revisão, nós investigamos particularidades da relação entre as memórias explicita e implícita na DA. Foi verificado que a memória é preservada na DA, independente da tarefa usada; ou seja, não ocorre interferência da memória explícita. Além disso, foi verificado que é possível através de estratégias compensatórias de memória implícita, tais como, atividades da vida diária (AVD) compensar os déficits da memória explícita. Neste sentido, a reabilitação cognitiva (RC) demonstra resultados razoáveis no processo de compensação dos déficits da memória explicita. Concluindo, a queda na memória explícita sugere que ambos os sistemas são funcionalmente independentes mesmo que outro esteja comprometido. esperamos que quando o sistema de memória explícita não está envolvido em competição com o sistema implícito, o efeito final de aprendizagem é melhor, porque toda a capacidade da memória implícita está engajada na aprendizagem e não na competição com o sistema explícito. PALAVRAS-ChAVe: atividades da vida diária, doença de Alzheimer, memória implícita, reabilitação cognitiva.

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“…The combination of the forward prediction and the feedback estimate is optimal, i.e., the internal model is most precise, when the two sources of information are weighted according to their reliability. The optimal relative contributions of the sensory feedback and the forward prediction are described by "Kalman gain" (Kalman and Bucy, 1961) computed recursively from the sensory and motor variance (Denève et al, 2007). The Kalman gain increases as a function of the motor noise and decreases as a function of the sensory noise (see Materials and Methods).…”

Section: Introductionmentioning

confidence: 99%

Optimal Sensorimotor Control in Eye Movement Sequences

Munuera¹,

Morel²,

Duhamel³

et al. 2009

J. Neurosci.

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Fast and accurate motor behavior requires combining noisy and delayed sensory information with knowledge of self-generated body motion; much evidence indicates that humans do this in a near-optimal manner during arm movements. However, it is unclear whether this principle applies to eye movements. We measured the relative contributions of visual sensory feedback and the motor efference copy (and/or proprioceptive feedback) when humans perform two saccades in rapid succession, the first saccade to a visual target and the second to a memorized target. Unbeknownst to the subject, we introduced an artificial motor error by randomly "jumping" the visual target during the first saccade. The correction of the memory-guided saccade allowed us to measure the relative contributions of visual feedback and efferent copy (and/or proprioceptive feedback) to motor-plan updating. In a control experiment, we extinguished the target during the saccade rather than changing its location to measure the relative contribution of motor noise and target localization error to saccade variability without any visual feedback. The motor noise contribution increased with saccade amplitude, but remained Ͻ30% of the total variability. Subjects adjusted the gain of their visual feedback for different saccade amplitudes as a function of its reliability. Even during trials where subjects performed a corrective saccade to compensate for the target-jump, the correction by the visual feedback, while stronger, remained far below 100%. In all conditions, an optimal controller predicted the visual feedback gain well, suggesting that humans combine optimally their efferent copy and sensory feedback when performing eye movements.

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Optimal Sensorimotor Integration in Recurrent Cortical Networks: A Neural Implementation of Kalman Filters

Cited by 117 publications

References 73 publications

Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics

Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics

Alzheimer's disease and implicit memory

Optimal Sensorimotor Control in Eye Movement Sequences

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