Modular neuron-based body estimation: maintaining consistency over different limbs, modalities, and frames of reference

Abstract

This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control.

Population based Mean of Multiple Computations networks: A building block for kinematic models

“…This would allow to incorporate additional sensor readings. Sensor failures could be detected and accounted for, similar to the error-handling that's described in [19]. Again, as an additional influence and to stabilize the dynamics, comparable to the classical MMC approach [8], the current activation of a neuron is also fed back as an input to that neuron and is fused with the outputs of the MLPs.…”

“…The explicit representation of prototypic postures also imposes a problem regarding the scalability of the model with respect to the complexity of the manipulator. To overcome these drawbacks, the authors presented the neural Modular Modality Frame (nMMF) in [19]. The nMMF model relies only on local representations and is in this respect comparable to the MMC approach as it follows in principle a similar idea.…”

Editorial: Modularity in motor control: from muscle synergies to cognitive action representation

“…Ehrenfeld et al ( 2013 ) address the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The results showed that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions.…”

Setup of a Recurrent Neural Network as a Body Model for Solving Inverse and Forward Kinematics as well as Dynamics for a Redundant Manipulator

“…This differs considerably from most approaches to motor control. One system that follows a similar approach is the (neural) Modular Modality Frame (nMMF) model [21] which aims at a probabilistic state estimate based on the integration of redundant information sources. Comparable to our model it is constituted of different frames of reference and transformations between those.…”