A modular theory of multisensory integration for motor control

Abstract

To control targeted movements, such as reaching to grasp an object or hammering a nail, the brain can use divers sources of sensory information, such as vision and proprioception. Although a variety of studies have shown that sensory signals are optimally combined according to principles of maximum likelihood, increasing evidence indicates that the CNS does not compute a single, optimal estimation of the target's position to be compared with a single optimal estimation of the hand. Rather, it employs a more modular approach in which the overall behavior is built by computing multiple concurrent comparisons carried out simultaneously in a number of different reference frames. The results of these individual comparisons are then optimally combined in order to drive the hand. In this article we examine at a computational level two formulations of concurrent models for sensory integration and compare this to the more conventional model of converging multi-sensory signals. Through a review of published studies, both our own and those performed by others, we produce evidence favoring the concurrent formulations. We then examine in detail the effects of additive signal noise as information flows through the sensorimotor system. By taking into account the noise added by sensorimotor transformations, one can explain why the CNS may shift its reliance on one sensory modality toward a greater reliance on another and investigate under what conditions those sensory transformations occur. Careful consideration of how transformed signals will co-vary with the original source also provides insight into how the CNS chooses one sensory modality over another. These concepts can be used to explain why the CNS might, for instance, create a visual representation of a task that is otherwise limited to the kinesthetic domain (e.g., pointing with one hand to a finger on the other) and why the CNS might choose to recode sensory information in an external reference frame.

Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems

“…We find that, despite chaos, the network’s spike patterns encode temporal features of stimuli with sufficient precision so that the responses to close-by stimuli can be accurately discriminated. We relate this coding precision to previous work grounded in the mathematical theory of dynamical systems, which shows that—at the level of multi-neuron spike patterns—chaotic networks do not produce as much variability as one might guess at first glance [14, 15]. This is because in such networks, the trial-to-trial variability of spike trains evoked by time-dependent stimuli leads to the formation of low-dimensional chaotic attractors.…”

“…The dynamics of each neuron –representing an axis on the torus– is given by where F ( θ i ) = 1 + cos(2 πθ i ), Z ( θ i ) = 1 − cos(2 πθ i ) (the canonical phase response curve of a Type I neuron [39]), and g ( θ j ) is a sharp “bump” function, nonzero only near the spiking phase θ j = 1 ∼ 0. As in [14, 15], we set with b = 1/20 and d = 35/32. This phase coupling function is chosen to model the rapid rise and fall of post-synaptic currents, while being differentiable everywhere so that the vector field defined by Eq (2) remains smooth.…”

“…Indeed, chaos may represent a mechanism by which other sources of variability are amplified. Importantly, we find that chaos in driven networks produce a type of intermittent variability, with some spiking “events” predictably repeated across trials [14, 15] (cf. [30–32]).…”

Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter

“…Finally, as we move away from the prerequisite of spike-sorting, multivariate marked point process models can be developed to describe coupling between neurons (Ba et al, 2014). …”

Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network

“…Here, η represents the learning rate, α denotes the momentum factor, and δ pk denotes the deviation value between t k p and y k p , which shows the difference between the reference value and the practical output of the p th sample caused by the k th output neutron [48]. Therefore, the weights of middle layer can then be adjusted into $\begin{array}{c}\mathrm{\Delta }{w}_{ij}\left(n+\mathrm{1}\right)=\eta {\epsilon }_j^p{O}_i^p+\alpha \mathrm{\Delta }{w}_{ij}\left(n\right);\\ {\epsilon }_j^p=f_j^{\prime }\left({\text{net}}_j^p\right)\underset{k=\mathrm{normal1}}{\overset{NK}{{\displaystyle false\sum }}}{\delta }_{pk}{w}_{jk}.\end{array}$ …”