Abstract
In this paper, we suggest that perception could be modeled by assuming that sensory input is generated by a hierarchy of attractors in a dynamic system. We describe a mathematical model which exploits the temporal structure of rapid sensory dynamics to track the slower trajectories of their underlying causes. This model establishes a proof of concept that slowly changing neuronal states can encode the trajectories of faster sensory signals. We link this hierarchical account to recent developments in the perception of human action; in particular artificial speech recognition. We argue that these hierarchical models of dynamical systems are a plausible starting point to develop robust recognition schemes, because they capture critical temporal dependencies induced by deep hierarchical structure. We conclude by suggesting that a fruitful computational neuroscience approach may emerge from modeling perception as non-autonomous recognition dynamics enslaved by autonomous hierarchical dynamics in the sensorium.
Highlights
There have been tremendous advances in the development of algorithms and devices that can extract meaningful information from their environment, we seem still far away from building machines that perceive as robustly and as quickly as our brains
We link this hierarchical account to recent developments in the perception of human action; in particular artificial speech recognition. We argue that these hierarchical models of dynamical systems are a plausible starting point to develop robust recognition schemes, because they capture critical temporal dependencies induced by deep hierarchical structure
The question we address in this paper is whether these developments in hierarchical, trajectory-based perception models point to a computational principle which can be implemented by the brain
Summary
There have been tremendous advances in the development of algorithms and devices that can extract meaningful information from their environment, we seem still far away from building machines that perceive as robustly and as quickly as our brains. A novel approach is emerging that suggests a fundamental computational principle: the idea is to model fast acoustic features of speech as the expression of comparatively slow articulator movement (Deng et al, 2006; King et al, 2007; McDermott and Nakamura, 2006). These models describe speech as a hierarchy of dynamic systems, where the lowest (fastest) level generates auditory output. Similar hierarchical models have been considered for making inference on dynamic human behavior, such as those used in robot-human interaction or surveillance technology (Kruger et al, 2007; Moeslund et al, 2006; Oliver et al, 2004; Robertson and Reid, 2006; Saenko et al, 2005; Yam et al, 2004)
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