Dataset Information

Echo-acoustic flow shapes object representation in spatially complex acoustic scenes.

ABSTRACT: Echolocating bats use echoes of their sonar emissions to determine the position and distance of objects or prey. Target distance is represented as a map of echo delay in the auditory cortex (AC) of bats. During a bat's flight through a natural complex environment, echo streams are reflected from multiple objects along its flight path. Separating such complex streams of echoes or other sounds is a challenge for the auditory system of bats as well as other animals. We investigated the representation of multiple echo streams in the AC of anesthetized bats (Phyllostomus discolor) and tested the hypothesis that neurons can lock on echoes from specific objects in a complex echo-acoustic pattern while the representation of surrounding objects is suppressed. We combined naturalistic pulse/echo sequences simulating a bat's flight through a virtual acoustic space with extracellular recordings. Neurons could selectively lock on echoes from one object in complex echo streams originating from two different objects along a virtual flight path. The objects were processed sequentially in the order in which they were approached. Object selection depended on sequential changes of echo delay and amplitude, but not on absolute values. Furthermore, the detailed representation of the object echo delays in the cortical target range map was not fixed but could be dynamically adapted depending on the temporal pattern of sonar emission during target approach within a simulated flight sequence.NEW & NOTEWORTHY Complex signal analysis is a challenging task in sensory processing for all animals, particularly for bats because they use echolocation for navigation in darkness. Recent studies proposed that the bat's perceptional system might organize complex echo-acoustic information into auditory streams, allowing it to track specific auditory objects during flight. We show that in the auditory cortex of bats, neurons can selectively respond to echo streams from specific objects.

SUBMITTER: Greiter W

PROVIDER: S-EPMC5454466 | biostudies-other | 2017 Jun

REPOSITORIES: biostudies-other

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Publications

Echo-acoustic flow shapes object representation in spatially complex acoustic scenes.

Greiter Wolfgang W Firzlaff Uwe U

Journal of neurophysiology 20170308 6

Echolocating bats use echoes of their sonar emissions to determine the position and distance of objects or prey. Target distance is represented as a map of echo delay in the auditory cortex (AC) of bats. During a bat's flight through a natural complex environment, echo streams are reflected from multiple objects along its flight path. Separating such complex streams of echoes or other sounds is a challenge for the auditory system of bats as well as other animals. We investigated the representati ...[more]

PMID: 28275060

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Project description:Scene understanding and decomposition is a crucial challenge for intelligent systems, whether it is for object manipulation, navigation, or any other task. Although current machine and deep learning approaches for object detection and classification obtain high accuracy, they typically do not leverage interaction with the world and are limited to a set of objects seen during training. Humans on the other hand learn to recognize and classify different objects by actively engaging with them on first encounter. Moreover, recent theories in neuroscience suggest that cortical columns in the neocortex play an important role in this process, by building predictive models about objects in their reference frame. In this article, we present an enactive embodied agent that implements such a generative model for object interaction. For each object category, our system instantiates a deep neural network, called Cortical Column Network (CCN), that represents the object in its own reference frame by learning a generative model that predicts the expected transform in pixel space, given an action. The model parameters are optimized through the active inference paradigm, i.e., the minimization of variational free energy. When provided with a visual observation, an ensemble of CCNs each vote on their belief of observing that specific object category, yielding a potential object classification. In case the likelihood on the selected category is too low, the object is detected as an unknown category, and the agent has the ability to instantiate a novel CCN for this category. We validate our system in an simulated environment, where it needs to learn to discern multiple objects from the YCB dataset. We show that classification accuracy improves as an embodied agent can gather more evidence, and that it is able to learn about novel, previously unseen objects. Finally, we show that an agent driven through active inference can choose their actions to reach a preferred observation.

Dataset Information

Echo-acoustic flow shapes object representation in spatially complex acoustic scenes.

Publications

Echo-acoustic flow shapes object representation in spatially complex acoustic scenes.

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