Population-Wide Bias of Surround Suppression in Auditory Spatial Receptive Fields of the Owl's Midbrain

Wang, Yunyan; Shanbhag, Sharad J.; Fischer, Brian J.; Peña, José Luis

doi:10.1523/jneurosci.0047-12.2012

Cited by 18 publications

(39 citation statements)

References 82 publications

(122 reference statements)

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“…Surround suppression is consistent with the sharpened spatial tuning in ICx during white noise stimulation (Wang et al, 2012), and with the broadening of tuning after the application of GABA antagonists (Fujita and Konishi, 1991; Mori, 1997; Zheng and Knudsen, 1999, 2001). It is also possible that GABA-mediated lateral inhibition in space originates upstream to ICx.…”

Section: Surround Suppression In Auditory Spatial Receptive Fieldssupporting

confidence: 60%

Coding space-time stimulus dynamics in auditory brain maps

2014

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Sensory maps are often distorted representations of the environment, where ethologically-important ranges are magnified. The implication of a biased representation extends beyond increased acuity for having more neurons dedicated to a certain range. Because neurons are functionally interconnected, non-uniform representations influence the processing of high-order features that rely on comparison across areas of the map. Among these features are time-dependent changes of the auditory scene generated by moving objects. How sensory representation affects high order processing can be approached in the map of auditory space of the owl's midbrain, where locations in the front are over-represented. In this map, neurons are selective not only to location but also to location over time. The tuning to space over time leads to direction selectivity, which is also topographically organized. Across the population, neurons tuned to peripheral space are more selective to sounds moving into the front. The distribution of direction selectivity can be explained by spatial and temporal integration on the non-uniform map of space. Thus, the representation of space can induce biased computation of a second-order stimulus feature. This phenomenon is likely observed in other sensory maps and may be relevant for behavior.

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Section: Surround Suppression In Auditory Spatial Receptive Fieldssupporting

confidence: 60%

Coding space-time stimulus dynamics in auditory brain maps

2014

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“…Data collection methods have been described in detail previously (Peña and Konishi 2002; Wang et al 2012). Procedures complied with the U.S. National Institutes of Health and the Albert Einstein College of Medicine's Institute of Animal Studies guidelines.…”

Section: Methodsmentioning

confidence: 99%

Neural representation of probabilities for Bayesian inference

et al. 2015

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Bayesian models are often successful in describing perception and behavior, but the neural representation of probabilities remains in question. There are several distinct proposals for the neural representation of probabilities, but they have not been directly compared in an example system. Here we consider three models: a non-uniform population code where the stimulus-driven activity and distribution of preferred stimuli in the population represent a likelihood function and a prior, respectively; the sampling hypothesis which proposes that the stimulus-driven activity over time represents a posterior probability and that the spontaneous activity represents a prior; and the class of models which propose that a population of neurons represents a posterior probability in a distributed code. It has been shown that the non-uniform population code model matches the representation of auditory space generated in the owl's external nucleus of the inferior colliculus (ICx). However, the alternative models have not been tested, nor have the three models been directly compared in any system. Here we tested the three models in the owl's ICx. We found that spontaneous firing rate and the average stimulus-driven response of these neurons were not consistent with predictions of the sampling hypothesis. We also found that neural activity in ICx under varying levels of sensory noise did not reflect a posterior probability. On the other hand, the responses of ICx neurons were consistent with the non-uniform population code model. We further show that Bayesian inference can be implemented in the non uniform population code model using one spike per neuron when the population is large and is thus able to support the rapid inference that is necessary for sound localization.

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“…including the auditory cortex [44], visual cortex [45], motor cortex [43]. Computational models that mimic surround suppression (surround inhibition) are used in a variety of tasks such as motion detection and noise suppression [46], contour detection [27], speech processing [47].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…How the auditory cortex maps the auditory space is a key factor in the surround suppression. For instance in [44] authors show that in barn owls the mapping of the frontal region of auditory space is overrepresented. They show a population-wide bias in surround suppression such that suppression from frontal space is stronger.…”

Section: Accepted Manuscriptmentioning

confidence: 99%