Effect of background clutter on neural discrimination in the bat auditory midbrain

Author(s):  
Kathryne M Allen ◽  
Angeles Salles ◽  
Sanwook Park ◽  
Mounya Elhilali ◽  
Cynthia F. Moss

The discrimination of complex sounds is a fundamental function of the auditory system. This operation must be robust in the presence of noise and acoustic clutter. Echolocating bats are auditory specialists that discriminate sonar objects in acoustically complex environments. Bats produce brief signals, interrupted by periods of silence, rendering echo snapshots of sonar objects. Sonar object discrimination requires that bats process spatially and temporally overlapping echoes to make split-second decisions. The mechanisms that enable this discrimination are not well understood, particularly in complex environments. We explored the neural underpinnings of sonar object discrimination in the presence of acoustic scattering caused by physical clutter. We performed electrophysiological recordings in the inferior colliculus of awake big brown bats, to broadcasts of pre-recorded echoes from physical objects. We acquired single unit responses to echoes and discovered a sub-population of IC neurons that encode acoustic features that can be used to discriminate between sonar objects. We further investigated the effects of environmental clutter on this population's encoding of acoustic features. We discovered that the effect of background clutter on sonar object discrimination is highly variable and depends on object properties and target-clutter spatio-temporal separation. In many conditions, clutter impaired discrimination of sonar objects. However, in some instances clutter enhanced acoustic features of echo returns, enabling higher levels of discrimination. This finding suggests that environmental clutter may augment acoustic cues used for sonar target discrimination and provides further evidence in a growing body of literature that noise is not universally detrimental to sensory encoding.

Lab on a Chip ◽  
2009 ◽  
Vol 9 (18) ◽  
pp. 2644 ◽  
Author(s):  
Luca Berdondini ◽  
Kilian Imfeld ◽  
Alessandro Maccione ◽  
Mariateresa Tedesco ◽  
Simon Neukom ◽  
...  

PLoS ONE ◽  
2012 ◽  
Vol 7 (6) ◽  
pp. e39073 ◽  
Author(s):  
Suma Choorapoikayil ◽  
Bernd Willems ◽  
Peter Ströhle ◽  
Martin Gajewski

2007 ◽  
pp. 176-193
Author(s):  
Qian Diao ◽  
Jianye Lu ◽  
Wei Hu ◽  
Yimin Zhang ◽  
Gary Bradski

In a visual tracking task, the object may exhibit rich dynamic behavior in complex environments that can corrupt target observations via background clutter and occlusion. Such dynamics and background induce nonlinear, nonGaussian and multimodal observation densities. These densities are difficult to model with traditional methods such as Kalman filter models (KFMs) due to their Gaussian assumptions. Dynamic Bayesian networks (DBNs) provide a more general framework in which to solve these problems. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. Under the DBN umbrella, a broad class of learning and inference algorithms for time-series models can be used in visual tracking. Furthermore, DBNs provide a natural way to combine multiple vision cues. In this chapter, we describe some DBN models for tracking in nonlinear, nonGaussian and multimodal situations, and present a prediction method to assist feature extraction part by making a hypothesis for the new observations.


2019 ◽  
Author(s):  
Peter W. Elliott ◽  
Matthew J. Boring ◽  
Yuanning Li ◽  
R. Mark Richardson ◽  
Avniel Singh Ghuman ◽  
...  

AbstractMultivariate time series from neural electrophysiological recordings are a rich source of information about neural processing systems and require appropriate methods for proper analysis. Current methods for mapping brain function in these data using neural decoding aggregate information across space and time in limited ways, rarely incorporating spatial dependence across recording locations. We propose Shrinkage Classification for Overlapping Time Series (SCOTS), a neural decoding method that maps brain function, while accounting for spatio-temporal dependence, through interpretable dimensionality reduction and classification of multivariate neural time series. SCOTS has two components: first, overlapping clustering from sparse semi-nonnegative matrix factorization gives a data-driven aggregation of neural information across space; second, wavelet-transformed nearest shrunken centroids with sparse group lasso performs multi-class classification with selection of informative clusters and time intervals. We demonstrate use of SCOTS by applying it to human intracranial electrophysiological and MEG data collected while participants viewed visual stimuli from a range of categories. The method reveals the dynamic activation of brain regions with sensitivity to different object categories, giving insight into spatio-temporal contributions of these neural processing systems.


2021 ◽  
Author(s):  
Mirko Klukas ◽  
Sugandha Sharma ◽  
Yilun Du ◽  
Tomas Lozano-Perez ◽  
Leslie Pack Kaelbling ◽  
...  

When animals explore spatial environments, their representations often fragment into multiple maps. What determines these map fragmentations, and can we predict where they will occur with simple principles? We pose the problem of fragmentation of an environment as one of (online) spatial clustering. Taking inspiration from the notion of a "contiguous region" in robotics, we develop a theory in which fragmentation decisions are driven by surprisal. When this criterion is implemented with boundary, grid, and place cells in various environments, it produces map fragmentations from the first exploration of each space. Augmented with a long-term spatial memory and a rule similar to the distance-dependent Chinese Restaurant Process for selecting among relevant memories, the theory predicts the reuse of map fragments in environments with repeating substructures. Our model provides a simple rule for generating spatial state abstractions and predicts map fragmentations observed in electrophysiological recordings. It further predicts that there should be "fragmentation decision" or "fracture" cells, which in multicompartment environments could be called "doorway" cells. Finally, we show that the resulting abstractions can lead to large (orders of magnitude) improvements in the ability to plan and navigate through complex environments.


2018 ◽  
Author(s):  
S Chemla ◽  
A Reynaud ◽  
M di Volo ◽  
Y Zerlaut ◽  
L Perrinet ◽  
...  

SummaryHow does the brain link visual stimuli across space and time? Visual illusions provide an experimental paradigm to study these processes. When two stationary dots are flashed in close spatial and temporal succession, human observers experience a percept of motion. Large spatio-temporal separation challenges the visual system to keep track of object identity along the apparent motion path. Here, we utilize voltage-sensitive dye imaging in primary visual cortex (V1) of the awake monkey to investigate whether intra-cortical connections within V1 can shape cortical dynamics to represent the illusory motion. We find that the arrival of the second stimulus in V1 creates a suppressive wave traveling toward the retinotopic representation of the first. Computational approaches show that this suppressive wave can be explained by recurrent gain control fed by the intra-cortical network and contributes to precisely encode the expected motion velocity. We suggest that non-linear intra-cortical dynamics preformat population responses in V1 for optimal read-out by downstream areas.


Author(s):  
Cai Dieball ◽  
Diego Krapf ◽  
Matthias Weiss ◽  
Aljaz Godec

Abstract Particle transport in complex environments such as the interior of living cells is often (transiently) non-Fickian or anomalous, that is, it deviates from the laws of Brownian motion. Such anomalies may be the result of small-scale spatio-temporal heterogeneities in, or viscoelastic properties of, the medium, molecular crowding, etc. Often the observed dynamics displays multi-state characteristics, i.e. distinct modes of transport dynamically interconverting between each other in a stochastic manner. Reliably distinguishing between single- and multi-state dynamics is challenging and requires a combination of distinct approaches. To complement the existing methods relying on the analysis of the particle’s mean squared displacement, position- or displacement-autocorrelation function, and propagators, we here focus on “scattering fingerprints” of multi-state dynamics. We develop a theoretical framework for two-state scattering signatures – the intermediate scattering function and dynamic structure factor – and apply it to the analysis of simple model systems as well as particle-tracking experiments in living cells. We consider inert tracer-particle motion as well as systems with an internal structure and dynamics. Our results may generally be relevant for the interpretation of state-of-the-art differential dynamic microscopy experiments on complex particulate systems, as well as inelastic or quasielastic neutron (incl. spin-echo) and X-ray scattering scattering probing structural and dynamical properties of macromolecules, when the underlying dynamics displays two-state transport.


Saline silty clay soil (SSCS) is the stochastic spatio-temporal separation of a common territory, which changes under conditions of natural and man-made impact on the salt component of soil. The research methodology of SSCS is proposed here. The design parameters determined by the proposed methodology allow to take into account the entire spectrum of changes in the properties of the SSCS in the base for the standard service life of the designed facility at the design stage, and, therefore, to select reliable geotechnology for construction of the designed facility, thereby ensuring its reliable operation.


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