scholarly journals Framework for quantifying population responses to disturbance reveals that coastal birds are highly resilient to hurricanes

2019 ◽  
Vol 22 (12) ◽  
pp. 2039-2048 ◽  
Author(s):  
Christopher R. Field ◽  
Katharine J. Ruskin ◽  
Jonathan B. Cohen ◽  
Thomas P. Hodgman ◽  
Adrienne I. Kovach ◽  
...  
Keyword(s):  
Population Responses ◽  
Coastal Birds

scholarly journals Feature-Based Attention Increases the Selectivity of Population Responses in Primate Visual Cortex

2004 ◽  
Vol 14 (9) ◽  
pp. 744-751 ◽  
Author(s):  
Julio C Martinez-Trujillo ◽  
Stefan Treue
Keyword(s):  
Visual Cortex ◽  
Population Responses ◽  
Primate Visual Cortex ◽  
Feature Based

scholarly journals Modeling Atlantic salmon (Salmo salar) and brown trout (S. trutta) population responses and interactions under increased minimum flow in a regulated river

2021 ◽  
Vol 162 ◽  
pp. 106182
Author(s):  
Kristine L. Bjørnås ◽  
Steven F. Railsback ◽  
Olle Calles ◽  
John J. Piccolo
Keyword(s):  
Atlantic Salmon ◽  
Salmo Salar ◽  
Brown Trout ◽  
Regulated River ◽  
Minimum Flow ◽  
Population Responses ◽  
Atlantic Salmon Salmo Salar

scholarly journals Population responses to a historic drought across the range of the common monkeyflower ( Mimulus guttatus )

2021 ◽  
Author(s):  
Nicholas J. Kooyers ◽  
Kelsie A. Morioka ◽  
Jack M. Colicchio ◽  
Kaitlyn S. Clark ◽  
Abigail Donofrio ◽  
...  
Keyword(s):  
Mimulus Guttatus ◽  
Population Responses ◽  
The Common

scholarly journals Decision-related feedback in visual cortex lacks spatial selectivity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Katrina R. Quinn ◽  
Lenka Seillier ◽  
Daniel A. Butts ◽  
Hendrikje Nienborg
Keyword(s):  
Contextual Information ◽  
Relevant Information ◽  
Common Mechanism ◽  
Spatial Selectivity ◽  
Population Responses ◽  
Stimulus Choice ◽  
Visual Areas ◽  
Computational Work ◽  
Feature Based ◽  
The Brain

AbstractFeedback in the brain is thought to convey contextual information that underlies our flexibility to perform different tasks. Empirical and computational work on the visual system suggests this is achieved by targeting task-relevant neuronal subpopulations. We combine two tasks, each resulting in selective modulation by feedback, to test whether the feedback reflected the combination of both selectivities. We used visual feature-discrimination specified at one of two possible locations and uncoupled the decision formation from motor plans to report it, while recording in macaque mid-level visual areas. Here we show that although the behavior is spatially selective, using only task-relevant information, modulation by decision-related feedback is spatially unselective. Population responses reveal similar stimulus-choice alignments irrespective of stimulus relevance. The results suggest a common mechanism across tasks, independent of the spatial selectivity these tasks demand. This may reflect biological constraints and facilitate generalization across tasks. Our findings also support a previously hypothesized link between feature-based attention and decision-related activity.

scholarly journals Pseudosparse neural coding in the visual system of primates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sidney R. Lehky ◽  
Keiji Tanaka ◽  
Anne B. Sereno
Keyword(s):  
Neural Coding ◽  
Macaque Monkey ◽  
Implicit Assumption ◽  
Data Set ◽  
Efficient Coding ◽  
Population Responses ◽  
Lateral Intraparietal Cortex ◽  
Neural Populations ◽  
Neurophysiological Data ◽  
The Mean

AbstractWhen measuring sparseness in neural populations as an indicator of efficient coding, an implicit assumption is that each stimulus activates a different random set of neurons. In other words, population responses to different stimuli are, on average, uncorrelated. Here we examine neurophysiological data from four lobes of macaque monkey cortex, including V1, V2, MT, anterior inferotemporal cortex, lateral intraparietal cortex, the frontal eye fields, and perirhinal cortex, to determine how correlated population responses are. We call the mean correlation the pseudosparseness index, because high pseudosparseness can mimic statistical properties of sparseness without being authentically sparse. In every data set we find high levels of pseudosparseness ranging from 0.59–0.98, substantially greater than the value of 0.00 for authentic sparseness. This was true for synthetic and natural stimuli, as well as for single-electrode and multielectrode data. A model indicates that a key variable producing high pseudosparseness is the standard deviation of spontaneous activity across the population. Consistently high values of pseudosparseness in the data demand reconsideration of the sparse coding literature as well as consideration of the degree to which authentic sparseness provides a useful framework for understanding neural coding in the cortex.

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