The language of the brain: real-world neural population codes

2019 ◽  
Vol 58 ◽  
pp. 30-36 ◽  
Author(s):  
J Andrew Pruszynski ◽  
Joel Zylberberg
Keyword(s):  
Real World ◽  
Neural Population ◽  
Population Codes ◽  
The Brain

On Decoding Grid Cell Population Codes Using Approximate Belief Propagation

2017 ◽  
Vol 29 (3) ◽  
pp. 716-734 ◽  
Author(s):  
Yongseok Yoo ◽  
Woori Kim
Keyword(s):  
Belief Propagation ◽  
Fault Tolerant ◽  
Grid Cell ◽  
Spatial Location ◽  
Realistic Model ◽  
Ideal Observer ◽  
Neural Population ◽  
Neural Systems ◽  
Population Codes ◽  
The Brain

Neural systems are inherently noisy. One well-studied example of a noise reduction mechanism in the brain is the population code, where representing a variable with multiple neurons allows the encoded variable to be recovered with fewer errors. Studies have assumed ideal observer models for decoding population codes, and the manner in which information in the neural population can be retrieved remains elusive. This letter addresses a mechanism by which realistic neural circuits can recover encoded variables. Specifically, the decoding problem of recovering a spatial location from populations of grid cells is studied using belief propagation. We extend the belief propagation decoding algorithm in two aspects. First, beliefs are approximated rather than being calculated exactly. Second, decoding noises are introduced into the decoding circuits. Numerical simulations demonstrate that beliefs can be effectively approximated by combining polynomial nonlinearities with divisive normalization. This approximate belief propagation algorithm is tolerant to decoding noises. Thus, this letter presents a realistic model for decoding neural population codes and investigates fault-tolerant information retrieval mechanisms in the brain.

scholarly journals Reassessing optimal neural population codes with neurometric functions

2011 ◽  
Vol 108 (11) ◽  
pp. 4423-4428 ◽  
Author(s):  
P. Berens ◽  
A. S. Ecker ◽  
S. Gerwinn ◽  
A. S. Tolias ◽  
M. Bethge
Keyword(s):  
Neural Population ◽  
Population Codes

scholarly journals The Geometry of Information Coding in Correlated Neural Populations

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Rava Azeredo da Silveira ◽  
Fred Rieke
Keyword(s):  
Neural Coding ◽  
Neural Population ◽  
Annual Review ◽  
Publication Date ◽  
Information Coding ◽  
Population Code ◽  
New Approach ◽  
Neural Populations ◽  
The Brain ◽  
Theoretical Developments

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Adrian Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

Previous research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as ‘stiff’ dimensions, while it is insensitive to many others (‘sloppy’ dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

scholarly journals Cortical state transitions and stimulus response evolve along stiff and sloppy parameter dimensions, respectively

2019 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Gabriela Mochol ◽  
Ainhoa Hermoso-Mendizabal ◽  
Jaime de la Rocha ◽  
Gustavo Deco
Keyword(s):  
Collective Behavior ◽  
Neural Population ◽  
State Transitions ◽  
Population Activity ◽  
Sensory Inputs ◽  
Stimulus Response ◽  
Spontaneous Neuronal Activity ◽  
The Brain ◽  
Shed Light ◽  
Neural Population Activity

SummaryPrevious research showed that spontaneous neuronal activity presents sloppiness: the collective behavior is strongly determined by a small number of parameter combinations, defined as “stiff” dimensions, while it is insensitive to many others (“sloppy” dimensions). Here, we analyzed neural population activity from the auditory cortex of anesthetized rats while the brain spontaneously transited through different synchronized and desynchronized states and intermittently received sensory inputs. We showed that cortical state transitions were determined by changes in stiff parameters associated with the activity of a core of neurons with low responses to stimuli and high centrality within the observed network. In contrast, stimulus-evoked responses evolved along sloppy dimensions associated with the activity of neurons with low centrality and displaying large ongoing and stimulus-evoked fluctuations without affecting the integrity of the network. Our results shed light on the interplay among stability, flexibility, and responsiveness of neuronal collective dynamics during intrinsic and induced activity.

scholarly journals Structure-Aware Trail Bundling for Large DTI Datasets

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 316
Author(s):  
Steven Bouma ◽  
Christophe Hurter ◽  
Alexandru Telea
Keyword(s):  
Real World ◽  
User Study ◽  
Image Space ◽  
Small Scale ◽  
Medical Professionals ◽  
Specific Context ◽  
Connectivity Structure ◽  
Geometric Space ◽  
The Brain ◽  
Rendering Techniques

Creating simplified visualizations of large 3D trail sets with limited occlusion and preservation of the main structures in the data is challenging. We address this challenge for the specific context of 3D fiber trails created by DTI tractography. For this, we propose to jointly simplify trails in both the geometric space (by extending and adapting an existing bundling method to handle 3D trails) and in the image space (by proposing several shading and rendering techniques). Our method can handle 3D datasets of hundreds of thousands of trails at interactive rate, has parameters for the most of which good preset values are given, and produces visualizations that have been found, in a small-scale user study involving five medical professionals, to be better in occlusion reduction, conveying the connectivity structure of the brain, and overall clarity than existing methods for the same data. We demonstrate our technique with several real-world public DTI datasets.

scholarly journals Inference in the Brain: Statistics Flowing in Redundant Population Codes

Neuron ◽  
2017 ◽  
Vol 94 (5) ◽  
pp. 943-953 ◽  
Author(s):  
Xaq Pitkow ◽  
Dora E. Angelaki
Keyword(s):  
Population Codes ◽  
The Brain

Practical Challenges in Collecting Head Impact Biomechanics Data in Real-World Scenarios via a Helmet-Based Accelerometer System

2013 ◽  
Author(s):  
Mari A. Allison ◽  
Yun Seok Kang ◽  
Matthew R. Maltese ◽  
John H. Bolte ◽  
Kristy B. Arbogast
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Real World ◽  
Head Impact ◽  
Permanent Damage ◽  
Head Impact Biomechanics ◽  
Impact Biomechanics ◽  
The Brain

Recent studies have shown that mild traumatic brain injury (mTBI) can have long-term neurological consequences and may cause permanent damage to the brain [1,2]. Given estimates that millions of these injuries occur each year [3], this knowledge has created a demand for countermeasures to prevent mTBI. In order to create countermeasures, the biomechanical inputs leading to mTBI, which are still a matter of debate, must be better understood in both children and adults.

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