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.

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

Toward a Unified Framework for Cognitive Maps

2020 ◽  
Vol 32 (12) ◽  
pp. 2455-2485
Author(s):  
Woori Kim ◽  
Yongseok Yoo
Keyword(s):  
Belief Propagation ◽  
Spatial Information ◽  
Complex Dynamics ◽  
Spatial Location ◽  
Neural Representation ◽  
Modular Structure ◽  
Unified Framework ◽  
Unimodal Function ◽  
Neural Representations ◽  
The Brain

In this study, we integrated neural encoding and decoding into a unified framework for spatial information processing in the brain. Specifically, the neural representations of self-location in the hippocampus (HPC) and entorhinal cortex (EC) play crucial roles in spatial navigation. Intriguingly, these neural representations in these neighboring brain areas show stark differences. Whereas the place cells in the HPC fire as a unimodal function of spatial location, the grid cells in the EC show periodic tuning curves with different periods for different subpopulations (called modules). By combining an encoding model for this modular neural representation and a realistic decoding model based on belief propagation, we investigated the manner in which self-location is encoded by neurons in the EC and then decoded by downstream neurons in the HPC. Through the results of numerical simulations, we first show the positive synergy effects of the modular structure in the EC. The modular structure introduces more coupling between heterogeneous modules with different periodicities, which provides increased error-correcting capabilities. This is also demonstrated through a comparison of the beliefs produced for decoding two- and four-module codes. Whereas the former resulted in a complete decoding failure, the latter correctly recovered the self-location even from the same inputs. Further analysis of belief propagation during decoding revealed complex dynamics in information updates due to interactions among multiple modules having diverse scales. Therefore, the proposed unified framework allows one to investigate the overall flow of spatial information, closing the loop of encoding and decoding self-location in the brain.

scholarly journals Brain mast cells link the immune system to anxiety-like behavior

2008 ◽  
Vol 105 (46) ◽  
pp. 18053-18057 ◽  
Author(s):  
Katherine M. Nautiyal ◽  
Ana C. Ribeiro ◽  
Donald W. Pfaff ◽  
Rae Silver
Keyword(s):  
Mast Cell ◽  
Mast Cells ◽  
Cell Activation ◽  
Mast Cell Activation ◽  
Neural Systems ◽  
Loss Of Function ◽  
Littermate Control ◽  
Cytokines And Chemokines ◽  
Function Models ◽  
The Brain

Mast cells are resident in the brain and contain numerous mediators, including neurotransmitters, cytokines, and chemokines, that are released in response to a variety of natural and pharmacological triggers. The number of mast cells in the brain fluctuates with stress and various behavioral and endocrine states. These properties suggest that mast cells are poised to influence neural systems underlying behavior. Using genetic and pharmacological loss-of-function models we performed a behavioral screen for arousal responses including emotionality, locomotor, and sensory components. We found that mast cell deficient KitW−sh/W−sh (sash−/−) mice had a greater anxiety-like phenotype than WT and heterozygote littermate control animals in the open field arena and elevated plus maze. Second, we show that blockade of brain, but not peripheral, mast cell activation increased anxiety-like behavior. Taken together, the data implicate brain mast cells in the modulation of anxiety-like behavior and provide evidence for the behavioral importance of neuroimmune links.

scholarly journals FAULT-TOLERANT MULTI-AGENT EXACT BELIEF PROPAGATION

2009 ◽  
Vol 25 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Xiangdong An ◽  
Nick Cercone
Keyword(s):  
Belief Propagation ◽  
Fault Tolerant ◽  
Multi Agent

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

Context dependence of receptive field remapping in superior colliculus

2011 ◽  
Vol 106 (4) ◽  
pp. 1862-1874 ◽  
Author(s):  
Jan Churan ◽  
Daniel Guitton ◽  
Christopher C. Pack
Keyword(s):  
Receptive Field ◽  
Superior Colliculus ◽  
Visual Stimulation ◽  
Receptive Fields ◽  
Spatial Location ◽  
Macaque Monkey ◽  
Visual Signals ◽  
Perceptual Stability ◽  
Visual Background ◽  
The Brain

Our perception of the positions of objects in our surroundings is surprisingly unaffected by movements of the eyes, head, and body. This suggests that the brain has a mechanism for maintaining perceptual stability, based either on the spatial relationships among visible objects or internal copies of its own motor commands. Strong evidence for the latter mechanism comes from the remapping of visual receptive fields that occurs around the time of a saccade. Remapping occurs when a single neuron responds to visual stimuli placed presaccadically in the spatial location that will be occupied by its receptive field after the completion of a saccade. Although evidence for remapping has been found in many brain areas, relatively little is known about how it interacts with sensory context. This interaction is important for understanding perceptual stability more generally, as the brain may rely on extraretinal signals or visual signals to different degrees in different contexts. Here, we have studied the interaction between visual stimulation and remapping by recording from single neurons in the superior colliculus of the macaque monkey, using several different visual stimulus conditions. We find that remapping responses are highly sensitive to low-level visual signals, with the overall luminance of the visual background exerting a particularly powerful influence. Specifically, although remapping was fairly common in complete darkness, such responses were usually decreased or abolished in the presence of modest background illumination. Thus the brain might make use of a strategy that emphasizes visual landmarks over extraretinal signals whenever the former are available.

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 Mimicry is associated with procedural learning, not social bonding, neural systems in autism

2020 ◽  
Author(s):  
Bahar Tunçgenç ◽  
Carolyn Koch ◽  
Amira Herstic ◽  
Inge-Marie Eigsti ◽  
Stewart Mostofsky
Keyword(s):  
Social Bonding ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Skill Learning ◽  
Autism Spectrum Conditions ◽  
Neural Systems ◽  
Procedural Skill ◽  
Communication Difficulties ◽  
Learning Functions ◽  
The Brain

AbstractMimicry facilitates social bonding throughout the lifespan. Mimicry impairments in autism spectrum conditions (ASC) are widely reported, including differentiation of the brain networks associated with its social bonding and learning functions. This study examined associations between volumes of brain regions associated with social bonding versus procedural skill learning, and mimicry of gestures during a naturalistic interaction in ASC and neurotypical (NT) children. Consistent with predictions, results revealed reduced mimicry in ASC relative to the NT children. Mimicry frequency was negatively associated with autism symptom severity. Mimicry was predicted predominantly by the volume of procedural skill learning regions in ASC, and by bonding regions in NT. Further, bonding regions contributed significantly less to mimicry in ASC than in NT, while the contribution of learning regions was not different across groups. These findings suggest that associating mimicry with skill learning, rather than social bonding, may partially explain observed communication difficulties in ASC.

scholarly journals Population rate-coding predicts correctly that human sound localization depends on sound intensity

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Antje Ihlefeld ◽  
Nima Alamatsaz ◽  
Robert M Shapley
Keyword(s):  
Sound Localization ◽  
Sound Intensity ◽  
Spatial Location ◽  
Spike Rate ◽  
Behavioral Experiments ◽  
Interaural Time Differences ◽  
Sound Levels ◽  
Rate Coding ◽  
The Brain ◽  
Population Rate

Human sound localization is an important computation performed by the brain. Models of sound localization commonly assume that sound lateralization from interaural time differences is level invariant. Here we observe that two prevalent theories of sound localization make opposing predictions. The labelled-line model encodes location through tuned representations of spatial location and predicts that perceived direction is level invariant. In contrast, the hemispheric-difference model encodes location through spike-rate and predicts that perceived direction becomes medially biased at low sound levels. Here, behavioral experiments find that softer sounds are perceived closer to midline than louder sounds, favoring rate-coding models of human sound localization. Analogously, visual depth perception, which is based on interocular disparity, depends on the contrast of the target. The similar results in hearing and vision suggest that the brain may use a canonical computation of location: encoding perceived location through population spike rate relative to baseline.

scholarly journals Brain signatures of surprise in EEG and MEG data

2020 ◽  
Author(s):  
Zahra Mousavi ◽  
Mohammad Mahdi Kiani ◽  
Hamid Aghajan
Keyword(s):  
Sensory Modality ◽  
Ideal Observer ◽  
Neural Signals ◽  
Sensory Data ◽  
Temporal Features ◽  
Recorded Data ◽  
Recent Trends ◽  
Temporal Sample ◽  
Past Experiences ◽  
The Brain

AbstractThe brain is constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from predictions shaped by recent trends, neural signals are generated to report this surprise. Existing models for quantifying surprise are based on an ideal observer assumption operating under one of the three definitions of surprise set forth as the Shannon, Bayesian, and Confidence-corrected surprise. In this paper, we analyze both visual and auditory EEG and auditory MEG signals recorded during oddball tasks to examine which temporal components in these signals are sufficient to decode the brain’s surprise based on each of these three definitions. We found that for both recording systems the Shannon surprise is always significantly better decoded than the Bayesian surprise regardless of the sensory modality and the selected temporal features used for decoding.Author summaryA regression model is proposed for decoding the level of the brain’s surprise in response to sensory sequences using selected temporal components of recorded EEG and MEG data. Three surprise quantification definitions (Shannon, Bayesian, and Confidence-corrected surprise) are compared in offering decoding power. Four different regimes for selecting temporal samples of EEG and MEG data are used to evaluate which part of the recorded data may contain signatures that represent the brain’s surprise in terms of offering a high decoding power. We found that both the middle and late components of the EEG response offer strong decoding power for surprise while the early components are significantly weaker in decoding surprise. In the MEG response, we found that the middle components have the highest decoding power while the late components offer moderate decoding powers. When using a single temporal sample for decoding surprise, samples of the middle segment possess the highest decoding power. Shannon surprise is always better decoded than the other definitions of surprise for all the four temporal feature selection regimes. Similar superiority for Shannon surprise is observed for the EEG and MEG data across the entire range of temporal sample regimes used in our analysis.

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