pwc-archive/papers-with-abstracts huggingface dataset:
Human Inference in Changing Environments With Temporal Structure
Authors: Arthur Prat-Carrabin, Robert C. Wilson, Jonathan D. Cohen, Rava Azeredo da Silveira
Date: 2021-01-27 00:00:00
Tasks: Bayesian Inference

To make informed decisions in natural environments that change over time, humans must update their beliefs as new observations are gathered. Studies exploring human inference as a dynamical process that unfolds in time have focused on situations in which the statistics of observations are history-independent. Yet temporal structure is everywhere in nature, and yields history-dependent observations. Do humans modify their inference processes depending on the latent temporal statistics of their observations? We investigate this question experimentally and theoretically using a change-point inference task. We show that humans adapt their inference process to fine aspects of the temporal structure in the statistics of stimuli. As such, humans behave qualitatively in a Bayesian fashion, but, quantitatively, deviate away from optimality. Perhaps more importantly, humans behave suboptimally in that their responses are not deterministic, but variable. We show that this variability itself is modulated by the temporal statistics of stimuli. To elucidate the cognitive algorithm that yields this behavior, we investigate a broad array of existing and new models that characterize different sources of suboptimal deviations away from Bayesian inference. While models with 'output noise' that corrupts the response-selection process are natural candidates, human behavior is best described by sampling-based inference models, in which the main ingredient is a compressed approximation of the posterior, represented through a modest set of random samples and updated over time. This result comes to complement a growing literature on sample-based representation and learning in humans.
Galvanic vestibular stimulation produces cross-modal improvements in visual thresholds
Authors: Jamie L. Voros, Sage O. Sherman, Rachel Rise, Alexander Kryuchkov, Ponder Stine, Allison P. Anderson, Torin K. Clark
Date: 2021-01-26 00:00:00

Background: Stochastic resonance (SR) refers to a faint signal being enhanced with the addition of white noise. Previous studies have found that vestibular perceptual thresholds are lowered with noisy galvanic vestibular stimulation (i.e., "in-channel" SR). Auditory white noise has been shown to improve tactile and visual thresholds, suggesting "cross-modal" SR. Objective: We aimed to study the cross-modal impact of noisy galvanic vestibular stimulation (nGVS) (n=9 subjects) on visual and auditory thresholds. Methods: We measured auditory and visual perceptual thresholds of human subjects across a swath of different nGVS levels in order to determine if a subject-specific best nGVS level elicited a reduction in thresholds as compared the no noise condition (sham). Results: We found an 18% improvement in visual thresholds (p = 0.026). Among the 7 of 9 subjects with reduced thresholds, the average improvement was 26%. Subjects with higher (worse) visual thresholds with no stimulation (sham) improved more than those with lower thresholds (p = 0.005). Auditory thresholds were unchanged by vestibular stimulation. Conclusions: These results are the first demonstration of cross-modal improvement with nGVS, indicating galvanic vestibular white noise can produce cross-modal improvements in some sensory channels, but not all.
State-dependent regulation of cortical processing speed via gain modulation
Authors: David Wyrick, Luca Mazzucato
Date: 2020-04-08 00:00:00

To thrive in dynamic environments, animals must be capable of rapidly and flexibly adapting behavioral responses to a changing context and internal state. Examples of behavioral flexibility include faster stimulus responses when attentive and slower responses when distracted. Contextual or state-dependent modulations may occur early in the cortical hierarchy and may be implemented via top-down projections from cortico-cortical or neuromodulatory pathways. However, the computational mechanisms mediating the effects of such projections are not known. Here, we introduce a theoretical framework to classify the effects of cell-type specific top-down perturbations on the information processing speed of cortical circuits. Our theory demonstrates that perturbation effects on stimulus processing can be predicted by intrinsic gain modulation, which controls the timescale of the circuit dynamics. Our theory leads to counter-intuitive effects such as improved performance with increased input variance. We tested the model predictions using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where we found that a decrease in single-cell intrinsic gain during locomotion led to an acceleration of visual processing. Our results establish a novel theory of cell-type specific perturbations, applicable to top-down modulation as well as optogenetic and pharmacological manipulations. Our theory links connectivity, dynamics, and information processing via gain modulation.
What If Memory Information is Stored Inside the Neuron, Instead of in the Synapse?
Authors: James Tee, Desmond P. Taylor
Date: 2021-01-24 00:00:00

Memory information in the brain is commonly believed to be stored in the synapse. However, a recent groundbreaking electrophysiology research has raised the possibility that memory information may actually be stored inside the neuron itself. Drawing on information theory and communications system engineering perspectives, we examine the problem of how memory information might be transmitted reliably between neurons. We identify 2 types of errors that affect neuronal communications (i.e., channel error and source error), along with plausible error mitigation solutions. We confirm the feasibility of these solutions using simulations. Four alternative hypotheses of the synapse's function are also proposed. We conclude by highlighting some research directions, along with potential areas of application.
A symbolic information approach to characterize response-related differences in cortical activity during a Go/No-Go task
Authors: Helena B. Lucas, Steven L. Bressler, Fernanda S. Matias, Osvaldo A. Rosso
Date: 2021-01-22 00:00:00
Tasks: Time Series, Time Series Analysis

How the brain processes information from external stimuli in order to perceive the world and act on it is one of the greatest questions in neuroscience. To address this question different time series analyzes techniques have been employed to characterize the statistical properties of brain signals during cognitive tasks. Typically response-specific processes are addressed by comparing the time course of average event-related potentials in different trials type. Here we analyze monkey Local Field Potentials data during visual pattern discrimination called Go/No-Go task in the light of information theory quantifiers. We show that the Bandt-Pompe symbolization methodology to calculate entropy and complexity of data is a useful tool to distinguish response-related differences between Go and No-Go trials. We propose to use an asymmetry index to statistically validate trial type differences. Moreover, by using the multi-scale approach and embedding time delays to downsample the data we can estimate the important time scales in which the relevant information is been processed.
Library-based Fast Algorithm for Simulating the Hodgkin-Huxley Neuronal Networks
Authors: Zhong-Qi Kyle Tian, Douglas Zhou
Date: 2021-01-17 00:00:00

We present a modified library-based method for simulating the Hodgkin-Huxley (HH) neuronal networks. By pre-computing a high resolution data library during the interval of an action potential (spike), we can avoid evolving the HH equations during the spike and can use a large time step to raise efficiency. The library method can stably achieve at most 10 times of speedup compared with the regular Runge-Kutta method while capturing most statistical properties of HH neurons like the distribution of spikes which data is widely used in the statistical analysis like transfer entropy and Granger causality. The idea of library method can be easily and successfully applied to other HH-type models like the most prominent \textquotedblleft regular spiking\textquotedblright , \textquotedblleft fast spiking\textquotedblright , \textquotedblleft intrinsically bursting\textquotedblright{} and \textquotedblleft low-threshold spike\textquotedblright{} types of HH models.
Design Fast Algorithms For Hodgkin-Huxley Neuronal Networks
Authors: Zhong-Qi Kyle Tian, Douglas Zhou
Date: 2021-01-17 00:00:00

The stiffness of the Hodgkin-Huxley (HH) equations during an action potential (spike) limits the use of large time steps. We observe that the neurons can be evolved independently between spikes, $i.e.,$ different neurons can be evolved with different methods and different time steps. This observation motivates us to design fast algorithms to raise efficiency. We present an adaptive method, an exponential time differencing (ETD) method and a library-based method to deal with the stiff period. All the methods can use time steps one order of magnitude larger than the regular Runge-Kutta methods to raise efficiency while achieving precise statistical properties of the original HH neurons like the largest Lyapunov exponent and mean firing rate. We point out that the ETD and library methods can stably achieve maximum 8 and 10 times of speedup, respectively.
Perceptual spaces and their symmetries: The geometry of color space
Authors: Nicolás Vattuone, Thomas Wachtler, Inés Samengo
Date: 2020-10-01 00:00:00
Tasks: Perceptual Distance

Our sensory systems transform external signals into neural activity, thereby producing percepts. We are endowed with an intuitive notion of similarity between percepts, that need not reflect the proximity of the physical properties of the corresponding external stimuli. The quantitative characterization of the geometry of percepts is therefore an endeavour that must be accomplished behaviorally. Here we characterized the geometry of color space using discrimination and matching experiments. We proposed an individually tailored metric defined in terms of the minimal chromatic difference required for each observer to differentiate a stimulus from its surround. Next, we showed that this perceptual metric was particularly adequate to describe two additional experiments, since it revealed the natural symmetry of perceptual computations. In one of the experiments, observers were required to discriminate two stimuli surrounded by a chromaticity that differed from that of the tested stimuli. In the perceptual coordinates, the change in discrimination thresholds induced by the surround followed a simple law that only depended on the perceptual distance between the surround and each of the two compared stimuli. In the other experiment, subjects were asked to match the color of two stimuli surrounded by two different chromaticities. Again, in the perceptual coordinates the induction effect produced by surrounds followed a simple, symmetric law. We conclude that the individually-tailored notion of perceptual distance reveals the symmetry of the laws governing perceptual computations.
Interactions of SARS-CoV-2 spike protein and transient receptor potential (TRP) cation channels could explain smell, taste, and/or chemesthesis disorders
Authors: Halim Maaroufi
Date: 2021-01-15 00:00:00

A significant subset of patients infected by SARS-CoV-2 presents olfactory, taste, and/or chemesthesis (OTC) disorders (OTCD). These patients recover rapidly, eliminating damage of sensory nerves. Discovering that S protein contains two ankyrin repeat binding motifs (S-ARBMs) and some TRP cation channels, implicated in OTC, have ankyrin repeat domains (TRPs-ARDs), I hypothesized that interaction of S-ARBMs and TRPs-ARDs could dysregulate the function of the latter and thus explains OTCD. Of note, some TRPs-ARDs are expressed in the olfactory epithelium, taste buds, trigeminal neurons in the oronasal cavity and vagal neurons in the trachea/lungs. Furthermore, this hypothesis is supported by studies that have shown: (i) respiratory viruses interact with TRPA1 and TRPV1 on sensory nerves and epithelial cells in the airways, (ii) the respiratory pathophysiology in COVID-19 patients is similar to lungs injuries produced by the sensitization of TRPV1 and TRPV4, and (iii) resolvin D1 and D2 shown to reduce SARS-CoV-2-induced inflammation, directly inhibit TRPA1, TRPV1, TRPV3 and TRPV4. Herein, results of blind dockings of S-ARBMs, 408-RQIAPG-413 (in RBD but distal from the ACE-2 binding region) and 905-RFNGIG-910 (in HR1), into TRPA1, TRPV1 and TRPV4 suggest that S-ARBMs interact with ankyrin repeat 6 of TRPA1 near an active site, and ankyrin repeat 3-4 of TRPV1 near cysteine 258 supposed to be implicated in the formation of inter-subunits disulfide bond. These findings suggest that S-ARBMs affect TRPA1, TRPV1 and TRPV4 function by interfering with channel assembly and trafficking. After an experimental confirmation of these interactions, among possible preventive treatments against COVID-19, the use of pharmacological manipulation (probably inhibition) of TRPs-ARDs to control or mitigate sustained pro-inflammatory response.
Mean-field approximations of networks of spiking neurons with short-term synaptic plasticity
Authors: Richard Gast, Thomas R. Knösche, Helmut Schmidt
Date: 2021-01-15 00:00:00
Tasks: Open-Ended Question Answering

Low-dimensional descriptions of neural network dynamics are an effective tool for bridging different scales of organization of brain structure and function. Recent advances in deriving mean-field descriptions for networks of coupled oscillators have sparked the development of a new generation of neural mass models. Of notable interest are mean-field descriptions of all-to-all coupled quadratic integrate-and-fire (QIF) neurons, which have already seen numerous extensions and applications. These extensions include different forms of short-term adaptation (STA) considered to play an important role in generating and sustaining dynamic regimes of interest in the brain. It is an open question, however, whether the incorporation of pre-synaptic forms of synaptic plasticity driven by single neuron activity would still permit the derivation of mean-field equations using the same method. Here, we discuss this problem using an established model of short-term synaptic plasticity at the single neuron level, for which we present two different approaches for the derivation of the mean-field equations. We compare these models with a recently proposed mean-field approximation that assumes stochastic spike timings. In general, the latter fails to accurately reproduce the macroscopic activity in networks of deterministic QIF neurons with distributed parameters. We show that the mean-field models we propose provide a more accurate description of the network dynamics, although they are mathematically more involved. Using bifurcation analysis, we find that QIF networks with pre-synaptic short-term plasticity can express regimes of periodic bursting activity as well as bi-stable regimes. Together, we provide novel insight into the macroscopic effects of short-term synaptic plasticity in spiking neural networks, as well as two different mean-field descriptions for future investigations of such networks.
The NonHuman Primate Neuroimaging & Neuroanatomy Project
Authors: Takuya Hayashi, Yujie Hou, Matthew F Glasser, Joonas A Autio, Kenneth Knoblauch, Miho Inoue-Murayama, Tim Coalson, Essa Yacoub, Stephen Smith, Henry Kennedy, David C Van Essen
Date: 2020-10-01 00:00:00
Tasks: Diffusion  MRI

Multi-modal neuroimaging projects are advancing our understanding of human brain architecture, function, connectivity using high-quality non-invasive data from many subjects. However, ground truth validation of connectivity using invasive tracers is not feasible in humans. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior.
Conex-Connect: Learning Patterns in Extremal Brain Connectivity From Multi-Channel EEG Data
Authors: Matheus B. Guerrero, Raphaël Huser, Hernando Ombao
Date: 2021-01-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Epilepsy is a chronic neurological disorder affecting more than 50 million people globally. An epileptic seizure acts like a temporary shock to the neuronal system, disrupting normal electrical activity in the brain. Epilepsy is frequently diagnosed with electroencephalograms (EEGs). Current methods study the time-varying spectra and coherence but do not directly model changes in extreme behavior. Thus, we propose a new approach to characterize brain connectivity based on the joint tail behavior of the EEGs. Our proposed method, the conditional extremal dependence for brain connectivity (Conex-Connect), is a pioneering approach that links the association between extreme values of higher oscillations at a reference channel with the other brain network channels. Using the Conex-Connect method, we discover changes in the extremal dependence driven by the activity at the foci of the epileptic seizure. Our model-based approach reveals that, pre-seizure, the dependence is notably stable for all channels when conditioning on extreme values of the focal seizure area. Post-seizure, by contrast, the dependence between channels is weaker, and dependence patterns are more "chaotic". Moreover, in terms of spectral decomposition, we find that high values of the high-frequency Gamma-band are the most relevant features to explain the conditional extremal dependence of brain connectivity.
Is Brain-Mind Quantum? A theory and supporting evidence
Authors: Stuart Kauffman, Dean Radin
Date: 2021-01-03 00:00:00

We propose a non-substance dualism theory, following Heisenberg: The world consists of both ontologically real possibilities that do not obey Aristotle's Law of the Excluded Middle, and ontologically real Actuals that do o0bey the Law of the Excluded Middle. This quantum approach solves five issues in quantum mechanics and numerous puzzles about the mind-brain relationship. It raises the possibility that some aspects of mind are non-local, and that mind plays an active role in the physical world. We present supporting evidence.
The growing topology of the C. elegans connectome
Authors: Alec Helm, Ann S. Blevins, Danielle S. Bassett
Date: 2020-12-31 00:00:00

Probing the developing neural circuitry in Caenorhabditis elegans has enhanced our understanding of nervous systems. The C. elegans connectome, like those of other species, is characterized by a rich club of densely connected neurons embedded within a small-world architecture. This organization of neuronal connections, captured by quantitative network statistics, provides insight into the system's capacity to perform integrative computations. Yet these network measures are limited in their ability to detect weakly connected motifs, such as topological cavities, that may support the systems capacity to perform segregated computations. We address this limitation by using persistent homology to track the evolution of topological cavities in the growing C. elegans connectome throughout neural development, and assess the degree to which the growing connectomes topology is resistant to biological noise. We show that the developing connectome topology is both relatively robust to changes in neuron birth times and not captured by similar growth models. Additionally, we quantify the consequence of a neurons specific birth time and ask if this metric tracks other biological properties of neurons. Our results suggest that the connectomes growing topology is a robust feature of the developing connectome that is distinct from other network properties, and that the growing topology is particularly sensitive to the exact birth times of a small set of predominantly motor neurons. By utilizing novel measurements that track biological features, we anticipate that our study will be helpful in the construction of more accurate models of neuronal development in C. elegans
Correlation Across Environments Encoded by Hippocampal Place Cells
Authors: Bhav Jain, Sean Elliott
Date: 2020-12-29 00:00:00
Tasks: Hippocampus

The hippocampus is often attributed to episodic memory formation and storage in the mammalian brain; in particular, Alme et al. showed that hippocampal area CA3 forms statistically independent representations across a large number of environments, even if the environments share highly similar features. This lack of overlap between spatial maps indicates the large capacity of the CA3 circuitry. In this paper, we support the argument for the large capacity of the CA3 network. To do so, we replicate the key findings of Alme et al. and extend the results by perturbing the neural activity encodings with noise and conducting representation similarity analysis (RSA). We find that the correlations between firing rates are partially resistant to noise, and that the spatial representations across cells show similar patterns, even across different environments. Finally, we discuss some theoretical and practical implications of our results.
Occipital and left temporal EEG correlates of phenomenal consciousness
Authors: Vitor Manuel Dinis Pereira
Date: 2017-11-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Experimental Design

In the first section, Introduction, we present our experimental design. In the second section, we characterize the grand average occipital and temporal electrical activity correlated with a contrast in access. In the third section, we characterize the grand average occipital and temporal electrical activity correlated with a contrast in phenomenology and conclude characterizing the grand average occipital and temporal electrical activity co-occurring with unconsciousness.