pwc-archive/papers-with-abstracts huggingface dataset:
Vector-based Pedestrian Navigation in Cities
Authors: Christian Bongiorno, Yulun Zhou, Marta Kryven, David Theurel, Alessandro Rizzo, Paolo Santi, Joshua Tenenbaum, Carlo Ratti
Date: 2021-03-12 00:00:00

How do pedestrians choose their paths within city street networks? Researchers have tried to shed light on this matter through strictly controlled experiments, but an ultimate answer based on real-world mobility data is still lacking. Here, we analyze salient features of human path planning through a statistical analysis of a massive dataset of GPS traces, which reveals that (1) people increasingly deviate from the shortest path when the distance between origin and destination increases, and (2) chosen paths are statistically different when origin and destination are swapped. We posit that direction to goal is a main driver of path planning and develop a vector-based navigation model that is a statistically better predictor of human paths than a model based on minimizing distance with stochastic effects. Our findings generalize across two major US cities with different street networks, hinting to the fact that vector-based navigation might be a universal property of human path planning.
Graph Theory in Brain Networks
Authors: Moo K. Chung
Date: 2021-03-09 00:00:00

Recent developments in graph theoretic analysis of complex networks have led to deeper understanding of brain networks. Many complex networks show similar macroscopic behaviors despite differences in the microscopic details. Probably two most often observed characteristics of complex networks are scale-free and small-world properties. In this paper, we will explore whether brain networks follow scale-free and small-worldness among other graph theory properties.
Optogenetically Induced Spatiotemporal Gamma Oscillations in Visual Cortex
Authors: Fereshteh Arab, Sareh Rostami, Mohammad Dehghani-Habibabadi, Vahid Salari, Mir-Shahram Safari
Date: 2021-03-08 00:00:00

It has been hypothesized that Gamma cortical oscillations play important roles in numerous cognitive processes and may involve psychiatric conditions including anxiety, schizophrenia, and autism. Gamma rhythms are commonly observed in many brain regions during both waking and sleep states, yet their functions and mechanisms remain a matter of debate. Spatiotemporal Gamma oscillations can explain neuronal representation, computation, and the shaping of communication among cortical neurons, even neurological and neuropsychiatric disorders in neo-cortex. In this study, the neural network dynamics and spatiotemporal behavior in the cerebral cortex are examined during Gamma brain activity. We have directly observed the Gamma oscillations on visual processing as spatiotemporal waves induced by targeted optogenetics stimulation. We have experimentally demonstrated the constant optogenetics stimulation based on the ChR2 opsin under the control of the CaMKII{\alpha} promotor, which can induce sustained narrowband Gamma oscillations in the visual cortex of rats during their comatose states. The injections of the viral vector [LentiVirus CaMKII{\alpha} ChR2] was performed at two different depths, 200 and 500 \mu m. Finally, we computationally analyze our results via Wilson-Cowan model.
Prefrontal cortex functional connectivity based on simultaneous record of electrical and hemodynamic responses associated with mental stress
Authors: Fares Al-Shargie
Date: 2021-03-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Functional Connectivity

This paper investigates prefrontal cortex (PFC) functional connectivity based on synchronized electrical and hemodynamic responses associated with mental stress. The electrical response was based on alpha rhythmic of Electroencephalography (EEG) signals and the hemodynamic responses were based on the mean concentrations of oxygenated and deoxygenated hemoglobin measured using functional Near-Infrared Spectroscopy (fNIRS). The aim is to explore the effects of stress on the inter and intra hemispheric PFC functional connectivity at narrow and wide frequency bands with 8- 13 Hz in EEG and 0.009-0.1Hz in fNIRS signals. The results demonstrated significantly reduce in the functional connectivity on the dorsolateral PFC within the inter and intra hemispheric PFC areas based in EEG alpha rhythmic and fNIRS oxygenated and deoxygenated hemoglobin. The statistical analysis further demonstrated right dorsolateral dominant to mental stress.
Seven Survival Senses: Evolutionary Training makes Discerning Differences more Natural than Spotting Similarities
Authors: Ravi Kashyap
Date: 2021-03-08 00:00:00
Tasks: Attribute

We discuss preliminary results from two experiments and put forth the notion that the development of sensory systems might be more geared towards discerning differences rather than for spotting similarities. We present the possibility that the necessity to spot differences might have evolved to ensure the survival of the organism, which suggests numerous other experiments to assess the response of participants to various stimuli. We consider our present state of affairs, wherein the need is to thrive and not merely survive, which requires us to spot similarities around us. We provide some suggestions on how this attribute can be developed, which includes mathematical education. We conclude with an alternate measure for intelligence, termed the Involvement Quotient (also, IQ), which gauges the level of involvement of the sense organs to whatever is happening around the individual.
Analyzing the Spatiotemporal Interaction and Propagation of ATN Biomarkers in Alzheimer's Disease using Longitudinal Neuroimaging Data
Authors: Qing Liu, Defu Yang, Jingwen Zhang, Ziming Wei, Guorong Wu, Minghan Chen
Date: 2021-03-07 00:00:00
Tasks: valid

Three major biomarkers: beta-amyloid (A), pathologic tau (T), and neurodegeneration (N), are recognized as valid proxies for neuropathologic changes of Alzheimer's disease. While there are extensive studies on cerebrospinal fluids biomarkers (amyloid, tau), the spatial propagation pattern across brain is missing and their interactive mechanisms with neurodegeneration are still unclear. To this end, we aim to analyze the spatiotemporal associations between ATN biomarkers using large-scale neuroimaging data. We first investigate the temporal appearances of amyloid plaques, tau tangles, and neuronal loss by modeling the longitudinal transition trajectories. Second, we propose linear mixed-effects models to quantify the pathological interactions and propagation of ATN biomarkers at each brain region. Our analysis of the current data shows that there exists a temporal latency in the build-up of amyloid to the onset of tau pathology and neurodegeneration. The propagation pattern of amyloid can be characterized by its diffusion along the topological brain network. Our models provide sufficient evidence that the progression of pathological tau and neurodegeneration share a strong regional association, which is different from amyloid.
Electromyography biofeedback system with visual and vibratory feedbacks designed for lower limb rehabilitation
Authors: João Vitor da Silva Moreira, Karina A. Rodrigues, Daniel José L. L. Pinheiro, Thaís C. Santos, João Luiz Vieira, Esper A. Cavalheiro, Jean Faber
Date: 2021-03-06 00:00:00

One of the main causes of long-term prosthetic abandonment is the lack of ownership over the prosthesis, caused mainly by the absence of sensory information regarding the lost limb. One strategy to overcome this problem is to provide alternative feedback mechanisms to convey information respective to the absent limb. To address this issue, we developed a Biofeedback system for the rehabilitation of transfemoral amputees, controlled via electromyographic activity from the leg muscles, that can provide real-time visual and/or vibratory feedback for the user. In this study, we tested this device with able-bodied individuals performing an adapted version of the clinical protocol. Our idea was to test the effectiveness of combining vibratory and visual feedbacks and how task difficulty affects overall performance. Our results show no negative interference combining both feedback modalities, and that performance peaked at the intermediate difficulty. These results provide powerful insights of what can be expected with the population of amputee people and will help in the final steps of protocol development. Our goal is to use this biofeedback system to engage another sensory modality in the process of spatial representation of a virtual leg, bypassing the lack of information associated with the disruption of afferent pathways following amputation.
Visual Modulation of Human Responses to Support Surface Translation
Authors: Mustafa Emre Akçay, Vittorio Lippi, Thomas Mergner
Date: 2021-03-05 00:00:00
Tasks: Position, Translation

Vision is known to improve human postural responses to external perturbations. This study investigates the role of vision for the responses to continuous pseudorandom support surface translations in the body sagittal plane in three visual conditions: with the eyes closed (EC), in stroboscopic illumination (EO/SI; only visual position information) and with eyes open in continuous illumination (EO/CI; position and velocity information) with the room as static visual scene (or the interior of a moving cabin, in some of the trials). In the frequency spectrum of the translation stimulus we distinguished on the basis of the response patterns between a low-frequency, mid-frequency, and high-frequency range (LFR: 0.0165-0.14 Hz; MFR: 0.15-0.57 Hz; HFR: 0.58-2.46 Hz). With EC, subjects' mean sway response gain was very low in the LFR. On average it increased with EO/SI (although not to a significant degree p = 0.078) and more so with EO/CI (p < 10<sup>-6</sup>). In contrast, the average gain in the MFR decreased from EC to EO/SI (although not to a significant degree, p = 0.548) and further to EO/CI (p = 0.0002). In the HFR, all three visual conditions produced, similarly, high gain levels. A single inverted pendulum (SIP) model controlling center of mass (COM) balancing about the ankle joints formally described the EC response as being strongly shaped by a resonance phenomenon arising primarily from the control's proprioceptive feedback loop. The effect of adding visual information in these simulations lies in a reduction of the resonance, similar as in the experiments. Extending the model to a double inverted pendulum (DIP) suggested in addition a biomechanical damping effective from trunk sway in the hip joints on the resonance.
To Deconvolve, or Not to Deconvolve: Inferences of Neuronal Activities using Calcium Imaging Data
Authors: Tong Shen, Gyorgy Lur, Xiangmin Xu, Zhaoxia Yu
Date: 2021-03-03 00:00:00
Tasks: Clustering

With the increasing popularity of calcium imaging data in neuroscience research, methods for analyzing calcium trace data are critical to address various questions. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step. In this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding. Our simulations and applications to real data suggest that the estimated spike data outperform calcium trace data for both clustering and PCA. Although calcium trace data show higher predictability than spike data at each time point, spike history or cumulative spike counts is comparable to or better than calcium traces in population decoding.
Microwave amplitude reflecting instability of LFP electrode ground field is useful for consciousness state identification
Authors: Chika Koyama, Taichi Haruna, Kazuto Yamashita
Date: 2021-03-01 00:00:00

We recently developed original electroencephalogram indices focusing on microwaves of flattish period (named $\tau$), which correlated with volatile anesthesia concentration in dogs. However, this mechanism remains unclear. $\tau$ was defined as a subthreshold wave and burst wave was defined as an above-threshold wave. This study shows that these indices well quantified the morphological features of local field potential waveforms in mice, and made it possible to discriminate the specific waveforms of the state of consciousness; awake, shallow sleep, rapid eye movement sleep, and non-rapid eye movement sleep. In addition, examination of $\tau$ suggested that microwaves are local fluctuations of the electrode that can be formed for each data sampling, and that its amplitude may increase with the degree of arousal.
Update on the multimodal pathophysiological dataset of gradual cerebral ischemia in a cohort of juvenile pigs: auditory, sensory and high-frequency sensory evoked potentials
Authors: Martin G. Frasch, Bernd Walter, Christoph Anders, Reinhard Bauer
Date: 2021-02-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We expand from a spontaneous to an evoked potentials (EP) data set of brain electrical activities as electrocorticogram (ECoG) and electrothalamogram (EThG) in juvenile pig under various sedation, ischemia and recovery states. This EP data set includes three stimulation paradigms: auditory (AEP, 40 and 2000 Hz), sensory (SEP, left and right maxillary nerve) and high-frequency oscillations (HFO) SEP. This permits derivation of electroencephalogram (EEG) biomarkers of corticothalamic communication under these conditions. The data set is presented in full band sampled at 2000 Hz. We provide technical validation of the evoked responses for the states of sedation, ischemia and recovery. This extended data set now permits mutual inferences between spontaneous and evoked activities across the recorded modalities. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries.
A Technical Critique of Some Parts of the Free Energy Principle
Authors: Martin Biehl, Felix A. Pollock, Ryota Kanai
Date: 2020-01-12 00:00:00
Tasks: Bayesian Inference, LEMMA

We summarize the original formulation of the free energy principle, and highlight some technical issues. We discuss how these issues affect related results involving generalised coordinates and, where appropriate, mention consequences for and reveal, up to now unacknowledged, differences to newer formulations of the free energy principle. In particular, we reveal that various definitions of the "Markov blanket" proposed in different works are not equivalent. We show that crucial steps in the free energy argument which involve rewriting the equations of motion of systems with Markov blankets, are not generally correct without additional (previously unstated) assumptions. We prove by counterexample that the original free energy lemma, when taken at face value, is wrong. We show further that this free energy lemma, when it does hold, implies equality of variational density and ergodic conditional density. The interpretation in terms of Bayesian inference hinges on this point, and we hence conclude that it is not sufficiently justified. Additionally, we highlight that the variational densities presented in newer formulations of the free energy principle and lemma are parameterised by different variables than in older works, leading to a substantially different interpretation of the theory. Note that we only highlight some specific problems in the discussed publications. These problems do not rule out conclusively that the general ideas behind the free energy principle are worth pursuing.
Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map
Authors: Birgitta Dresp-Langley, John M. Wandeto
Date: 2021-02-27 00:00:00
Tasks: Quantization

Symmetry in biological and physical systems is a product of self organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry based feature extrac-tion or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self Organizing Map, the Quantization Error (SOM QE). Shape pairs with perfect geometric mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within or across the shapes in a given pair produce, as shown here, longer choice RT for yes responses relative to symmetry. These data are consistently mirrored by the variations in the SOM QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metrics proven selectivity to local contrast and color variations in large and highly complex image data.
Structural Characterization of Oscillations in Brain Networks with Rate Dynamics
Authors: Erfan Nozari, Robert Planas, Jorge Cortes
Date: 2021-02-27 00:00:00

Among the versatile forms of dynamical patterns of activity exhibited by the brain, oscillations are one of the most salient and extensively studied, yet are still far from being well understood. In this paper, we provide various structural characterizations of the existence of oscillatory behavior in neural networks using a classical neural mass model of mesoscale brain activity called linear-threshold dynamics. Exploiting the switched-affine nature of this dynamics, we obtain various necessary and/or sufficient conditions on the network structure and its external input for the existence of oscillations in (i) two-dimensional excitatory-inhibitory networks (E-I pairs), (ii) networks with one inhibitory but arbitrary number of excitatory nodes, (iii) purely inhibitory networks with an arbitrary number of nodes, and (iv) networks of E-I pairs. Throughout our treatment, and given the arbitrary dimensionality of the considered dynamics, we rely on the lack of stable equilibria as a system-based proxy for the existence of oscillations, and provide extensive numerical results to support its tight relationship with the more standard, signal-based definition of oscillations in computational neuroscience.
About solving the Fechner-Stevens problem
Authors: Vasily Romanchak
Date: 2021-02-26 00:00:00

In this paper, we prove that the Fechner and Stevens laws are equivalent (coincide up to isomorphism). Therefore, the problem does not exist.
Ubiquitous proximity to a critical state for collective neural activity in the CA1 region of freely moving mice
Authors: Yi-Ling Chen, Chun-Chung Chen, Yu-Ying Mei, Ning Zhou, Dongchuan Wu, Ting-Kuo Lee
Date: 2021-02-26 00:00:00
Tasks: Hippocampus

Using miniscope recordings of calcium fluorescence signals in the CA1 region of the hippocampus of mice, we monitor the neural activity of hippocampal regions while the animals are freely moving in an open chamber. Using a data-driven statistical modeling approach, the statistical properties of the recorded data are mapped to spin-glass models with pairwise interactions. Considering the parameter space of the model, the observed system is generally near a critical state between two distinct phases. The close proximity to the criticality is found to be robust against different ways of sampling and segmentation of the measured data. By independently altering the coupling distribution and the network structure of the statistical model, the network structures are found to be vital to maintain the proximity to the critical state. We further find the observed assignment of the coupling strengths makes the net coupling at each site more balanced with slight variation, which likely helps the maintenance of the critical state. Network analysis on the connectivity obtained by thresholding the coupling strengths find the connectivity of the networks to be well described by a random network model. These results are consistent across different experiments, sampling and segmentation choices in our analysis.
Overfitting the literature to one set of stimuli and data
Authors: Tijl Grootswagers, Amanda K Robinson
Date: 2021-02-19 00:00:00

The fast-growing field of Computational Cognitive Neuroscience is on track to meet its first crisis. A large number of papers in this nascent field are developing and testing novel analysis methods using the same stimuli and neuroimaging datasets. Publication bias and confirmatory exploration will result in overfitting to the limited available data. The field urgently needs to collect more good quality open neuroimaging data using a variety of experimental stimuli, to test the generalisability of current published results, and allow for more robust results in future work.
A Robotic Model of Hippocampal Reverse Replay for Reinforcement Learning
Authors: Matthew T. Whelan, Tony J. Prescott, Eleni Vasilaki
Date: 2021-02-23 00:00:00
Tasks: Hippocampus, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Hippocampal reverse replay is thought to contribute to learning, and particularly reinforcement learning, in animals. We present a computational model of learning in the hippocampus that builds on a previous model of the hippocampal-striatal network viewed as implementing a three-factor reinforcement learning rule. To augment this model with hippocampal reverse replay, a novel policy gradient learning rule is derived that associates place cell activity with responses in cells representing actions. This new model is evaluated using a simulated robot spatial navigation task inspired by the Morris water maze. Results show that reverse replay can accelerate learning from reinforcement, whilst improving stability and robustness over multiple trials. As implied by the neurobiological data, our study implies that reverse replay can make a significant positive contribution to reinforcement learning, although learning that is less efficient and less stable is possible in its absence. We conclude that reverse replay may enhance reinforcement learning in the mammalian hippocampal-striatal system rather than provide its core mechanism.