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What does it mean to understand a neural network?Jul 15 2019We can define a neural network that can learn to recognize objects in less than 100 lines of code. However, after training, it is characterized by millions of weights that contain the knowledge about many object types across visual scenes. Such networks ... More
Autoencoding sensory substitutionJul 14 2019Tens of millions of people live blind, and their number is ever increasing. Visual-to-auditory sensory substitution (SS) encompasses a family of cheap, generic solutions to assist the visually impaired by conveying visual information through sound. The ... More
Signal Conditioning for Learning in the WildJul 12 2019The mammalian olfactory system learns rapidly from very few examples, presented in unpredictable online sequences, and then recognizes these learned odors under conditions of substantial interference without exhibiting catastrophic forgetting. We have ... More
Cortical Surface Parcellation using Spherical Convolutional Neural NetworksJul 11 2019We present cortical surface parcellation using spherical deep convolutional neural networks. Traditional multi-atlas cortical surface parcellation requires inter-subject surface registration using geometric features with high processing time on a single ... More
From Single Neurons to Behavior in the Jellyfish Aurelia auritaJul 11 2019Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earliest neuron-bearing, actively-swimming animals. Here we develop the first ... More
Modeling the relationship between regional activation and functional connectivity during wakefulness and sleepJul 09 2019Global brain activity self-organizes into discrete patterns characterized by distinct behavioral observables and modes of information processing. The human thalamocortical system is a densely connected network where local neural activation reciprocally ... More
Functional Brain Networks Discovery Using Dictionary Learning with Correlated SparsityJul 09 2019Functional Magnetic Resonance Imaging (fMRI) helps constructing functional brain networks by using brain activity information. Principal component analysis (PCA) and independent component analysis (ICA) are widely used methods to generate functional brain ... More
Brain network organization as the computational architecture of cognitionJul 08 2019Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. We review recent advances in linking (empirical and simulated) brain ... More
A family of closed-form solutions for two-dimensional correlated diffusion processesJul 07 2019Bounded diffusion processes are models of transport phenomena with wide applicability across many fields. These processes are described by their probability density functions (PDFs), which often obey Fokker-Planck equations (FPEs). While obtaining analytical ... More
Models of ConsciousnessJul 07 2019The scientific study of consciousness is a new field which has emerged as a response to groundbreaking developments in neuroscience, cognitive psychology and analytic philosophy. Its aim is to develop a scientific account, formulated in terms of formal ... More
Topological Information Data AnalysisJul 06 2019This paper presents methods that quantify the structure of statistical interactions within a given data set, and was first used in \cite{Tapia2018}. It establishes new results on the k-multivariate mutual-informations (I_k) inspired by the topological ... More
An Approximate Bayesian Approach to Surprise-Based LearningJul 05 2019Surprise-based learning allows agents to adapt quickly in non-stationary stochastic environments. Most existing approaches to surprise-based learning and change point detection assume either implicitly or explicitly a simple, hierarchical generative model ... More
A Unified Framework of Online Learning Algorithms for Training Recurrent Neural NetworksJul 05 2019We present a framework for compactly summarizing many recent results in efficient and/or biologically plausible online training of recurrent neural networks (RNN). The framework organizes algorithms according to several criteria: (a) past vs. future facing, ... More
A Cognition-Affect Integrated Model of EmotionJul 04 2019What is an emotion? an old riddle repeatedly being attempted with advance modern tools and understanding of the age. With the new advancement old theories are tested and with correction new is formed. Such is the case with defining emotion broadly shifting ... More
Evaluation of the criticality of in vitro neuronal networks: Toward an assessment of computational capacityJul 04 2019Novel computing hardwares are necessary to keep up with today's increasing demand for data storage and processing power. In this research project, we turn to the brain for inspiration to develop novel computing substrates that are self-learning, scalable, ... More
infotheory: A C++/Python package for multivariate information theoretic analysisJul 04 2019This paper introduces \texttt{infotheory}: a package that implements multivariate information theoretic measures for discrete and continuous data. This package implements widely used measures such as entropy and mutual information, as well as more recent ... More
infotheory: A C++/Python package for multivariate information theoretic analysisJul 04 2019Jul 08 2019This paper introduces \texttt{infotheory}: a package written in C++ and usable from Python and C++, for multivariate information theoretic analyses of discrete and continuous data. This package allows the user to study the relationship between components ... More
Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelinationJul 04 2019The intra-axonal water exchange time {\tau}i, a parameter associated with axonal permeability, could be an important biomarker for understanding demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI is sensitive to changes in permeability, ... More
Multiplicative modulations in hue-selective cells enhance unique hue representationJul 03 2019There is still much to understand about the color processing mechanisms in the brain and the transformation from cone-opponent representations to perceptual hues. Moreover, it is unclear which areas(s) in the brain represent unique hues. We propose a ... More
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRIJul 03 2019Reconstructing observed images from fMRI brain recordings is challenging. Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i.e., images with their corresponding fMRI responses) to span the huge space of natural images is prohibitive ... More
Effect of assistive method on the sense of fulfillment with agency: Modeling with flow and attribution theoryJul 03 2019Several assistive technologies for users' operations have been recently developed. A user's sense of agency (SoA) decreases with increasing system assistance, possibly resulting in a decrease in the user's sense of fulfillment. This study aims to provide ... More
Quantitative evaluation of sense of discrepancy to operation response using event-related potentialJul 03 2019This study aimed to develop a method to evaluate the sense of discrepancy to the operation response quantitatively. We examined the availability of event-related potential (P300), which is considered to reflect attention to stimulation, to evaluate the ... More
Introducing Astrocytes on a Neuromorphic Processor: Synchronization, Local Plasticity and Edge of ChaosJul 02 2019While there is still a lot to learn about astrocytes and their neuromodulatory role associated with their spatial and temporal integration of synaptic activity, the introduction of an additional to neurons processing unit into neuromorphic hardware is ... More
Reverse engineering neural networks from many partial recordingsJul 02 2019Much of neuroscience aims at reverse engineering the brain, but we only record a small number of neurons at a time. We do not currently know if reverse engineering the brain requires us to simultaneously record most neurons or if multiple recordings from ... More
Mathematical Model of Emotional Habituation to Novelty: Modeling with Bayesian Update and Information TheoryJul 02 2019Novelty is an important factor of creativity in product design. Acceptance of novelty, however, depends on one's emotions. Yanagisawa, the last author, and his colleagues previously developed a mathematical model of emotional dimensions associated with ... More
Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in AutismJul 02 2019Deep learning methods are increasingly being used with neuroimaging data like structural and function magnetic resonance imaging (MRI) to predict the diagnosis of neuropsychiatric and neurological disorders. For psychiatric disorders in particular, it ... More
DeepTEGINN: Deep Learning Based Tools to Extract Graphs from Images of Neural NetworksJul 01 2019In the brain, the structure of a network of neurons defines how these neurons implement the computations that underlie the mind and the behavior of animals and humans. Provided that we can describe the network of neurons as a graph, we can employ methods ... More
Learning to aggregate feature representationsJul 01 2019Jul 04 2019The Algonauts challenge requires to construct a multi-subject encoder of images to brain activity. Deep networks such as ResNet-50 and AlexNet trained for image classification are known to produce feature representations along their intermediate stages ... More
Learning to aggregate feature representationsJul 01 2019Jul 03 2019The Algonauts challenge requires to construct an multi-subject encoder of images to brain activity. Deep networks such as ResNet-50 and AlexNet trained for image classification are known to produce feature representations along their intermediate stages ... More
Explaining the Human Visual Brain Challenge 2019 -- receptive fields and surrogate featuresJul 01 2019In this paper I review the submission to the Explaining the Human Visual Brain Challenge 2019 in both the fMRI and MEG tracks. The goal was to construct neural network features which generate the so-called representational dissimilarity matrix (RDM) which ... More
Dissimilarity learning via Siamese network predicts brain imaging dataJul 01 2019The advent of deep learning has a profound effect on visual neuroscience. It paved the way for new models to predict neural data. Although deep convolutional neural networks are explicitly trained for categorization, they learn a representation similar ... More
Mechanisms underlying the response of mouse cortical networks to optogenetic manipulationJul 01 2019GABAergic interneurons can be subdivided into three subclasses: parvalbumin positive (PV), somatostatin positive (SOM) and serotonin positive neurons. With principal cells (PCs) they form complex networks. We examine PCs and PV responses in mouse anterior ... More
Neural Dynamics Discovery via Gaussian Process Recurrent Neural NetworksJul 01 2019Latent dynamics discovery is challenging in extracting complex dynamics from high-dimensional noisy neural data. Many dimensionality reduction methods have been widely adopted to extract low-dimensional, smooth and time-evolving latent trajectories. However, ... More
Unsupervised predictive coding models may explain visual brain representationJun 30 2019Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations ... More
A Power Efficient Artificial Neuron Using Superconducting NanowiresJun 29 2019With the rising societal demand for more information-processing capacity with lower power consumption, alternative architectures inspired by the parallelism and robustness of the human brain have recently emerged as possible solutions. In particular, ... More
Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neuronsJun 29 2019Oscillations are a hallmark of neural population activity in various brain regions with a spectrum covering a wide range of frequencies. Within this spectrum gamma oscillations have received particular attention due to their ubiquitous nature and to their ... More
Modeling Response Time Distributions with Generalized Beta PrimeJun 28 2019We use Generalized Beta Prime distribution, also known as GB2, for fitting response time distributions. This distribution, characterized by one scale and three shape parameters, is incredibly flexible in that it can mimic behavior of many other distributions. ... More
Symphony of high-dimensional brainJun 27 2019This paper is the final part of the scientific discussion organised by the Journal "Physics of Life Rviews" about the simplicity revolution in neuroscience and AI. This discussion was initiated by the review paper "The unreasonable effectiveness of small ... More
Attentional Modulation of Visual Spatial Integration: Psychophysical Evidence Supported by Population Coding ModelingJun 27 2019Two prominent strategies that the human visual system uses to reduce incoming information are spatial integration and selective attention. Although spatial integration summarizes and combines information over the visual field, selective attention can ... More
The role of node dynamics in shaping emergent functional connectivity patterns in the brainJun 27 2019The contribution of structural connectivity to dynamic and often highly variable brain states remains poorly understood. We present a mathematical and computational study suited to assess the structure--function issue. We treat a system of Jansen--Rit ... More
Increasing signal amplitude in electrical impedance tomography of neural activity using a parallel resistor inductor capacitor (RLC) circuitJun 27 2019Objective: To increase the impedance signal amplitude produced during neural activity using a novel approach of implementing a parallel resistor inductor capacitor (RLC) circuit across the current source used in electrical impedance tomography (EIT) of ... More
Tactile Hallucinations on Artificial Skin Induced by Homeostasis in a Deep Boltzmann MachineJun 25 2019Jun 26 2019Perceptual hallucinations are present in neurological and psychiatric disorders and amputees. While the hallucinations can be drug-induced, it has been described that they can even be provoked in healthy subjects. Understanding their manifestation could ... More
Tactile Hallucinations on Artificial Skin Induced by Homeostasis in a Deep Boltzmann MachineJun 25 2019Perceptual hallucinations are present in neurological and psychiatric disorders and amputees. While the hallucinations can be drug-induced, it has been described that they can even be provoked in healthy subjects. Understanding their manifestation could ... More
A World unto Itself: Human Communication as Active InferenceJun 25 2019Work in developmental psychology suggests that humans are predisposed to align their mental states with other individuals. This manifests principally in cooperative communication, that is, intentional communication geared towards aligning mental states. ... More
Computing persistent homology of directed flag complexesJun 25 2019We present a new computing package Flagser, designed to construct the directed flag complex of a finite directed graph, and compute persistent homology for flexibly defined filtrations on the graph and the resulting complex. The persistent homology computation ... More
A free energy principle for a particular physicsJun 24 2019This monograph attempts a theory of every 'thing' that can be distinguished from other things in a statistical sense. The ensuing statistical independencies, mediated by Markov blankets, speak to a recursive composition of ensembles (of things) at increasingly ... More
A Review on Neural Network Models of Schizophrenia and Autism Spectrum DisorderJun 24 2019This survey presents the most relevant neural network models of autism spectrum disorder and schizophrenia, from the first connectionist models to recent deep network architectures. We analyzed and compared the most representative symptoms with its neural ... More
The BIDS Toolbox: A web service to manage brain imaging datasetsJun 24 2019Data sharing is a key factor for ensuring reproducibility and transparency of scientific experiments, and neuroimaging is no exception. The vast heterogeneity of data formats and imaging modalities utilised in the field makes it a very challenging problem. ... More
An interdisciplinary overview of developmental indices and behavioral measures of the minimal selfJun 24 2019Jul 02 2019In this review paper we discuss the development of the minimal self in humans, the behavioural measures indicating the presence of different aspects of the minimal self, namely, body ownership and sense of agency, and also discuss robotics research investigating ... More
Digital Multiplier-less Event-Driven Spiking Neural Network Architecture for Learning a Context-Dependent TaskJun 24 2019Neuromorphic engineers aim to develop event-based spiking neural networks (SNNs) in hardware. These SNNs closer resemble dynamics of biological neurons than todays' artificial neural networks and achieve higher efficiency thanks to the event-based, asynchronous ... More
Harnessing behavioral diversity to understand circuits for cognitionJun 23 2019With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we review emerging conceptual and technological advances that begin ... More
Neural networks with motivationJun 23 2019Motivational salience is a mechanism that determines an organism's current level of attraction to or repulsion from a particular object, event, or outcome. Motivational salience is described by modulating the reward by an externally controlled parameter ... More
A neurally plausible model learns successor representations in partially observable environmentsJun 22 2019Animals need to devise strategies to maximize returns while interacting with their environment based on incoming noisy sensory observations. Task-relevant states, such as the agent's location within an environment or the presence of a predator, are often ... More
Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor function, and diminished in schizophreniaJun 21 2019Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory entails brainwide switching between activity states. The stability of states relates to ... More
Information Flow Theory (IFT) of Biologic and Machine Consciousness: Implications for Artificial General Intelligence and the Technological SingularityJun 21 2019The subjective experience of consciousness is at once familiar and yet deeply mysterious. Strategies exploring the top-down mechanisms of conscious thought within the human brain have been unable to produce a generalized explanatory theory that scales ... More
Visualizing Representational Dynamics with Multidimensional Scaling AlignmentJun 21 2019Representational similarity analysis (RSA) has been shown to be an effective framework to characterize brain-activity profiles and deep neural network activations as representational geometry by computing the pairwise distances of the response patterns ... More
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental DisordersJun 21 2019Jun 28 2019Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward ... More
Split Q Learning: Reinforcement Learning with Two-Stream RewardsJun 21 2019Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward ... More
Derivation of the Variational Bayes EquationsJun 20 2019Jun 24 2019The derivation of key equations for the variational Bayes approach is well-known in certain circles. However, translating the fundamental derivations (e.g., as found in Beal (2003)) to the notation of Friston (2013, 2015) is somewhat delicate. Further, ... More
Derivation of the Variational Bayes EquationsJun 20 2019The derivation of key equations for the variational Bayes approach is well-known in certain circles. However, translating the fundamental derivations (e.g., as found in Beal (2003)) to the notation of Friston (2013, 2015) is somewhat delicate. Further, ... More
Derivation of the Variational Bayes EquationsJun 20 2019Jun 26 2019The derivation of key equations for the variational Bayes approach is well-known in certain circles. However, translating the fundamental derivations (e.g., as found in Beal (2003)) to the notation of Friston (2013, 2015) is somewhat delicate. Further, ... More
Low-dimensional Embodied Semantics for Music and LanguageJun 20 2019Embodied cognition states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according ... More
Multitaper Spectral Analysis of Neuronal Spiking Activity Driven by Latent Stationary ProcessesJun 20 2019Investigating the spectral properties of the neural covariates that underlie spiking activity is an important problem in systems neuroscience, as it allows to study the role of brain rhythms in cognitive functions. While the spectral estimation of continuous ... More
Extraction of hierarchical functional connectivity components in human brain using resting-state fMRIJun 19 2019The study of hierarchy in networks of the human brain has been of significant interest among the researchers as numerous studies have pointed out towards a functional hierarchical organization of the human brain. This paper provides a novel method for ... More
Extraction of hierarchical functional connectivity components in human brain using resting-state fMRIJun 19 2019Jun 27 2019The study of hierarchy in networks of the human brain has been of significant interest among the researchers as numerous studies have pointed out towards a functional hierarchical organization of the human brain. This paper provides a novel method for ... More
Divisive Normalization from Wilson-Cowan DynamicsJun 19 2019Divisive Normalization and the Wilson-Cowan equations are influential models of neural interaction and saturation [Carandini and Heeger Nat.Rev.Neurosci. 2012; Wilson and Cowan Kybernetik 1973]. However, they have not been analytically related yet. In ... More
Brain correlates of task-load and dementia elucidation with tensor machine learning using oddball BCI paradigmJun 19 2019Dementia in the elderly has recently become the most usual cause of cognitive decline. The proliferation of dementia cases in aging societies creates a remarkable economic as well as medical problems in many communities worldwide. A recently published ... More
TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral TherapyJun 18 2019We introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics related to the activity patterns and modular affiliations of different regions ... More
Cortical computations via metastable activityJun 18 2019Metastable brain dynamics are characterized by abrupt, jump-like modulations so that the neural activity in single trials appears to unfold as a sequence of discrete, quasi-stationary states. Evidence that cortical neural activity unfolds as a sequence ... More
Inferred successor maps for better transfer learningJun 18 2019Jul 02 2019Humans and animals show remarkable flexibility in adjusting their behaviour when their goals, or rewards in the environment change. While such flexibility is a hallmark of intelligent behaviour, these multi-task scenarios remain an important challenge ... More
Inferred successor maps for better transfer learningJun 18 2019Humans and animals show remarkable flexibility in adjusting their behaviour when their goals, or rewards in the environment change. While such flexibility is a hallmark of intelligent behaviour, these multi-task scenarios remain an important challenge ... More
ADAM30 Downregulates APP-Linked Defects Through Cathepsin D Activation in Alzheimer's DiseaseJun 18 2019Although several ADAMs (A disintegrin-like and metalloproteases) have been shown to contribute to the amy-loid precursor protein (APP) metabolism, the full spectrum of metalloproteases involved in this metabolism remains to be established. Transcriptomic ... More
Bayesian fusion and multimodal DCM for EEG and fMRIJun 18 2019This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from ... More
Brain Maturation Study during Adolescence Using Graph Laplacian Learning Based Fourier TransformJun 17 2019Objective: Longitudinal neuroimaging studies have demonstrated that adolescence is the crucial developmental epoch of continued brain growth and change. A large number of researchers dedicate to uncovering the mechanisms about brain maturity during adolescence. ... More
Brain Network Construction and Classification Toolbox (BrainNetClass)Jun 17 2019Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based ... More
Rapid online learning and robust recall in a neuromorphic olfactory circuitJun 17 2019The mammalian olfactory system learns new odors rapidly, exhibits negligible interference among odor memories, and identifies known odors under challenging conditions. The mechanisms by which it does so are unknown. We here present a general theory for ... More
Brain Controllability: not a slam dunk yetJun 16 2019In our recent article (Tu et al., Warnings and caveats in brain controllability, arXiv:1705.08261) we provided quantitative evidence to show that there are warnings and caveats in the way Gu and collaborators (Gu et al. Controllability of structural brain ... More
Quantized Three-Ion-Channel Neuron Model for Neural Action PotentialsJun 16 2019The Hodgkin-Huxley model describes the conduction of the nervous impulse through the axon, whose membrane's electric response can be described employing multiple connected electric circuits containing capacitors, voltage sources, and conductances. These ... More
Essential Motor Cortex Signal Processing: an ERP and functional connectivity MATLAB toolbox -- User GuideJun 15 2019The purpose of this document is to help individuals use the "Essential Motor Cortex Signal Processing MATLAB Toolbox". The toolbox implements various methods for three major aspects of investigating human motor cortex from Neuroscience view point: (1) ... More
Agito ergo sum: correlates of spatiotemporal motion characteristics during fMRIJun 15 2019The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-ofthe-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise ... More
Blocking facial mimicry selectively alters early stages of facial expression processingJun 14 2019Simulation models of facial expressions suggest that posterior visual areas and brain areas underpinning sensorimotor simulations might interact to improve facial expression processing. According to these models, facial mimicry may contribute to the visual ... More
The scientific case for brain simulationsJun 14 2019A key element of the European Union's Human Brain Project (HBP) and other large-scale brain research projects is simulation of large-scale model networks of neurons. Here we argue why such simulations will likely be indispensable for bridging the scales ... More
Periodic Codes and Sound LocalizationJun 14 2019Inspired by the sound localization system of the barn owl, we define a new class of neural codes, called periodic codes, and study their basic properties. Periodic codes are binary codes with a special patterned form that reflects the periodicity of the ... More
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement LearningJun 13 2019We model human decision-making behaviors in a risk-taking task using inverse reinforcement learning (IRL) for the purposes of understanding real human decision making under risk. To the best of our knowledge, this is the first work applying IRL to reveal ... More
Self-organized critical balanced networks: a unified frameworkJun 13 2019Asynchronous irregular (AI) and critical states are two competing frameworks proposed to explain spontaneous neuronal activity. Here, we propose a mean-field model with simple stochastic neurons that generalizes the integrate-and-fire network of Brunel ... More
Information capacity of a network of spiking neuronsJun 13 2019We study a model of spiking neurons, with recurrent connections that result from learning a set of spatio-temporal patterns with a spike-timing dependent plasticity rule and a global inhibition. We investigate the ability of the network to store and selectively ... More
Imperfect fifths pack into musical scalesJun 13 2019Musical scales are used in cultures throughout the world, but the question as to how they evolved remains open. Some suggest that scales based on the harmonic series are inherently pleasant, while others propose that scales are chosen that are easy to ... More
Modeling functional resting-state brain networks through neural message passing on the human connectomeJun 12 2019Understanding the relationship between the structure and function of the human brain is one of the most important open questions in Neurosciences. In particular, Resting State Networks (RSN) and more specifically the Default Mode Network (DMN) of the ... More
Core language brain network for fMRI-language task used in clinical applicationsJun 12 2019Functional magnetic resonance imaging (fMRI) is widely used in clinical applications to highlight brain areas involved in specific cognitive processes. Brain impairments, such as tumors, suppress the fMRI activation of the anatomical areas they invade ... More
Hysteresis, neural avalanches and critical behaviour near a first-order transition of a spiking neural networkJun 12 2019Many experimental results, both in-vivo and in-vitro, support the idea that the brain cortex operates near a critical point, and at the same time works as a reservoir of precise spatio-temporal patterns. However the mechanism at the basis of these observations ... More
Parallel scalable simulations of biological neural networks using TensorFlow: A beginner's guideJun 10 2019Neuronal networks are often modeled as systems of coupled, nonlinear, ordinary or partial differential equations. The number of differential equations used to model a network increases with the size of the network and the level of detail used to model ... More
Quickly fading afterimages: hierarchical adaptations in human perceptionJun 10 2019Afterimages result from a prolonged exposure to still visual stimuli. They are best detectable when viewed against uniform backgrounds and can persist for multiple seconds. Consequently, the dynamics of afterimages appears to be slow by their very nature. ... More
Electrodiffusion Models of Axon and Extracellular Space Using the Poisson-Nernst-Planck EquationsJun 07 2019In studies of the brain and the nervous system, extracellular signals - as measured by local field potentials (LFPs) or electroencephalography (EEG) - are of capital importance, as they allow to simultaneously obtain data from multiple neurons. The exact ... More
Association Between Intelligence and Cortical Thickness in Adolescents: Evidence from the ABCD StudyJun 07 2019The relationship between the intelligence and brain morphology is warmly concerned in cognitive field. General intelligence can be defined as the weighted sum of fluid and crystallized intelligence. Fluid abilities depend on genes and genes expression ... More
Stochasticity and Robustness in Spiking Neural NetworksJun 06 2019Artificial neural networks normally require precise weights to operate, despite their origins in biological systems, which can be highly variable and noisy. When implementing artificial networks which utilize analog 'synaptic' devices to encode weights, ... More
Telling neuronal apples from oranges: analytical performance modeling of neural tissue simulationsJun 06 2019Computational modeling and simulation have become essential tools in the quest to better understand the brain's makeup and to decipher the causal interrelations of its components. The breadth of biochemical and biophysical processes and structures in ... More
The Substrates of Integrated Neurocognitive Rehabilitation Platforms (INCRPs)Jun 06 2019The integrated neurocognitive rehabilitation platforms (INCRPs) refer to infrastructures and teams integrated for set of interventions which aim to restore, or compensate for cognitive deficits. Cognitive skills may be lost or altered due to brain damage ... More
Non-uniqueness phenomenon of object representation in modelling IT cortex by deep convolutional neural network (DCNN)Jun 06 2019Recently DCNN (Deep Convolutional Neural Network) has been advocated as a general and promising modelling approach for neural object representation in primate inferotemporal cortex. In this work, we show that some inherent non-uniqueness problem exists ... More
A Deep Learning Framework for Classification of in vitro Multi-Electrode Array RecordingsJun 05 2019Multi-Electrode Arrays (MEAs) have been widely used to record neuronal activities, which could be used in the diagnosis of gene defects and drug effects. In this paper, we address the problem of classifying in vitro MEA recordings of mouse and human neuronal ... More
On the use of Pairwise Distance Learning for Brain Signal Classification with Limited ObservationsJun 05 2019The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neuronal diseases. This work proposes a pairwise distance learning approach ... More