Computational Neuroscience Commons^™

Neuromatch Academy: A 3-Week, Online Summer School In Computational Neuroscience, Bernard Marius 'T Hart, Titipat Achakulvisut, Ayoade Adeyemi, Athena Akrami, Bradly Alicea, Alicia Alonso-Andres, Diego Alzate-Correa, Arash Ash, Jesus J. Ballesteros, Aishwarya Balwani, Eleanor Batty, Ulrik Beierholm, Ari S. Benjamin, Upinder Bhalla, Gunnar Blohm, Joachim C. H. Blohm, Kathryn Bonnen, Marco Brigham, Bingni W. Brunton, John Butler, Brandon Caie, N Alex Cayco Gajic, Sharbatanu Chatterjee, Spyridon Chavlis, Ruidong Chen, You Cheng, H. M. Chow, Raymond Chua, Yunwei Dai, Isaac David, Eric E. J. Dewitt, Julien Denis, Alish Dipani, Arianna Dorschel, Jan Drugowitsch, Kshitij Dwivedi, Sean Escola, Haoxue Fan, Roozbeh Farhoodi, Yicheng Fei, Pierre-Étienne Fiquet, Lorenzo Fontolan, Jeremy Forest, Yuki Fujishima, Byron V. Galbraith, Mario Galdamez, Richard Gao, Julijana Gjorgjieva, Alexander Gonzalez, Qinglong Gu, Yueqi Guo, Ziyi Guo, Pankaj K. Gupta, Busra Tugce Gurbuz, Caroline Haimerl, Jordan B. Harrod, Alexandre Hyafil, Martin Irani, Daniel Jacobson, Michelle Johnson, Ilenna Simone Jones, Gili Karni, Robert E. Kass, Hyosub Edward Kim, Andreas M. Kist, Randal Koene, Konrad Kording, Matthew R. Krause, Arvind Kumar, Norma K. Kühn, Ray Lc, Matthew L. Laporte, Junseok Lee, Songting Li, Sikun Lin, Yang Lin, Shuze Liu, Tony Liu, Jesse A. Livezey, Linlin Lu, Jakob H. Macke, Kelly Mahaffy, A. Lucas Martins, Nicolás Martorell, Manolo Martínez, Marcelo G. Mattar, Jorge Aurelio Menendez, Kenneth D. Miller, Patrick J. Mineault, Nosratullah Mohammadi, Yalda Mohsenzadeh, Elenor Morgenroth, Taha Morshedzadeh, Alice Claudia Mosberger, Madhuvanthi Muliya, Marieke Mur, John D. Murray, Yashas Nd, Richard Naud, Prakriti Nayak, Anushka Oak, Itzel Olivos Castillo, Seyedmehdi Orouji, Jorge Otero-Millan, Marius Pachitariu, Biraj Pandey, Renato Paredes, Jesse Parent, Il Memming Park, Megan A. K. Peters, Xaq Pitkow, Panayiota Poirazi, Haroon Popal, Sandhya Prabhakaran, Tian Qiu, Srinidhi Ragunathan, Raul Rodriguez-Cruces, David Rolnick, Ashish Kumar Sahoo, Saeed Salehinajafabadi, Cristina Savin, Shreya Saxena, Paul Schrater, Karen Schroeder, Alice C. Schwarze, Madineh Sedigh-Sarvestani, K. Yuvaraj Sekhar, Reza Shadmehr, Maryam M. Shanechi, Siddhant Sharma, Eric Shea-Brown, Krishna V. Shenoy, Carolina L. Shimabukuro, Sergey Shuvaev, Man Ching Alison Sin, Maurice Smith, Nicholas A. Steinmetz, Karolina Stosio, Elizabeth Straley, Gabrielle Strandquist, Carsen Stringer, Rimjhim Tomar, Ngoc Tran, Sofia Triantafillou, Lawrence Udeigwe, Davide Valeriani, Vincent Valton, Maryam Vaziri-Pashkam, Peter Vincent, Gal Vishne, Pascal Wallisch, Peiyuan Wang, Claire Ward, Michael Waskom, Kunlin Wei, Anqi Wu, Zhengwei Wu, Brad Wyble, Lei Zhang, Daniel Zysman, Federico D’Oleire Uquillas, Tara Van Viegen

Articles

No abstract provided.

Performance Of Openbci Eeg Binary Intent Classification With Laryngeal Imagery, Nathan George, Samuel Kuhn

Regis University Faculty Publications (comprehensive list)

One of the greatest goals of neuroscience in recent decades has been to rehabilitate individuals who no longer have a functional relationship between their mind and their body. Although neuroscience has produced technologies which allow the brains of paralyzed patients to accomplish tasks such as spell words or control a motorized wheelchair, these technologies utilize parts of the brain which may not be optimal for simultaneous use. For example, if you needed to look at flashing lights to spell words for communication, it would be difficult to simultaneously look at where you are moving. To improve upon this issue, this …

3d Architectural Analysis Of Neurons, Astrocytes, Vasculature & Nuclei In The Motor And Somatosensory Murine Cortical Columns, Jared Leichner

FIU Electronic Theses and Dissertations

Characterization of the complex cortical structure of the brain at a cellular level is a fundamental goal of neuroscience which can provide a better understanding of both normal function as well as disease state progression. Many challenges exist however when carrying out this form of analysis. Immunofluorescent staining is a key technique for revealing 3-dimensional structure, but subsequent fluorescence microscopy is limited by the quantity of simultaneous targets that can be labeled and intrinsic lateral and isotropic axial point-spread function (PSF) blurring during the imaging process in a spectral and depth-dependent manner. Even after successful staining, imaging and optical deconvolution, …

Circuits With Broken Fibration Symmetries Perform Core Logic Computations In Biological Networks, Ian Leifer, Flaviano Morone, Saulo D. S. Reis, José S. Andrade Jr., Mariano Sigman, Hernán A. Makse

Publications and Research

We show that logic computational circuits in gene regulatory networks arise from a fibration symmetry breaking in the network structure. From this idea we implement a constructive procedure that reveals a hierarchy of genetic circuits, ubiquitous across species, that are surprising analogues to the emblematic circuits of solid-state electronics: starting from the transistor and progressing to ring oscillators, current-mirror circuits to toggle switches and flip-flops. These canonical variants serve fundamental operations of synchronization and clocks (in their symmetric states) and memory storage (in their broken symmetry states). These conclusions introduce a theoretically principled strategy to search for computational building blocks …

Timing Is Everything: Temporal Dynamics Of Brain Activity Using The Human Connectome Project, Francesca Lofaro

Summer Research

Most neuroimaging studies produce snapshots of brain activity. The goal of this project is to examine the temporal dynamics of how these areas interact through time, using fear as a case study to assess how regions involved in fear interact. Working with Matlab computer code, I sort through the large fMRI dataset known as the Human Connectome Project to extract neuroimaging data from patients with different NIH Toolbox Fear-Somatic survey scores to assess the temporal dynamics between brain regions. The results will allow an understanding beyond which areas are involved, and instead will provide a picture of how these areas …

Code For "Noise-Enhanced Coding In Phasic Neuron Spike Trains", Cheng Ly, Brent D. Doiron

Statistical Sciences and Operations Research Data

This zip file contains Matlab scripts and ode (XPP) files to calculate the statistics of the models in "Noise-Enhanced Coding in Phasic Neuron Spike Trains". This article is published in PLoS ONE.

Rules And Mechanisms For Efficient Two-Stage Learning In Neural Circuits, Tiberiu Teşileanu, Bence Ölveczky, Vijay Balasubramanian

Publications and Research

Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in ‘tutor’ circuits (e.g., LMAN) should match plasticity mechanisms in ‘student’ circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching …

Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang

COBRA Preprint Series

Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …

Analysis Of Neuronal Sequences Using Pairwise Biases, Zachary Roth

Department of Mathematics: Dissertations, Theses, and Student Research

Sequences of neuronal activation have long been implicated in a variety of brain functions. In particular, these sequences have been tied to memory formation and spatial navigation in the hippocampus, a region of mammalian brains. Traditionally, neuronal sequences have been interpreted as noisy manifestations of neuronal templates (i.e., orderings), ignoring much richer structure contained in the sequences. This paper introduces a new tool for understanding neuronal sequences: the bias matrix. The bias matrix captures the probabilistic tendency of each neuron to fire before or after each other neuron. Despite considering only pairs of neurons, the bias matrix captures the best …

Design, Programming, And User-Experience, Kaila G. Manca

Honors Scholar Theses

This thesis is a culmination of my individualized major in Human-Computer Interaction. As such, it showcases my knowledge of design, computer engineering, user-experience research, and puts into practice my background in psychology, com- munications, and neuroscience.

I provided full-service design and development for a web application to be used by the Digital Media and Design Department and their students.This process involved several iterations of user-experience research, testing, concepting, branding and strategy, ideation, and design. It lead to two products.

The first product is full-scale development and optimization of the web appli- cation.The web application adheres to best practices. It was …

Modeling Neurovascular Coupling From Clustered Parameter Sets For Multimodal Eeg-Nirs, M. Tanveer Talukdar, H. Robert Frost, Solomon G. G. Diamond

Dartmouth Scholarship

Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from …

Firing Rate Dynamics In Recurrent Spiking Neural Networks With Intrinsic And Network Heterogeneity, Cheng Ly

Statistical Sciences and Operations Research Publications

Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, …

A Principle Of Economy Predicts The Functional Architecture Of Grid Cells, Xue-Xin Wei, Jason Prentice, Vijay Balasubramanian

Publications and Research

Grid cells in the brain respond when an animal occupies a periodic lattice of ‘grid fields’ during navigation. Grids are organized in modules with different periodicity. We propose that the grid system implements a hierarchical code for space that economizes the number of neurons required to encode location with a given resolution across a range equal to the largest period. This theory predicts that (i) grid fields should lie on a triangular lattice, (ii) grid scales should follow a geometric progression, (iii) the ratio between adjacent grid scales should be √e for idealized neurons, and lie between 1.4 and 1.7 …

Derivation Of A Novel Efficient Supervised Learning Algorithm From Cortical-Subcortical Loops, Ashok Chandrashekar, Richard Granger

Dartmouth Scholarship

Although brain circuits presumably carry out powerful perceptual algorithms, few instances of derived biological methods have been found to compete favorably against algorithms that have been engineered for specific applications. We forward a novel analysis of a subset of functions of cortical-subcortical loops, which constitute more than 80% of the human brain, thus likely underlying a broad range of cognitive functions. We describe a family of operations performed by the derived method, including a non-standard method for supervised classification, which may underlie some forms of cortically dependent associative learning. The novel supervised classifier is compared against widely used algorithms for …

Neural Spike Renormalization. Part I — Universal Number 1, Bo Deng

Department of Mathematics: Faculty Publications

For a class of circuit models for neurons, it has been shown that the transmembrane electrical potentials in spike bursts have an inverse correlation with the intra-cellular energy conversion: the fewer spikes per burst the more energetic each spike is. Here we demonstrate that as the per-spike energy goes down to zero, a universal constant to the bifurcation of spike-bursts emerges in a similar way as Feigenbaum’s constant does to the period-doubling bifurcation to chaos generation, and the new universal constant is the first natural number 1.

Neural Spike Renormalization. Part Ii — Multiversal Chaos, Bo Deng

Department of Mathematics: Faculty Publications

Reported here for the first time is a chaotic infinite-dimensional system which contains infinitely many copies of every deterministic and stochastic dynamical system of all finite dimensions. The system is the renormalizing operator of spike maps that was used in a previous paper to show that the first natural number 1 is a universal constant in the generation of metastable and plastic spike-bursts of a class of circuit models of neurons.

Metastability And Plasticity In A Conceptual Model Of Neurons, Bo Deng

Department of Mathematics: Faculty Publications

For a new class of neuron models we demonstrate here that typical membrane action potentials and spike-bursts are only transient states but appear to be asymptotically stable; and yet such metastable states are plastic — being able to dynamically change from one action potential to another with different pulse frequencies and from one spike-burst to another with different spike-per-burst numbers. The pulse and spike-burst frequencies change with individual ions’ pump currents while their corresponding metastable-plastic states maintain the same transmembrane voltage and current profiles in range. It is also demonstrated that the plasticity requires two one-way ion pumps operating in …

Conceptual Circuit Models Of Neurons, Bo Deng

Department of Mathematics: Faculty Publications

A systematic circuit approach tomodel neurons with ion pump is presented here by which the voltage-gated current channels are modeled as conductors, the diffusion-induced current channels are modeled as negative resistors, and the one-way ion pumps are modeled as one-way inductors. The newly synthesized models are different from the type of models based on Hodgkin-Huxley (HH) approach which aggregates the electro, the diffusive, and the pump channels of each ion into one conductance channel. We show that our new models not only recover many known properties of the HH type models but also exhibit some new that cannot be extracted …