Computational Neuroscience Commons^™

Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen

Dartmouth College Ph.D Dissertations

Transfer learning is a machine learning technique founded on the idea that knowledge acquired by a model during “pretraining” on a source task can be transferred to the learning of a target task. Successful transfer learning can result in improved performance, faster convergence, and reduced demand for data. This technique is particularly desirable for the task of brain decoding in the domain of functional magnetic resonance imaging (fMRI), wherein even the most modern machine learning methods can struggle to decode labelled features of brain images. This challenge is due to the highly complex underlying signal, physical and neurological differences between …

Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi

Dissertations

Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. This dissertation constructs Deep Hybrid Models that address these shortcomings by combining deep learning with mechanistic modeling. In particular, this dissertation uses Generative Adversarial Networks (GANs) to provide an inverse mapping of data to mechanistic models and identifies the distributions of mechanistic model parameters coherent to the data.

Chapter 1 provides background information on …

A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann

MODVIS Workshop

Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the …

Artificial Dendritic Neuron: A Model Of Computation And Learning Algorithm, Zachary Hutchinson

Electronic Theses and Dissertations

Dendrites are root-like extensions from the neuron cell body and have long been thought to serve as the predominant input structures of neurons. Since the early twentieth century, neuroscience research has attempted to define the dendrite’s contribution to neural computation and signal integration. This body of experimental and modeling research strongly indicates that dendrites are not just input structures but are crucial to neural processing. Dendritic processing consists of both active and passive elements that utilize the spatial, electrical and connective properties of the dendritic tree.

This work presents a neuron model based around the structure and properties of dendrites. …

Multimodal Neuron Classification Based On Morphology And Electrophysiology, Aqib Ahmad

Graduate Theses, Dissertations, and Problem Reports

Categorizing neurons into different types to understand neural circuits and ultimately brain function is a major challenge in neuroscience. While electrical properties are critical in defining a neuron, its morphology is equally important. Advancements in single-cell analysis methods have allowed neuroscientists to simultaneously capture multiple data modalities from a neuron. We propose a method to classify neurons using both morphological structure and electrophysiology. Current approaches are based on a limited analysis of morphological features. We propose to use a new graph neural network to learn representations that more comprehensively account for the complexity of the shape of neuronal structures. In …

Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler

Computer Science and Computer Engineering Undergraduate Honors Theses

Sounds with a high level of stationarity, also known as sound textures, have perceptually relevant features which can be captured by stimulus-computable models. This makes texture-like sounds, such as those made by rain, wind, and fire, an appealing test case for understanding the underlying mechanisms of auditory recognition. Previous auditory texture models typically measured statistics from auditory filter bank representations, and the statistics they used were somewhat ad-hoc, hand-engineered through a process of trial and error. Here, we investigate whether a better auditory texture representation can be obtained via contrastive learning, taking advantage of the stationarity of auditory textures to …

A Generative-Discriminative Approach To Human Brain Mapping, Deepanshu Wadhwa

Electronic Thesis and Dissertation Repository

During everyday behaviours, the brain shows complex spatial patterns of activity. These activity maps are very replicable within an individual, but vary significantly across individuals, even though they are evoked by the same behaviour. It is unknown how differences in these spatial patterns relate to differences in behavior or function. More fundamentally, the structural, developmental, and genetic factors that determine the spatial organisation of these brain maps in each individual are unclear. Here we propose a new quantitative approach for uncovering the basic principles by which functional brain maps are organized. We propose to take an generative-discriminative approach to human …

Is The Selective Tuning Model Of Visual Attention Still Relevant?, John K. Tsotsos

MODVIS Workshop

No abstract provided.

Finding Nonlinear Relationships In Functional Magnetic Resonance Imaging Data With Genetic Programming, James Hughes

Electronic Thesis and Dissertation Repository

The human brain is a complex, nonlinear dynamic chaotic system that is poorly understood. When faced with these difficult to understand systems, it is common to observe the system and develop models such that the underlying system might be deciphered. When observing neurological activity within the brain with functional magnetic resonance imaging (fMRI), it is common to develop linear models of functional connectivity; however, these models are incapable of describing the nonlinearities we know to exist within the system.

A genetic programming (GP) system was developed to perform symbolic regression on recorded fMRI data. Symbolic regression makes fewer assumptions than …

Efficient Reduced Bias Genetic Algorithm For Generic Community Detection Objectives, Aditya Karnam Gururaj Rao

Theses

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by …

Deep Learning-Based Framework For Autism Functional Mri Image Classification, Xin Yang, Saman Sarraf, Ning Zhang

Journal of the Arkansas Academy of Science

The purpose of this paper is to introduce deep learning-based framework LeNet-5 architecture and implement the experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.

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 …

Object Recognition And Visual Search With A Physiologically Grounded Model Of Visual Attention, Frederik Beuth, Fred H. Hamker

MODVIS Workshop

Visual attention models can explain a rich set of physiological data (Reynolds & Heeger, 2009, Neuron), but can rarely link these findings to real-world tasks. Here, we would like to narrow this gap with a novel, physiologically grounded model of visual attention by demonstrating its objects recognition abilities in noisy scenes.

To base the model on physiological data, we used a recently developed microcircuit model of visual attention (Beuth & Hamker, in revision, Vision Res) which explains a large set of attention experiments, e.g. biased competition, modulation of contrast response functions, tuning curves, and surround suppression. Objects are represented by …

Modeling Visual Features To Recognize Biological Motion: A Developmental Approach, Giulio Sandini, Nicoletta Noceti, Alessia Vignolo, Alessandra Sciutti, Francesco Rea, Alessandro Verri, Francesca Odone

MODVIS Workshop

In this work we deal with the problem of designing and developing computational vision models – comparable to the early stages of the human development – using coarse low-level information.

More specifically, we consider a binary classification setting to characterize biological movements with respect to non-biological dynamic events. To this purpose, our model builds on top of the optical flow estimation, and abstract the representation to simulate the limited amount of visual information available at birth. We take inspiration from known biological motion regularities explained by the Two-Thirds Power Law, and design a motion representation that includes different low-level features, …

Learning Emotions: A Software Engine For Simulating Realistic Emotion In Artificial Agents, Douglas Code

Senior Independent Study Theses

This paper outlines a software framework for the simulation of dynamic emotions in simulated agents. This framework acts as a domain-independent, black-box solution for giving actors in games or simulations realistic emotional reactions to events. The emotion management engine provided by the framework uses a modified Fuzzy Logic Adaptive Model of Emotions (FLAME) model, which lets it manage both appraisal of events in relation to an individual’s emotional state, and learning mechanisms through which an individual’s emotional responses to a particular event or object can change over time. In addition to the FLAME model, the engine draws on the design …

Brain Function Differences In Language Processing In Children And Adults With Autism, Diane L. Williams, Vladimir L. Cherkassky, Robert A. Mason, Timothy A. Keller, Nancy J. Minshew, Marcel Adam Just

Marcel Adam Just

No abstract provided.

Distinctive Neural Processes During Learning In Autism, Sarah Schipul, Diane Williams, Timothy Keller, Nancy Minshew, Marcel Just

Marcel Adam Just

No abstract provided.

Brain Activation For Language Dual-Tasking: Listening To Two People Speak At The Same Time And A Change In Network Timing, Augusto Buchweitz, Timothy Keller, Ann Meyler, Marcel Just

Marcel Adam Just

No abstract provided.

Autism As A Neural Systems Disorder: A Theory Of Frontal-Posterior Underconnectivity, Marcel Just, Timothy Keller, Vicente Malave, Rajesh Kana, Sashank Varma

Marcel Adam Just

No abstract provided.

An Fmri Investigation Of Analogical Mapping In Metaphor Comprehension: The Influence Of Context And Individual Cognitive Capacities On Processing Demands., Chantel Prat, Robert Mason, Marcel Just

Marcel Adam Just

No abstract provided.

Exploring Commonalities Across Participants In The Neural Representation Of Objects, Svetlana V. Shinkareva, Vicente L. Malave, Marcel Adam Just, Tom M. Mitchell

Marcel Adam Just

No abstract provided.

Identifying Bilingual Semantic Neural Representations Across Languages, Augusto Buchweitz, Svetlana V. Shinkareva, Robert A. Mason, Tom M. Mitchell, Marcel Adam Just

Marcel Adam Just

No abstract provided.

Differentiable Cortical Networks For Inferences Concerning People’S Intentions Versus Physical Causality, Robert Mason, Marcel Just

Marcel Adam Just

No abstract provided.

Autonomy Of Lower-Level Perception From Global Processing In Autism: Evidence From Brain Activation And Functional Connectivity, Yanni Liu, Vladimir L. Cherkassky, Nancy J. Minshew, Marcel Adam Just

Marcel Adam Just

No abstract provided.

Inter-Regional Brain Communication And Its Disturbance In Autism, Sarah E. Schipul, Timothy A. Keller, Marcel Adam Just

Marcel Adam Just

No abstract provided.

Quantitative Modeling Of The Neural Representation Of Objects: How Semantic Feature Norms Can Account For Fmri Activation, Kai-Min Kevin Chang, Tom Mitchell, Marcel Adam Just

Marcel Adam Just

No abstract provided.

Individual Differences In The Neural Basis Of Causal Inferencing, Chantel S. Prat, Robert A. Mason, Marcel Adam Just

Marcel Adam Just

No abstract provided.

Commonality Of Neural Representations Of Words And Pictures, Svetlana V. Shinkareva, Vincente L. Malave, Robert A. Mason, Tom M. Mitchell, Marcel Adam Just

Marcel Adam Just

No abstract provided.

The Neural Basis Of Deictic Shifting In Linguistic Perspective-Taking In High-Functioning Autism, Akiko Mizuno, Yanni Liu, Diane L. Williams, Timothy A. Keller, Nancy J. Minshew, Marcel Adam Just

Marcel Adam Just

No abstract provided.

Exploring The Neural Dynamics Underpinning Individual Differences In Sentence Comprehension, Chantel S. Prat, Marcel Adam Just

Marcel Adam Just

No abstract provided.