Computational Neuroscience Commons^™

Computational Neuroscience, Michelle Greene, Ns/Py 357 Students

Open Educational Resources

In Fall 2019, the Computational Neuroscience class at Bates College collaborated to begin an open textbook. Eight students across three majors collaborated to present the content they were learning to students who were similar to themselves. This project serves both pedagogical and social goals. By writing for fellow students, we leveraged the power of teaching for learning. By bringing together students of diverse academic backgrounds, we leveraged the power of peer instruction. Finally, by writing in the open, students not only brought their best work forward, but are working to contribute to an open knowledge environment that democratizes information. This …

Understanding Object Motion Encoding In The Mammalian Retina., Victor Julian Depiero

Electronic Theses and Dissertations

Phototransduction, transmission of visual information down the optic nerve incurs delays on the order of 50 – 100ms. This implies that the neuronal representation of a moving object should lag behind the object’s actual position. However, studies have demonstrated that the visual system compensates for neuronal delays using a predictive mechanism called phase advancing, which shifts the population response toward the leading edge of a moving object’s retinal image. To understand how this compensation is achieved in the retina, I investigated cellular and synaptic mechanisms that drive phase advancing. I used three approaches, each testing phase advancing at a …

Network Structure And Dynamics Of Biological Systems, Deena R. Schmidt

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Analog Implementation Of The Hodgkin-Huxley Model Neuron, Zachary D. Mobille, George H. Rutherford, Jordan Brandt-Trainer, Rosangela Follmann, Epaminondas Rosa

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

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

MODVIS Workshop

No abstract provided.

Functional Organization Of Cortical Maps For Ocular Dominance And Light-Dark Polarity In Primary Visual Cortex, Sohrab Najafian, Jian Zhong Jin, Jose-Manuel Alonso

MODVIS Workshop

No abstract provided.

Computations Of Top-Down Attention By Modulating V1 Dynamics, David Berga, Xavier Otazu

MODVIS Workshop

The human visual system processes information defining what is visually conspicuous (saliency) to our perception, guiding eye movements towards certain objects depending on scene context and its feature characteristics. However, attention has been known to be biased by top-down influences (relevance), which define voluntary eye movements driven by goal-directed behavior and memory. We propose a unified model of the visual cortex able to predict, among other effects, top-down visual attention and saccadic eye movements. First, we simulate activations of early mechanisms of the visual system (RGC/LGN), by processing distinct image chromatic opponencies with Gabor-like filters. Second, we use a cortical …

Differentiating Changes In Population Encoding Models With Psychophysics And Neuroimaging, Jason Hays, Fabian Soto Phd

MODVIS Workshop

It is now common among visual scientists to make inferences about neural population coding of stimuli from indirect measures such as those provided by neuroimaging and psychophysics. The success of such studies depends strongly on simulation work using standard population encoding models extended with decoders (in psychophysics) and measurement models (in neuroimaging). However, not all studies are accompanied by simulation work, and those that are tend to vary widely in their assumptions about encoding, decoding, and measurement. To solve these issues, we designed a Python package (PEMGUIN) to assist computational modelling by providing simple ways to manage encoders' tuning functions, …

Bifurcation Analysis Of A Photoreceptor Interaction Model For Retinitis Pigmentosa, Anca R. Radulescu

Biology and Medicine Through Mathematics Conference

No abstract provided.

Spiking Activity In Networks Of Neurons Impacted By Axonal Swelling, Brian Frost, Stan Mintchev

Biology and Medicine Through Mathematics Conference

No abstract provided.

Virtual Eye: A Spatial-Temporal Bottom-Up Eye Sensitivity Model, Todd Goodall

MODVIS Workshop

Video quality and compression models use the

spatial contrast sensitivity function (CSF), which is solved

based on a linear system approximation. This function measures

the eye’s sensitivity to sinusoid gratings, ignoring the subtle

connectivity and inhomogeniety of cell density across the

visual field. Non-linear aspects of the eye, such as the change

in frequency sensitivity with changing illumination, are not

captured by this simple approximation. We propose Virtual

Eye, a bottom-up approach that models the spatio-temporal

dynamics of the eye across the visual field. Each functional

retinal cell layer in the eye is modeled using non-uniform spatial

cell responses, which …

Predicting Dynamics From Hardwiring In Canonical Low-Dimensional Coupled Networks, Anca R. Radulescu

Biology and Medicine Through Mathematics Conference

No abstract provided.

Variance Partitioning Reveals Consistent Representation Of Object Boundary Contours In Lo Across Different Datasets, Mark D. Lescroart, Utkarsh Singhal

MODVIS Workshop

No abstract provided.

The 5-Ht1a-R Knockout Mouse As A Model Of Later Life Anxiety Disorders: Implications For Sex Differences, Tatyana Budylin

Dissertations, Theses, and Capstone Projects

Anxiety affects nearly twice as many women as it affects men across all cultures and economic groups. Importantly, girls have a higher chance of inheriting anxiety disorders than boys, and many anxiety disorders appear at a very young age. However, little is known about sex differences in brain and behavioral development and how they relate to anxiety in adulthood. Serotonin 1A receptor (5-HT_1A-R) mediated signaling has been implicated in depression and anxiety, however most studies that focus on the involvement of the 5-HT_1A-R have been conducted in adults. Little is known about how the 5-HT_{1A …}

Strengthening Relationships Between Neural Ideals And Receptive Fields, Angelique Morvant

Rose-Hulman Undergraduate Mathematics Journal

Neural codes are collections of binary vectors that represent the firing patterns of neurons. The information given by a neural code C can be represented by its neural ideal J_C. In turn, the polynomials in J_C can be used to determine the relationships among the receptive fields of the neurons. In a paper by Curto et al., three such relationships, known as the Type 1-3 relations, were linked to the neural ideal by three if-and-only-if statements. Later, Garcia et al. discovered the Type 4-6 relations. These new relations differed from the first three in that they were …

A Defense Of Pure Connectionism, Alex B. Kiefer

Dissertations, Theses, and Capstone Projects

Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming …

The Characterization Of Alzheimer’S Disease And The Development Of Early Detection Paradigms: Insights From Nosology, Biomarkers And Machine Learning, Isabel Milano

CMC Senior Theses

Alzheimer’s Disease (AD) is the only condition in the top ten leading causes of death for which we do not have an effective treatment that prevents, slows, or stops its progression. Our ability to design useful interventions relies on (a) increasing our understanding of the pathological process of AD and (b) improving our ability for its early detection. These goals are impeded by our current reliance on the clinical symptoms of AD for its diagnosis. This characterizations of AD often falsely assumes a unified, underlying AD-specific pathology for similar presentations of dementia that leads to inconsistent diagnoses. It also hinges …