Computational Neuroscience Commons^™

Astrocyte Spatial Distribution Affects Growth Dynamics Of Breast Cancer Brain Metastases: An Agent-Based Modeling Study, Rupleen Kaur

Biology and Medicine Through Mathematics Conference

No abstract provided.

Modeling Human Temporal Eeg Responses To Vr Visual Stimuli, Richard R. Foster, Connor Delaney, Dean J. Krusienski, Cheng Ly

Biology and Medicine Through Mathematics Conference

No abstract provided.

Spike Timing-Dependent Plasticity And Synaptic Scaling Invoke Episodic Bursting In An Excitatory Recurrent Neuronal Population, Ryno Chen, Gregory D. Conradi Smith

Biology and Medicine Through Mathematics Conference

No abstract provided.

The Effects Of Brain Control: A 3-D Agent-Based Model For Studying Pain, Kayla Kraeuter, Carley Reith, Benedict Kolber, Rachael Miller Neilan

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Computing Brain Networks With Complex Dynamics, Anca R. Radulescu

Biology and Medicine Through Mathematics Conference

No abstract provided.

Gap Junctions And Synchronization Clusters In The Thalamic Reticular Nuclei, Anca R. Radulescu, Michael Anderson

Biology and Medicine Through Mathematics Conference

No abstract provided.

Task-Driven Influences On Fixational Eye Movements, Jonathan Victor, Yen-Chu Lin, Michele Rucci

MODVIS Workshop

There is now compelling evidence that the spatiotemporal remapping carried out by fixational eye movements (FEMs) is an essential step in visual processing. Moreover, the overall Brownian-like statistics of FEMs are calibrated to map fine spatial detail into the temporal frequency range to which retinal circuitry is tuned. Here, we tested the hypothesis that the detailed spatial characteristics of FEMs can be adjusted to task demands via cognitive influences that operate even in the absence of a visual stimulus. We examined FEMs in a task that required subjects (N=6) to report which of two letters was displayed. Trials were blocked; …

Active Encoding Of Space Through Time, Michele Rucci, Jonathan D. Victor

MODVIS Workshop

No abstract provided.

Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens

MODVIS Workshop

No abstract provided.

Toward A Manifold Encoding Neural Responses, Luciano Dyballa, Andra M. Rudzite, Mahmood S. Hoseini, Mishek Thapa, Michael P. Stryker, Greg D. Field, Steven W. Zucker

MODVIS Workshop

Understanding circuit properties from physiological data presents two challenges: (i) recordings do not reveal connectivity, and (ii) stimuli only exercise circuits to a limited extent. We address these challenges for the mouse visual system with a novel neural manifold obtained using unsupervised algorithms. Each point in our manifold is a neuron; nearby neurons respond similarly in time to similar parts of a stimulus ensemble. This ensemble includes drifting gratings and flows, i.e., patterns resembling what a mouse would “see” running through fields.

Regarding (i), our manifold differs from the standard practice in computational neuroscience: embedding trials in neural coordinates. Topology …

Constraining The Binding Problem Using Maps, Zhixian Han, Anne Sereno

MODVIS Workshop

We constrained the binding problem by creating maps of different attributes. We compared the performance of different models with different maps in our current study. Our preliminary results showed that the performance of the model is the highest when location maps were used. These results suggest that the optimal way to constrain the binding problem is to create location maps of different attributes.

From Image Gradients To A Perceptual Metric Space, Alan Johnston

MODVIS Workshop

How do we achieve a sense of spatial dimension from a sense of location? There are three predominant ideas about how we achieve this; spatial isomorphism, in which what we see reflects differences in distance or size in the brain; that spatial extent depends upon motor sensations or intentions related to eye movements; and that distance is computed from the degree of correlation in neural activity between adjacent locations, with distance inversely proportional to the correlation. There are problems with each of these approaches, for example, neural correlation may depend more on image structure than adjacency - consider the case …

V1 Saliency Hypothesis And Central-Peripheral Dichotomy (Cpd), Li Zhaoping Prof. Dr.

MODVIS Workshop

No abstract provided.

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 …

Efficient Coding Of Local 2d Shape, James Elder, Timothy D. Oleskiw, Ingo Fruend, Gerick M. Lee, Andrew Sutter, Anitha Pasupathy, Eero Simoncelli, J Anthony Movshon, Lynne Kiorpes, Najib Majaj

MODVIS Workshop

Efficient coding provides a concise account of key early visual properties, but can it explain higher-level visual function such as shape perception? If curvature is a key primitive of local shape representation, efficient shape coding predicts that sensitivity of visual neurons should be determined by naturally-occurring curvature statistics, which follow a scale-invariant power-law distribution. To assess visual sensitivity to these power-law statistics, we developed a novel family of synthetic maximum-entropy shape stimuli that progressively match the local curvature statistics of natural shapes, but lack global structure. We find that humans can reliably identify natural shapes based on 4^th and …

Development Of A Neural Network Model To Identify Abnormalities In Cervical X-Rays, Alex P. Sheppert, Nasif Islam, Race Peterson, Michael T. Sullivan, John A. Kriak, David W. Sant, Kyle B. Bills

Annual Research Symposium

No abstract provided.

Mixed Mode Oscillations In Three-Timescale Coupled Morris-Lecar Neurons, Ngocanh Phan, Yangyang Wang

Biology and Medicine Through Mathematics Conference

No abstract provided.

Universality And Synchronization In Complex Quadratic Networks (Cqns), Anca R. Radulescu, Danae Evans

Biology and Medicine Through Mathematics Conference

No abstract provided.

Estimating Glutamate Transporter Surface Density In Mouse Hippocampal Astrocytes, Anca R. Radulescu, Annalisa Scimemi

Biology and Medicine Through Mathematics Conference

No abstract provided.

Automated Fitting Of Allosteric Parameters In Receptor Oligomer Models, Spenser Wood

Biology and Medicine Through Mathematics Conference

No abstract provided.

Olfactory Bulb Processing Of Ortho Versus Retronasal Odors, Michelle F. Craft, Andrea Barreiro, Shree Gautam, Woodrow Shew, Cheng Ly

Biology and Medicine Through Mathematics Conference

No abstract provided.

Validity Of Neural Distance Measures In Representational Similarity Analysis, Fabian A. Soto, Emily R. Martin, Hyeonjeong Lee, Nafiz Ahmed, Juan Estepa, Kianoosh Hosseini, Olivia A. Stibolt, Valentina Roldan, Alycia Winters, Mohammadreza Bayat

MODVIS Workshop

No abstract provided.

Visual Expertise In An Anatomically-Inspired Model Of The Visual System, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni

MODVIS Workshop

We report on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. Contrary to the generally accepted wisdom, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features due to face expertise. We take two features of the primate visual system into account: 1) The foveated retina; and 2) The log-polar mapping from retina to V1. We simulate acquisition of faces, etc., by gradually increasing the number of identities the network learns. …

Characterization Of Local And Global Statistics In Three Kinds Of Medical Images, And An Example Of Their Role In A Clinical Judgment, Jonathan Victor, Amanda Simon, Craig K. Abbey

MODVIS Workshop

No abstract provided.

A Two-Layer Model Explains Higher-Order Feature Selectivity Of V2 Neurons, Timothy D. Oleskiw, Justin D. Lieber, J. Anthony Movshon, Eero P. Simoncelli

MODVIS Workshop

Neurons in cortical area V2 respond selectively to higher-order visual features, such as the quasi-periodic structure of natural texture. However, a functional account of how V2 neurons build selectivity for complex natural image features from their inputs – V1 neurons locally tuned for orientation and spatial frequency – remains elusive.

We made single-unit recordings in area V2 in two fixating rhesus macaques. We presented stimuli composed of multiple superimposed grating patches that localize contrast energy in space, orientation, and scale. V2 activity is modeled via a two-layer linear-nonlinear network, optimized to use a sparse combination of V1-like outputs to account …

A Bayesian Account Of Depth From Shadow, James Elder, Patrick Cavanagh, Roberto Casati

MODVIS Workshop

When an object casts a shadow on a background surface, the offset of the shadow can be a compelling cue to the relative depth between the object and the background (e.g., Kersten et al 1996, Fig. 1). Cavanagh et al (2021) found that, at least for small shadow offsets, perceived depth scales almost linearly with shadow offset. Here we ask whether this finding can be understood quantitatively in terms of Bayesian decision theory.

Estimating relative depth from shadow offset is complicated by the fact that the shadow offset is co-determined by the slant of the light source relative to the …

Fixational Eye Movements, Perceptual Filling-In, And Perceptual Fading Of Grayscale Images, Michael E. Rudd

MODVIS Workshop

No abstract provided.

Constraining Computational Models Of Brightness Perception: What’S The Right Psychophysical Data?, Guillermo Aguilar, Joris Vincent, Marianne Maertens

MODVIS Workshop

No abstract provided.

Identifying And Localizing Multiple Objects Using Artificial Ventral And Dorsal Visual Cortical Pathways, Zhixian Han, Anne Sereno

MODVIS Workshop

We concluded in our previous study that model cortical visual pathways actively retained information differently according to the different goals of the training tasks. One limitation of our study was that there was only one object in each input image whereas in reality there may be multiple objects in a scene. In our current study, we try to find a brain-like algorithm that can recognize and localize multiple objects.

Model Of Visual Contrast Gain Control And Pattern And Noise Masking, Joshua A. Solomon

MODVIS Workshop

The first stage of the model can be subdivided into a global contrast sensitivity function (a 2-D log-parabolic filter of spatial frequency), followed by an array of sensors having Gabor-pattern receptive fields. The second stage is contrast gain control. At this stage, sensor outputs are subjected to an expansive transformation. Then the outputs are pooled and used to inhibit (or “normalize”) each other. Inhibition is strongest between sensors with similar preferences for orientation, spatial frequency and spatial location. In the final stage of the model, the nomalized sensor outputs for each image are subjected to Minkowski pooling. Two-alternative, forced-choice detection …