Systems Neuroscience Commons^™

Population Codes And Their Correlates In Decision Making, Neda Shahidi

Dissertations and Theses (Open Access)

This dissertation was organized in two parts: in part 1, we discussed Neural Correlates of Perceptual Accuracy” and in part 2 we discussed “Strategy encoding in Prefrontal Cortex”.

Abstract of part 1_The accurate transmission of electrical signals within neocortex is central to sensory perception and cognition. Theoretical studies have long proposed that the temporal coordination of cortical spiking activity controls signal transmission and cognitive function. In reality, whether and how the precise temporal coordination in neuronal populations during wakefulness influences perception remains a mystery. Here, we simultaneously recorded populations of neurons in early and mid-level visual cortex (areas V1 …

Mapping Molecular Datasets Back To The Brain Regions They Are Extracted From: Remembering The Native Countries Of Hypothalamic Expatriates And Refugees, Arshad Khan, Alice Grant, Anais Martinez, Gully Burns, Brendan Thatcher, Vishwanath Anekonda, Benjamin Thompson, Zachary Roberts, Daniel Moralejo, James Blevins

Selected Works Temporary Series

This article, which includes novel unpublished data along with commentary and analysis,focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined frombrain tissue to the original locations within the brain that they are derived from using digital atlasmapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomicanalyses conducted in the mammalian hypothalamus. Following a brief historical overview, wehighlight studies that have mined biochemical and molecular information from the hypothalamusand then lay out a strategy for how these data can be linked spatially to the mapped locations in acanonical brain atlas where the data come from, thereby allowing …

Mapping Molecular Datasets Back To The Brain Regions They Are Extracted From: Remembering The Native Countries Of Hypothalamic Expatriates And Refugees, Khan, Alice Grant, Anais Martinez, Gully Burns, Brendan Thatcher, Vishwanath Anekonda, Benjamin Thompson, Zachary Roberts, Daniel Moralejo, James Blevins

Departmental Papers (Biology)

This article, which includes novel unpublished data along with commentary and analysis,focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined frombrain tissue to the original locations within the brain that they are derived from using digital atlasmapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomicanalyses conducted in the mammalian hypothalamus. Following a brief historical overview, wehighlight studies that have mined biochemical and molecular information from the hypothalamusand then lay out a strategy for how these data can be linked spatially to the mapped locations in acanonical brain atlas where the data come from, thereby allowing …

Brightness Perception Involves Local Adaptation Opposed By Lateral Interaction, Qasim Zaidi, Romain Bachy, Jose-Manuel Alonso

MODVIS Workshop

No abstract provided.

Effect Of Noise On Mutually Inhibiting Pyramidal Cells In Visual Cortex: Foundation Of Stochasticity In Bi-Stable Perception, Naoki Kogo, Felix Kern, Thomas Nowotny, Raymond Van Ee, Richard Van Wezel, Takeshi Aihara

MODVIS Workshop

Bi-stable perception has been an important tool to investigate how visual input is interpreted and how it reaches consciousness. To explain the mechanisms of this phenomenon, it has been assumed that a mutual inhibition circuit plays a key role. It is possible that this circuit functions to resolve ambiguity of input image by quickly shifting the balance of competing signals in response to conflicting features. Recently we established an in vitro neural recording system combined with computerized connections mediated by model neurons and synapses (“dynamic clamp” system). With this system, mutual inhibition circuit between two pyramidal cells from primary visual …

Review: Do The Different Sensory Areas Within The Cat Anterior Ectosylvian Sulcal Cortex Collectively Represent A Network Multisensory Hub?, M. Alex Meredith, Mark T. Wallace, H. Ruth Clemo

Anatomy and Neurobiology Publications

Current theory supports that the numerous functional areas of the cerebral cortex are organized and function as a network. Using connectional databases and computational approaches, the cerebral network has been demonstrated to exhibit a hierarchical structure composed of areas, clusters and, ultimately, hubs. Hubs are highly connected, higher-order regions that also facilitate communication between different sensory modalities. One region computationally identified network hub is the visual area of the Anterior Ectosylvian Sulcal cortex (AESc) of the cat. The Anterior Ectosylvian Visual area (AEV) is but one component of the AESc that also includes the auditory (Field of the Anterior Ectosylvian …

Central And Peripheral Difference In Perceptual Bias In Ambiguous Perception Using Dichoptic Stimuli --- Implications For The Analysis-By-Synthesis Process In Visual Recognition, Li Zhaoping Prof

MODVIS Workshop

No abstract provided.

Similarity-Based Fusion Of Meg And Fmri Discerns Early Feedforward And Feedback Processing In The Ventral Stream, Yalda Mohsenzadeh Dr., Radoslaw Martin Cichy Dr., Aude Oliva Dr., Dimitrios Pantazis Dr.

MODVIS Workshop

Successful models of vision, such as DNNs and HMAX, are inspired by the human visual system, relying on a hierarchical cascade of feedforward transformations akin to the ventral stream. Despite these advances, the human visual cortex remains unique in complexity, with feedforward and feedback pathways characterized by rapid spatiotemporal dynamics as visual information is transformed into semantic content. Thus, a systematic characterization of the spatiotemporal and representational space of the ventral visual pathway can offer novel insights in the duration and sequencing of cognitive processes, suggesting computational constraints and new architectures for computer vision models.

To discern the feedforward and …

Comparing Diverse V1 Models On The Same Platform: Virtual V1sion, Cheryl Olman

MODVIS Workshop

No abstract provided.

Evaluating And Interpreting A Convolutional Neural Net As A Model Of V4, Dean A. Pospisil, Anitha Pasupathy, Wyeth Bair

MODVIS Workshop

Convolutional neural nets (CNNs) are currently the highest performing image recognition computer algorithms. Of interest is whether these CNNs, following extensive supervised training, perform computations similar to those in the ventral visual stream. We investigated whether CNN units’ tuning for shape boundaries was similar to V4’s as described in the angular position and curvature (APC) model of Pasupathy and Connor 2001. From units in all layers of AlexNet (see Figure A), an object recognition CNN, we recorded responses to the original study’s set of shape stimuli (51 simple closed shapes at up to 8 rotations) presented at 51 spatial translations …

On The Origin Of Sensory Errors, Jonathan R. Flynn

Dissertations and Theses (Open Access)

Estimation of perceptual variables is imprecise and prone to errors. Although the properties of these perceptual errors are well characterized, the physiological basis for these errors is unknown. One previously proposed explanation for these errors is the trial-by-trial variability of the responses of sensory neurons that encode the percept. Initially, it would seem that a complicated electrophysiological experiment would need to be performed to test this hypothesis. However, using a strong theoretical framework, I demonstrate that it is possible to determine statistical characteristics of the physiological mechanism responsible for perceptual errors solely from a behavioral experiment. The basis for this …

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 …

Synchronization Of Coupled Neurons Via Robust Feedback, Hector Puebla, Ricardo Aguilar-Lopez, Priti Kumar Roy

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

The Impact Of Cortical State On Neural Coding And Behavior, Charles Beaman

Dissertations and Theses (Open Access)

The brain is never truly silent – up to 80% of its energy budget is expended during ongoing activity in the absence of sensory input. Previous research has shown that sensory neurons are not exclusively influenced by external stimuli but rather reflect interactions between sensory inputs and the ongoing activity of the brain. Yet, whether fluctuations in the state of cortical networks influence sensory coding in neural circuits and the behavior of the animal are unknown. To shed light on this issue, we conducted multi-unit electrophysiology experiments in visual areas V1 and V4 of behaving monkeys. First, we studied the …

Wilson-Cowan Coupled Dynamics In A Model Of The Cortico-Striato-Thalamo-Cortical Circuit, Anca R. Radulescu

Biology and Medicine Through Mathematics Conference

No abstract provided.

Virtual V1sion: A Collaborative Coding Project, Cheryl Olman

MODVIS Workshop

Virtual V1sion is a new idea for fostering modeling collaborations and data sharing. While still in its infancy, the ultimate goal is a website that hosts repositories for (1) interchangeable model elements, (2) datasets that can be fit/predicted by those models, and (3) educational modules that explain the background for both the models and the datasets. The scope of the modeling is limited to predictions of V1 responses, although not all computations represented by model elements in Virtual V1sion are required to be V1-intrinsic: a goal of the project is to provide a framework in which predictions for modulation by …

Failure Of Surface Color Cues Under Natural Changes In Lighting, David H. Foster, Iván Marín-Franch

MODVIS Workshop

Color allows us to effortlessly discriminate and identify surfaces and objects by their reflected light. Although the reflected spectrum changes with the illumination spectrum, cone photoreceptor signals can be transformed to give useful cues for surface color. But what happens when both the spectrum and the geometry of the illumination change, as with lighting from the sun and sky? Is it possible, as a matter of principle, to obtain reliable cues by processing cone signals alone? This question was addressed here by estimating the information provided by cone signals from time-lapse hyperspectral radiance images of five outdoor scenes under natural …

A Geometric Approach To Sparse Coding Yields Insight Into Nonlinear Responses, Kedarnath Vilankar, James Golden, David Field

MODVIS Workshop

In artificial and biological networks, it is a common accepted practice to describe a neurons (biological or artificial) response properties by a two-dimensional feature map (receptive field). However, real neurons have nonlinear response properties which are not represented by their receptive fields. The efficient coding mechanisms such as sparse coding network or ICA, learn the response properties of V1 neurons from natural images using neural networks. These networks learn the receptive fields which are similar to the receptive fields of V1 neurons. These networks also produces some of the nonlinearities (such as end-stopping and non-classical surround effect), which are exhibited …

Network Dynamics Of Visual Object Recognition, Mehmet C. Kadipasaoglu

Dissertations and Theses (Open Access)

Visual object recognition is the principal mechanism by which humans and many animals interpret their surroundings. Despite the complexity of neural computation required, object recognition is achieved with such rapidity and accuracy that it appears to us almost effortless. Extensive human and non-human primate research has identified putative category-selective regions within higher-level visual cortex, which are thought to mediate object recognition. Despite decades of study, however, the functional organization and network dynamics within these regions remain poorly understood, due to a lack of appropriate animal models as well as the spatiotemporal limitations of current non-invasive human neuroimaging techniques (e.g. fMRI, …

“My Logic Is Undeniable”: Replicating The Brain For Ideal Artificial Intelligence, Samuel C. Adams

Senior Honors Theses

Alan Turing asked if machines can think, but intelligence is more than logic and reason. I ask if a machine can feel pain or joy, have visions and dreams, or paint a masterpiece. The human brain sets the bar high, and despite our progress, artificial intelligence has a long way to go. Studying neurology from a software engineer’s perspective reveals numerous uncanny similarities between the functionality of the brain and that of a computer. If the brain is a biological computer, then it is the embodiment of artificial intelligence beyond anything we have yet achieved, and its architecture is advanced …

Characterizing Receptive Field Selectivity In Area V2, Corey M. Ziemba, Robbe Lt Goris, J Anthony Movshon, Eero P. Simoncelli

MODVIS Workshop

The computations performed by neurons in area V1 are reasonably well understood, but computation in subsequent areas such as V2 have been more difficult to characterize. When stimulated with visual stimuli traditionally used to investigate V1, such as sinusoidal gratings, V2 neurons exhibit similar selectivity (but with larger receptive fields, and weaker responses) relative to V1 neurons. However, we find that V2 responses to synthetic stimuli designed to produce naturalistic patterns of joint activity in a model V1 population are more vigorous than responses to control stimuli that lacked this naturalistic structure (Freeman, et. al. 2013). Armed with this signature …

‘Edge’ Integration Explains Contrast And Assimilation In A Gradient Lightness Illusion, Michael E. Rudd

MODVIS Workshop

In the ‘phantom’ illusion (Galmonte, Soranzo, Rudd, & Agostini, submitted), either an incremental or a decremental target, when surrounded by a luminance gradient, can to be made to appear as an increment or a decrement, depending on the gradient width. For wide gradients, incremental targets appear as increments and decremental targets appear as decrements. For narrow gradients, the reverse is true. Here, I model these phenomena with a two-stage neural lightness theory (Rudd, 2013, 2014) in which local steps in log luminance are first encoded by oriented spatial filters operating on a log-transformed version of the image; then the filter …

Towards A Unified Computational Model Of Contextual Interactions Across Visual Modalities, David A. Mély, Thomas Serre

MODVIS Workshop

The perception of a stimulus is largely determined by its surrounding. Examples abound from color (Land and McCann, 1971), disparity (Westheimer, 1986) and motion induction (Anstis and Casco, 2006) to orientation tilt effects (O’Toole and Wenderoth, 1976). Some of these phenomena have been studied individually using monkey neurophysiology techniques. In these experiments, a center stimulus is typically used to probe a cell’s classical “center” receptive field (cRF), whose activity is then modulated by an annular “surround” (extra-cRF) stimulus. While this center-surround integration (CSI) has been well characterized, a theoretical framework which unifies these different phenomena across visual modalities is lacking. …

A Binocular Model For Motion Integration In Mt Neurons, Pamela M. Baker, Wyeth Bair

MODVIS Workshop

Processing of visual motion by neurons in MT has long been an active area of study, however circuit models detailing the computations underlying binocular integration of motion signals remains elusive. Such models are important for studying the visual perception of motion in depth (MID), which involves both frontoparallel (FP) visual motion and binocular signal integration. Recent studies (Czuba et al. 2014, Sanada and DeAngelis 2014) have shown that many MT neurons are MID sensitive, contrary to the prevailing view (Maunsell and van Essen, 1983). These novel data are ideal for constraining models of binocular motion integration in MT. We have …

Modeling Shape Representation In Area V4, Wyeth Bair, Dina Popovkina, Abhishek De, Anitha Pasupathy

MODVIS Workshop

Our model builds on a convolutional-style neural network with hierarchical stages representing processing steps in the ventral visual pathway. It was designed to capture the translation-invariance and shape-selectivity of neurons in area V4. The model uses biologically plausible linear filters at the front end, normalization and sigmoidal nonlinear activation functions. The max() function is used to generate translation invariance.

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 …

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 …

Pathological Effects Of Repeated Concussive Tbi In Mouse Models: Periventricular Damage And Ventriculomegaly, Richard H. Wolferz Jr.

Honors Scholar Theses

Repeated concussive traumatic brain injury (rcTBI) is the most prominent form of head injury affecting the brain, with an estimated 1.7 million Americans affected each year (Kuhn 2012). Neurologists have been concerned about the danger of repeated head impacts since the 1920’s, but researchers have only begun to understand the long-term effects of rcTBI (McKee 2009). Although symptoms can be as mild as dizziness, current research suggests that multiple concussions can lead to a progressive degenerative brain disease known as chronic traumatic encephalopathy (CTE) (Luo 2008, McKee 2009, Kane 2013). Research on the brain is just beginning to scratch the …

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 …