Yoonsuck Choe

Computational Maps in the Visual Cortex

Biological Background.- Computational Foundations.- LISSOM: A Computational Map Model of V1.- Development of Maps and Connections.- Understanding Plasticity.- Understanding Visual Performance: The Tilt Aftereffect.- HLISSOM: A Hierarchical Model.- Understanding Low-Level Development: Orientation Maps.- Understanding High-Level Development: Face Detection.- PGLISSOM: A Perceptual Grouping Model.- Temporal Coding.- Understanding Perceptual Grouping: Contour Integration.- Computations in Visual Maps.- Scaling LISSOM simulations.- Discussion: Biological Assumptions and Predictions.- Future Work: Computational Directions.- Conclusion.

Self-Organization and Segmentation in a Laterally Connected Orientation Map of Spiking Neurons

The RF-SLISSOM model integrates two separate lines of research on computational modeling of the visual cortex. Laterally connected self-organizing maps have been used to model how afferent structures such as orientation columns and patterned lateral connections can simultaneously self-organize through input-driven Hebbian adaptation. Spiking neurons with leaky integrator synapses have been used to model image segmentation and binding by synchronization and desynchronization of neuronal group activity. Although these approaches differ in how they model the neuron and what they explain, they share the same overall layout of a laterally connected two-dimensional network. This paper shows how both self-organization and segmentation can be achieved in such an integrated network, thus presenting a unified model of development and functional dynamics in the primary visual cortex.

Self-Organization, Plasticity, and Low-Level Visual Phenomena in a Laterally Connected Map Model of the Primary Visual Cortex

Based on a Hebbian adaptation process, the afferent and lateral connections in the RF-LISSOM model organize simultaneously and cooperatively, and form structures such as those observed in the primary visual cortex. The neurons in the model develop local receptive fields that are organized into orientation, ocular dominance, and size selectivity columns. At the same time, patterned lateral connections form between neurons that follow the receptive field organization. This structure is in a continuously-adapting dynamic equilibrium with the external and intrinsic input, and can account for reorganization of the adult cortex following retinal and cortical lesions. The same learning processes may be responsible for a number of low-level functional phenomena such as tilt aftereffects, and combined with the leaky integrator model of the spiking neuron, for segmentation and binding. The model can also be used to verify quantitatively the hypothesis that the visual cortex forms a sparse, redundancy-reduced encoding of the input, which allows it to process massive amounts of visual information efficiently.

Contour integration and segmentation with self-organized lateral connections

Abstract.Contour integration in low-level vision is believed to occur based on lateral interaction between neurons with similar orientation tuning. How such interactions could arise in the brain has been an open question. Our model suggests that the interactions can be learned through input-driven self-organization, i.e., through the same mechanism that underlies many other developmental and functional phenomena in the visual cortex. The model also shows how synchronized firing mediated by these lateral connections can represent the percept of a contour, resulting in performance similar to that of human contour integration. The model further demonstrates that contour integration performance can differ in different parts of the visual field, depending on what kinds of input distributions they receive during development. The model thus grounds an important perceptual phenomenon onto detailed neural mechanisms so that various structural and functional properties can be measured and predictions can be made to guide future experiments.

Editorial IEEE Transactions on Neural Networks and Learning Systems 2016 and Beyond

“Happy New Year!” At the beginning of 2016, I would like to take this opportunity to wish everyone a very happy, healthy, and prosperous new year! It is my great honor and privilege to serve as the Editor-in-Chief (EiC) of the IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (TNNLS), and I am excited to write this Editorial to start a new journey with you all.

Cell tracking and segmentation in electron microscopy images using graph cuts

Understanding neural connectivity and structures in the brain requires detailed 3D anatomical models, and such an understanding is essential to the study of the nervous system. However, the reconstruction of 3D models from a large set of dense nanoscale medical images is very challenging, due to the imperfections in staining and noise in the imaging process. Manual segmentation in 2D followed by tracking the 2D contours through cross-sections to build 3D structures can be a solution, but it is impractical. In this paper, we propose an automated tracking and segmentation framework to extract 2D contours and to trace them through the z direction. The segmentation is posed as an energy minimization problem and solved via graph cuts. The energy function to be minimized contains a regional term and a boundary term. The regional term is defined over the flux of the gradient vector fields and the distance function. Our main idea is that the distance function should carry the information of the segmentation from the previous image based on the assumption that successive images have a similar segmentation. The boundary term is defined over the gray-scale intensity of the image. Experiments were conducted on nanoscale image sequences from the Serial Block Face Scanning Electron Microscope (SBF-SEM). The results show that our method can successfully track and segment densely packed cells in EM image stacks.

AUTONOMOUS LEARNING OF THE SEMANTICS OF INTERNAL SENSORY STATES BASED ON MOTOR EXPLORATION

What is available to developmental programs in autonomous mental development, and what should be learned at the very early stages of mental development? Our observation is that sensory and motor primitives are the most basic components present at the beginning, and what developmental agents need to learn from these resources is what their internal sensory states stand for. In this paper, we investigate the question in the context of a simple biologically motivated visuomotor agent. We observe and acknowledge, as many other researchers do, that action plays a key role in providing content to the sensory state. We propose a simple, yet powerful learning criterion, that of invariance, where invariance simply means that the internal state does not change over time. We show that after reinforcement learning based on the invariance criterion, the property of action sequence based on an internal sensory state accurately reflects the property of the stimulus that triggered that internal state. That way, the meaning of the internal sensory state can be firmly grounded on the property of that particular action sequence. We expect the framing of the problem and the proposed solution presented in this paper to help shed new light on autonomous understanding in developmental agents such as humanoid robots.

A Microchip for Quantitative Analysis of CNS Axon Growth under Localized Biomolecular Treatments

Growth capability of neurons is an essential factor in axon regeneration. To better understand how microenvironments influence axon growth, methods that allow spatial control of cellular microenvironments and easy quantification of axon growth are critically needed. Here, we present a microchip capable of physically guiding the growth directions of axons while providing physical and fluidic isolation from neuronal somata/dendrites that enables localized biomolecular treatments and linear axon growth. The microchip allows axons to grow in straight lines inside the axon compartments even after the isolation; therefore, significantly facilitating the axon length quantification process. We further developed an image processing algorithm that automatically quantifies axon growth. The effect of localized extracellular matrix components and brain-derived neurotropic factor treatments on axon growth was investigated. Results show that biomolecules may have substantially different effects on axon growth depending on where they act. For example, while chondroitin sulfate proteoglycan causes axon retraction when added to the axons, it promotes axon growth when applied to the somata. The newly developed microchip overcomes limitations of conventional axon growth research methods that lack localized control of biomolecular environments and are often performed at a significantly lower cell density for only a short period of time due to difficulty in monitoring of axonal growth. This microchip may serve as a powerful tool for investigating factors that promote axon growth and regeneration.

Structural systems identification of genetic regulatory networks

MOTIVATION Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit. RESULTS In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints. AVAILABILITY The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zaks data is available from his website, http://www.che.udel.edu/systems/people/zak.

Modeling cortical maps with Topographica

The biological function of cortical neurons can often be understood only in the context of large, highly interconnected networks. These networks typically form two-dimensional topographic maps, such as the retinotopic maps in the visual system. Computational simulations of these areas have led to valuable insights about how cortical topography develops and functions, but further progress is difficult because appropriate simulation tools are not available. This paper introduces the freely available Topographica map-level simulator, currently under development at the University of Texas at Austin. Topographica is designed to make large-scale, detailed models practical. The goal is to allow neuroscientists and computational scientists to understand how topographic maps and their connections organize and operate. This understanding will be crucial for integrating experimental observations into a comprehensive theory of cortical function.