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In JoVE (1)
Other Publications (11)
- Biological Cybernetics
- IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council
- Neural Computation
- Neurocomputing
- Biological Cybernetics
- Bioinformatics (Oxford, England)
- BMC Systems Biology
- IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council
- Neural Networks : the Official Journal of the International Neural Network Society
- Biomedical Optics Express
- Frontiers in Neuroinformatics
Articles by Yoonsuck Choe in JoVE
JoVE Bioengineering
Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy
1Department of Computer Science and Engineering, Texas A&M University, 2Beckman Institute for Advanced Science and Technology, University of Illinois, 3Department of Electrical and Computer Engineering, Kettering University, 43Scan, 5Department of Veterinary Integrative Biosciences, Texas A&M University
The full process from brain specimen preparation to serial sectioning imaging using the Knife-Edge Scanning Microscope, to data visualization and analysis is described. This technique is currently used to acquire mouse brain data, but it is applicable to other organs, other species.
Other articles by Yoonsuck Choe on PubMed
Contour Integration and Segmentation with Self-organized Lateral Connections
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.
The Role of Temporal Parameters in a Thalamocortical Model of Analogy
How multiple specialized cortical areas in the brain interact with each other to give rise to an integrated behavior is a largely unanswered question. This paper proposes that such an integration can be understood under the framework of analogy and that part of the thalamus and the thalamic reticular nucleus (TRN) may be playing a key role in this respect. The proposed thalamocortical model of analogy heavily depends on a diverse set of temporal parameters including axonal delay and membrane time constant, each of which is critical for the proper functioning of the model. The model requires a specific set of conditions derived from the need of the model to process analogies. Computational results with a network of integrate and fire (IF) neurons suggest that these conditions are indeed necessary, and furthermore, data found in the experimental literature also support these conditions. The model suggests that there is a very good reason for each temporal parameter in the thalamocortical network having a particular value, and that to understand the integrated behavior of the brain, we need to study these parameters simultaneously, not separately.
A Neural Model of the Scintillating Grid Illusion: Disinhibition and Self-inhibition in Early Vision
A stationary display of white discs positioned on intersecting gray bars on a dark background gives rise to a striking scintillating effect--the scintillating grid illusion. The spatial and temporal properties of the illusion are well known, but a neuronal-level explanation of the mechanism has not been fully investigated. Motivated by the neurophysiology of the Limulus retina, we propose disinhibition and self-inhibition as possible neural mechanisms that may give rise to the illusion. In this letter, a spatiotemporal model of the early visual pathway is derived that explicitly accounts for these two mechanisms. The model successfully predicted the change of strength in the illusion under various stimulus conditions, indicating that low-level mechanisms may well explain the scintillating effect in the illusion.
Segmentation of Textures Defined on Flat Vs. Layered Surfaces Using Neural Networks: Comparison of 2D Vs. 3D Representations
Texture boundary detection (or segmentation) is an important capability in human vision. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct (i.e., occluded) surfaces. Hence, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this paper, we conducted computational experiments with artificial neural networks to investigate the relative difficulty of learning to segment textures defined on flat 2D surfaces vs. those in 3D configurations where the boundaries are defined by occluding surfaces and their change over time due to the observer's motion. It turns out that learning is faster and more accurate in 3D, very much in line with our expectation. Furthermore, our results showed that the neural network's learned ability to segment texture in 3D transfers well into 2D texture segmentation, bolstering our initial hypothesis, and providing insights on the possible developmental origin of 2D texture segmentation function in human vision.
Neural Model of Disinhibitory Interactions in the Modified Poggendorff Illusion
Visual illusions can be strengthened or weakened with the addition of extra visual elements. For example, in the Poggendorff illusion, with an additional bar added, the illusory skew in the perceived angle can be enlarged or reduced. In this paper, we show that a nontrivial interaction between lateral inhibitory processes in the early visual system (i.e., disinhibition) can explain such an enhancement or degradation of the illusory effect. The computational model we derived successfully predicted the perceived angle in the Poggendorff illusion task that was modified to include an extra thick bar. The concept of disinhibition employed in the model is general enough that we expect it can be further extended to account for other classes of geometric illusions.
Structural Systems Identification of Genetic Regulatory Networks
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.
Dynamical Pathway Analysis
Although a great deal is known about one gene or protein and its functions under different environmental conditions, little information is available about the complex behaviour of biological networks subject to different environmental perturbations. Observing differential expressions of one or more genes between normal and abnormal cells has been a mainstream method of discovering pertinent genes in diseases and therefore valuable drug targets. However, to date, no such method exists for elucidating and quantifying the differential dynamical behaviour of genetic regulatory networks, which can have greater impact on phenotypes than individual genes.
Extrapolative Delay Compensation Through Facilitating Synapses and Its Relation to the Flash-lag Effect
Neural conduction delay is a serious issue for organisms that need to act in real time. Various forms of flash-lag effect (FLE) suggest that the nervous system may perform extrapolation to compensate for delay. For example, in motion FLE, the position of a moving object is perceived to be ahead of a brief flash when they are actually colocalized. However, the precise mechanism for extrapolation at a single-neuron level has not been fully investigated. Our hypothesis is that facilitating synapses, with their dynamic sensitivity to the rate of change in the input, can serve as a neural basis for extrapolation. To test this hypothesis, we constructed and tested models of facilitating dynamics. First, we derived a spiking neuron model of facilitating dynamics at a single-neuron level, and tested it in the luminance FLE domain. Second, the spiking neuron model was extended to include multiple neurons and spike-timing-dependent plasticity (STDP), and was tested with orientation FLE. The results showed a strong relationship between delay compensation, FLE, and facilitating synapses/STDP. The results are expected to shed new light on real time and predictive processing in the brain, at the single neuron level.
Facilitating Neural Dynamics for Delay Compensation: a Road to Predictive Neural Dynamics?
Goal-directed behavior is a hallmark of cognition. An important prerequisite to goal-directed behavior is that of prediction. In order to establish a goal and devise a plan, one needs to see into the future and predict possible future events. Our earlier work has suggested that compensation mechanisms for neuronal transmission delay may have led to a preliminary form of prediction. In that work, facilitating neuronal dynamics was found to be effective in overcoming delay (the Facilitating Activation Network model, or FAN). The extrapolative property of the delay compensation mechanism can be considered as prediction for incoming signals (predicting the present based on the past). The previous FAN model turns out to have a limitation especially when longer delay needs to be compensated, which requires higher facilitation rates than FAN's normal range. We derived an improved facilitating dynamics at the neuronal level to overcome this limitation. In this paper, we tested our proposed approach in controllers for 2D pole balancing, where the new approach was shown to perform better than the previous FAN model. Next, we investigated the differential utilization of facilitating dynamics in sensory vs. motor neurons and found that motor neurons utilize the facilitating dynamics more than the sensory neurons. These findings are expected to help us better understand the role of facilitating dynamics in delay compensation, and its potential development into prediction, a necessary condition for goal-directed behavior.
Fast Macro-scale Transmission Imaging of Microvascular Networks Using KESM
Accurate microvascular morphometric information has significant implications in several fields, including the quantification of angiogenesis in cancer research, understanding the immune response for neural prosthetics, and predicting the nature of blood flow as it relates to stroke. We report imaging of the whole mouse brain microvascular system at resolutions sufficient to perform accurate morphometry. Imaging was performed using Knife-Edge Scanning Microscopy (KESM) and is the first example of this technique that can be directly applied to clinical research. We are able to achieve ≈ 0.7μm resolution laterally with 1μm depth resolution using serial sectioning. No alignment was necessary and contrast was sufficient to allow segmentation and measurement of vessels.
Multiscale Exploration of Mouse Brain Microstructures Using the Knife-edge Scanning Microscope Brain Atlas
Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions.
