Daniel J. Felleman

Bio: Daniel J. Felleman is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Visual cortex & Cortex (anatomy). The author has an hindex of 26, co-authored 41 publications receiving 13348 citations. Previous affiliations of Daniel J. Felleman include Salk Institute for Biological Studies & Washington University in St. Louis.

Distributed Hierarchical Processing in the Primate Cerebral Cortex

Abstract: In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31% of the possible number of pathways if each area were connected with all others. The actual degree of connectivity is likely to be closer to 40%. The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somatosensory/motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainities in the reported assignment.

Information Processing in the Primate Visual System: An Integrated Systems Perspective

Abstract: The primate visual system contains dozens of distinct areas in the cerebral cortex and several major subcortical structures. These subdivisions are extensively interconnected in a distributed hierarchical network that contains several intertwined processing streams. A number of strategies are used for efficient information processing within this hierarchy. These include linear and nonlinear filtering, passage through information bottlenecks, and coordinated use of multiple types of information. In addition, dynamic regulation of information flow within and between visual areas may provide the computational flexibility needed for the visual system to perform a broad spectrum of tasks accurately and at high resolution.

Representations of the body surface in postcentral parietal cortex of Macaca fascicularis

Abstract: The somatotopic organization of the postcentral parietal cortex of the Old World monkey, Macaca fascicularis, was determined with multi-unit microelectrode recordings. The results lead to the following conclusions: 1) There are at least two complete and systematic representations of the contralateral body surface in the cortex of the postcentral gyrus. One representation is contained within Area 3b, the other within Area 1. 2) While there are important differences in the organization of the two representations, they are basically mirror-images of each other. 3) Each representation maintains body-surface adjacency by cortical adjacency in some mediolateral regions. In other regions, two types of discontinuities can be described: first, in which adjacent body surfaces are represented in separate cortical loci; second, in which adjacent cortical regions represent disparate body-surface regions. The internal organization of each representation is better described as a composite of somatotopic regions (Merzenich et al., '78) than as a serial array of dermatomal bands, or as a "homunculus." 4) While architectonic Area 2 responds to stimulation of deep body tissue, at least parts of Area 2 also respond to cutaneous stimulation. The organiation of the cutaneous representation of the hand in Area 2 is basically a mirror-image of the hand representation in Area 1. 5) Area 3a is activated by deep body tissue stimulation, suggesting the possibility of a fourth body representation within the traditional "S-I" region of somatosensory cortex in macaques. In accord with a previous study in a New World monkey (Merzenich et al., '78), we suggest that the cutaneous representation in Area 3b be considered as SI proper, and that the cutaneous representation in Area 1 be termed the posterior cutaneous field. Furthermore, based on the orientation of the representations of the body surface, as well as other factors, we suggest that the representation in Area 3b is homologous to "SmI" (or "SI") in non-primates.

Deep learning

Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Deep Learning

Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Complex brain networks: graph theoretical analysis of structural and functional systems

Abstract: Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.