Towards a Unified Theory of Neocortex: Laminar Cortical Circuits for Vision and Cognition

A key goal of computational neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how laminar neocortical circuits give rise to biological intelligence. These circuits embody two new and revolutionary computational paradigms: Complementary Computing and Laminar Computing. Circuit properties include a novel synthesis of feedforward and feedback processing, of digital and analog processing, and of pre-attentive and attentive processing. This synthesis clarifies the appeal of Bayesian approaches but has a far greater predictive range that naturally extends to self-organizing processes. Examples from vision and cognition are summarized. A LAMINART architecture unifies properties of visual development, learning, perceptual grouping, attention, and 3D vision. A key modeling theme is that the mechanisms which enable development and learning to occur in a stable way imply properties of adult behavior. It is noted how higher-order attentional constraints can influence multiple cortical regions, and how spatial and object attention work together to learn view-invariant object categories. In particular, a form-fitting spatial attentional shroud can allow an emerging view-invariant object category to remain active while multiple view categories are associated with it during sequences of saccadic eye movements. Finally, the chapter summarizes recent work on the LIST PARSE model of cognitive information processing by the laminar circuits of prefrontal cortex. LIST PARSE models the short-term storage of event sequences in working memory, their unitization through learning into sequence, or list, chunks, and their read-out in planned sequential performance that is under volitional control. LIST PARSE provides a laminar embodiment of Item and Order working memories, also called Competitive Queuing models, that have been supported by both psychophysical and neurobiological data. These examples show how variations of a common laminar cortical design can embody properties of visual and cognitive intelligence that seem, at least on the surface, to be mechanistically unrelated.

Working Memory Networks for Learning Multiple Groupings of Temporarily Ordered Events: Applications to 3-D Visual Object Recognition

Working memory neural networks are characterized which encode the invariant temporal order of sequential events that may be presented at widely differing speeds, durations, and interstimulus intervals. This temporal order code is designed to enable all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such a competence is needed in neural architectures which self-organize learned codes for variable-rate speech perception, sensory-motor planning, or 3-D visual object recognition. Using such a working memory, a self-organizing architecture for invariant 3-D visual object recognition is described that is based on the model of Seibert and Waxman [1].

ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation.

A Neural Network Architecture for Figure-ground Separation of Connected Scenic Figures

A neural network model, called an FBF network, is proposed for automatic parallel separation of multiple image figures from each other and their backgrounds in noisy grayscale or multi-colored images. The figures can then be processed in parallel by an array of self-organizing Adaptive Resonance Theory (ART) neural networks for automatic target recognition. An FBF network can automatically separate the disconnected but interleaved spirals that Minsky and Papert introduced in their book Perceptrons. The network's design also clarifies why humans cannot rapidly separate interleaved spirals, yet can rapidly detect conjunctions of disparity and color, or of disparity and motion, that distinguish target figures from surrounding distractors. Figure-ground separation is accomplished by iterating operations of a Feature Contour System (FCS) and a Boundary Contour System (BCS) in the order FCS-BCS-FCS, hence the term FBF, that have been derived from an analysis of biological vision. The FCS operations include the use of nonlinear shunting networks to compensate for variable illumination and nonlinear diffusion networks to control filling-in. A key new feature of an FBF network is the use of filling-in for figure-ground separation. The BCS operations include oriented filters joined to competitive and cooperative interactions designed to detect, regularize, and complete boundaries in up to 50 percent noise, while suppressing the noise. A modified CORT-X filter is described which uses both on-cells and off-cells to generate a boundary segmentation from a noisy image.

Working Memory Networks for Learning Temporal Order, with Application to 3-D Visual Object Recognition

Working memory neural networks are characterized which encode the invariant temporal order of sequential events. Inputs to the networks, called Sustained Temporal Order REcurrent (STORE) models, may be presented at widely differing speeds, durations, and interstimulus intervals. The STORE temporal order code is designed to enable all emergent groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such a competence is needed in neural architectures which self-organize learned codes for variable-rate speech perception, sensory-motor planning, or 3-D visual object recognition. Using such a working memory, a self-organizing architecture for invariant 3-D visual object recognition is described. The new model is based on the model of Seibert and Waxman (1990a), which builds a 3-D representation of an object from a temporally ordered sequence of its 2-D aspect graphs. The new model, called an ARTSTORE model, consists of the following cascade of processing modules: Invariant Preprocessor --> ART 2 --> STORE Model --> ART 2 --> Outstar Network.

A Neural Network Model of Spatio-Temporal Pattern Recognition, Recall and Timing

This paper describes the design of a self~organizing, hierarchical neural network model of unsupervised serial learning. The model learns to recognize, store, and recall sequences of unitized patterns, using either short-term memory (STM) or both STM and long-term memory (LTM) mechanisms. Timing information is learned and recall {both from STM and from LTM) is performed with a learned rhythmical structure. The network, bearing similarities with ART (Carpenter & Grossberg 1987a), learns to map temporal sequences to unitized patterns, which makes it suitable for hierarchical operation. It is therefore capable of self-organizing codes for sequences of sequences. The capacity is only limited by the number of nodes provided. Selected simulation results are reported to illustrate system properties.

Store Working Memory Networks for Storage and Recall of Arbitrary Temporal Sequences

Neural network models of working memory, called Sustained Temporal Order REcurrent (STORE) models, are described. They encode the invariant temporal order of sequential events in short term memory (STM) in a way that mimics cognitive data about working memory, including primacy, recency, and bowed order and error gradients. As new items are presented, the pattern of previously stored items is invariant in the sense that, relative activations remain constant through time. This invariant temporal order code enables all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such a competence is needed to design self-organizing temporal recognition and planning systems in which any subsequence of events may need to be categorized in order to to control and predict future behavior or external events. STORE models show how arbitrary event sequences may be invariantly stored, including repeated events. A preprocessor interacts with the working memory to represent event repeats in spatially separate locations. It is shown why at least two processing levels are needed to invariantly store events presented with variable durations and interstimulus intervals. It is also shown how network parameters control the type and shape of primacy, recency, or bowed temporal order gradients that will be stored.

Fibrose pulmonaire idiopathique

SCOPUS: ar.j

A MEMETIC/PARTICIPATORY APPROACH FOR CHANGING SOCIAL BEHAVIORS AND PROMOTING ENVIRONMENTAL STEWARDSHIP IN JALISCO, MEXICO

This dissertation addressed the issue of sustainable development at the level of individual behaviors. Environmental perceptions were obtained from people living around the biosphere reserve Chamela-Cuixmala in Jalisco, Mexico. Several environmental issues were identified by the people, such as garbage and grey water on the streets, burning plastics, and the lack of usage of recreational areas. All these issues could be addressed with a change in behavior by the villagers. Familiarization activities were conducted to gain people's trust in order to conduct a community forum. These activities included giving talks to school children and organizing workshops. Four different methodologies were generated using memetics and participation to test which would ameliorate those environmental issues identified by the people through a change in behavior. The methodologies were 1) Memes; 2) Participation and Memes; 3) Participation; 4) Neither Participation nor Memes. A meme is an idea expressed within a linguistic structure or architecture that provides it with self-disseminating and self-protecting characteristics within and among the minds of individuals congruent with their values, beliefs and filters. Four villages were chosen as the treatments, and one as the control, for a total of five experimental villages. A different behavior was addressed in each treatment village (garbage, grey-water, burning plastics, recreation.) A nonequivalent control-group design was established. A pretest was conducted in all five villages; the methodologies were tested in the four treatment villages; a posttest was conducted on the five villages. The pretest and posttest consisted in measuring sensory specific indicators which are manifestations of behavior that can either be seen, smelled, touched, heard or tasted. Statistically significant differences in behavior from the control were found for two of the methodologies 1) Memes (p=0.0403) and 2) Participation and Memes (p=0.0064). For the methodologies of 3) Participation alone and 4) Neither, the differences were not significant (p=0.8827, p=0.5627 respectively). When using memes, people's behavior improved when compared to the control. Participation alone did not generate a significant difference. Participation aided in the generation of the memes. Memetics is a tool that can be used to establish a linkage between human behavior and ecological health.

Neural network analysis identifies scaffold properties necessary for in vitro chondrogenesis in elastin-like polypeptide biopolymer scaffolds.

The successful design of biomaterial scaffolds for articular cartilage tissue engineering requires an understanding of the impact of combinations of material formulation parameters on diverse and competing functional outcomes of biomaterial performance. This study sought to explore the use of a type of unsupervised artificial network, a self-organizing map, to identify relationships between scaffold formulation parameters (crosslink density, molecular weight, and concentration) and 11 such outcomes (including mechanical properties, matrix accumulation, metabolite usage and production, and histological appearance) for scaffolds formed from crosslinked elastin-like polypeptide (ELP) hydrogels. The artificial neural network recognized patterns in functional outcomes and provided a set of relationships between ELP formulation parameters and measured outcomes. Mapping resulted in the best mean separation amongst neurons for mechanical properties and pointed to crosslink density as the strongest predictor of most outcomes, followed by ELP concentration. The map also grouped formulations together that simultaneously resulted in the highest values for matrix production, greatest changes in metabolite consumption or production, and highest histological scores, indicating that the network was able to recognize patterns amongst diverse measurement outcomes. These results demonstrated the utility of artificial neural network tools for recognizing relationships in systems with competing parameters, toward the goal of optimizing and accelerating the design of biomaterial scaffolds for articular cartilage tissue engineering.

Regulation of G protein-coupled receptor signaling by scaffold proteins.

The actions of many hormones and neurotransmitters are mediated through stimulation of G protein-coupled receptors. A primary mechanism by which these receptors exert effects inside the cell is by association with heterotrimeric G proteins, which can activate a wide variety of cellular enzymes and ion channels. G protein-coupled receptors can also interact with a number of cytoplasmic scaffold proteins, which can link the receptors to various signaling intermediates and intracellular effectors. The multicomponent nature of G protein-coupled receptor signaling pathways makes them ideally suited for regulation by scaffold proteins. This review focuses on several specific examples of G protein-coupled receptor-associated scaffolds and the roles they may play in organizing receptor-initiated signaling pathways in the cardiovascular system and other tissues.

Leptospirosis among hospitalized febrile patients in northern Tanzania.

We enrolled consecutive febrile admissions to two hospitals in Moshi, Tanzania. Confirmed leptospirosis was defined as a ≥ 4-fold increase in microscopic agglutination test (MAT) titer; probable leptospirosis as reciprocal MAT titer ≥ 800; and exposure to pathogenic leptospires as titer ≥ 100. Among 870 patients enrolled in the study, 453 (52.1%) had paired sera available, and 40 (8.8%) of these met the definition for confirmed leptospirosis. Of 832 patients with ≥ 1 serum sample available, 30 (3.6%) had probable leptospirosis and an additional 277 (33.3%) had evidence of exposure to pathogenic leptospires. Among those with leptospirosis the most common clinical diagnoses were malaria in 31 (44.3%) and pneumonia in 18 (25.7%). Leptospirosis was associated with living in a rural area (odds ratio [OR] 3.4, P < 0.001). Among those with confirmed leptospirosis, the predominant reactive serogroups were Mini and Australis. Leptospirosis is a major yet underdiagnosed cause of febrile illness in northern Tanzania, where it appears to be endemic.

Biological and ecological mechanisms supporting marine self-governance: The Seri Callo de Hacha Fishery in Mexico

My goal was to describe how biological and ecological factors give shape to fishing practices that can contribute to the successful self-governance of a small-scale fishing system in the Gulf of California, Mexico. The analysis was based on a comparison of the main ecological and biological indicators that fishers claim to use to govern their day-to-day decision making about fishing and data collected in situ. I found that certain indicators allow fishers to learn about differences and characteristics of the resource system and its units. Fishers use such information to guide their day-to-day fishing decisions. More importantly, these decisions appear unable to shape the reproductive viability of the fishery because no indicators were correlated to the reproductive cycle of the target species. As a result, the fishing practices constitute a number of mechanisms that might provide short-term buffering capacity against perturbations or stress factors that otherwise would threaten the overall sustainability and self-governance of the system. The particular biological circumstances that shape the harvesting practices might also act as a precursor of self-governance because they provide fishers with enough incentives to meet the costs of organizing the necessary rule structure that underlies a successful self-governance system.

Discharge competence and pattern formation in peatlands: a meta-ecosystem model of the Everglades ridge-slough landscape.

Regular landscape patterning arises from spatially-dependent feedbacks, and can undergo catastrophic loss in response to changing landscape drivers. The central Everglades (Florida, USA) historically exhibited regular, linear, flow-parallel orientation of high-elevation sawgrass ridges and low-elevation sloughs that has degraded due to hydrologic modification. In this study, we use a meta-ecosystem approach to model a mechanism for the establishment, persistence, and loss of this landscape. The discharge competence (or self-organizing canal) hypothesis assumes non-linear relationships between peat accretion and water depth, and describes flow-dependent feedbacks of microtopography on water depth. Closed-form model solutions demonstrate that 1) this mechanism can produce spontaneous divergence of local elevation; 2) divergent and homogenous states can exhibit global bi-stability; and 3) feedbacks that produce divergence act anisotropically. Thus, discharge competence and non-linear peat accretion dynamics may explain the establishment, persistence, and loss of landscape pattern, even in the absence of other spatial feedbacks. Our model provides specific, testable predictions that may allow discrimination between the self-organizing canal hypotheses and competing explanations. The potential for global bi-stability suggested by our model suggests that hydrologic restoration may not re-initiate spontaneous pattern establishment, particularly where distinct soil elevation modes have been lost. As a result, we recommend that management efforts should prioritize maintenance of historic hydroperiods in areas of conserved pattern over restoration of hydrologic regimes in degraded regions. This study illustrates the value of simple meta-ecosystem models for investigation of spatial processes.

Nucleolar organization, ribosomal DNA array stability, and acrocentric chromosome integrity are linked to telomere function.

The short arms of the ten acrocentric human chromosomes share several repetitive DNAs, including ribosomal RNA genes (rDNA). The rDNA arrays correspond to nucleolar organizing regions that coalesce each cell cycle to form the nucleolus. Telomere disruption by expressing a mutant version of telomere binding protein TRF2 (dnTRF2) causes non-random acrocentric fusions, as well as large-scale nucleolar defects. The mechanisms responsible for acrocentric chromosome sensitivity to dysfunctional telomeres are unclear. In this study, we show that TRF2 normally associates with the nucleolus and rDNA. However, when telomeres are crippled by dnTRF2 or RNAi knockdown of TRF2, gross nucleolar and chromosomal changes occur. We used the controllable dnTRF2 system to precisely dissect the timing and progression of nucleolar and chromosomal instability induced by telomere dysfunction, demonstrating that nucleolar changes precede the DNA damage and morphological changes that occur at acrocentric short arms. The rDNA repeat arrays on the short arms decondense, and are coated by RNA polymerase I transcription binding factor UBF, physically linking acrocentrics to one another as they become fusogenic. These results highlight the importance of telomere function in nucleolar stability and structural integrity of acrocentric chromosomes, particularly the rDNA arrays. Telomeric stress is widely accepted to cause DNA damage at chromosome ends, but our findings suggest that it also disrupts chromosome structure beyond the telomere region, specifically within the rDNA arrays located on acrocentric chromosomes. These results have relevance for Robertsonian translocation formation in humans and mechanisms by which acrocentric-acrocentric fusions are promoted by DNA damage and repair.