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This chapter focuses on dynamic coordination and how it is achieved, how it is related to cognitive functions and learning processes, and the role of neural oscillations in different frequency bands for dynamic coordination. It also considers modulation of coordination at the systems level and the relation of the mechanisms underlying coordination of behavior and cognition to neuropsychiatric disorders. In addition, the chapter examines dynamic coordination that arises from self-organization, the reduction in dimensionality associated with dynamic coordination, the theory of Coherent Infomax, Dynamic Pattern Theory, how optimization and synchrony are related to dynamic coordination, neural synchrony during human ontogeny, and how coordination contributes to perceptual and motor development. The chapter concludes with a discussion of the role of different frequency bands in sensory processing, attention and awareness, and motor circuits, together with how the prefrontal cortex modulates dynamic coordination.

This chapter focuses on dynamic coordination in the brain and mind and its major implications for the cognitive neurosciences. Coordinating interactions give rise to coherent and relevant patterns of activity without disrupting the essential individual identities and functions of the activities being coordinated. Dynamic coordination deals effectively with unpredictable aspects of the current situation. The chapter discusses how biological systems combine reliability and flexibility in nature before exploring a variety of computational goals for dynamic coordination and issues related to cognition, local cortical circuits, brain systems, and evolution. It also looks at dynamic coordination resulting from widely distributed processes of self-organization as well as the role of executive control. In addition, the chapter considers how holism is combined with localism in neuroscience.

A number of non-independent factors, such as neurotransmitter and receptor excesses and reductions, a reduced ability to generate oscillations and synchrony, and changes in gene expression due to primary genetic or environmental causes, may compromise dynamic coordination. This chapter focuses on brain disorders that have been linked to dynamic coordination failures, including autism, epilepsy, amblyopia, schizophrenia, Williams syndrome, Parkinson’s disease, and Alzheimer’s disease.

This chapter focuses on dynamic coordination, how it is mechanistically implemented in brain circuits, and the extent to which it accounts for functions performed by interacting brain systems. It also discusses how modulation of phase relationships of oscillations in different brain systems, in neocortex and hippocampus of the mammalian brain, may alter functional coupling; possible mechanisms for oscillation-based synchronization, particularly in the gamma frequency range; coordination by gain modulation and by synchronization of oscillation patterns; and the role of long-range projecting interneurons in synchronizing one neural circuit with another. Finally, the chapter considers how oscillations may influence dynamic routing within structurally constrained brain networks.

Evidence from the field of comparative cognition suggests that many mammal and bird species possess cognitive abilities, such as the use of episodic-like memory of past events to modify behavior flexibly and “understanding” physical properties. The convergent evolution of particular cognitive abilities in primates and corvids indicates that the type of coordination required for cognitive abilities has evolved at least twice. This chapter explores the link between comparative cognition and dynamic coordination in the brain. It looks at a number of recent examples of animal cognition and discusses the sort of coordination they require.

This chapter explores dynamic coordination in the primary sensory cortex of mammals during low-level (non-attention-related) perception. It considers the possible existence of subcortical or cortical supervisors in higher mammals and explains how coordination is generated by the sensory drive and amplified by built-in anisotropies in the network connectivity. Drawing on synaptic functional imaging (at the intracellular level) and real-time voltage-sensitive dye network imaging (at the functional map level), it illustrates the role of intracortical depolarizing waves whose functional features support the hypothesis of a dynamic association field. These waves help propagate synaptic modulation in space and time via lateral (and perhaps feedback) connectivity, which explains the emergence of illusions predicted by Gestalt theory. The chapter also explains how lateral propagation waves are reconstructed from synaptic echoes and looks at the propagation of orientation belief.

In order for perception to be effective, many noisy and ambiguous sensory signals across different modalities (for example, vision, audition) must be integrated into stable percepts. This chapter explores dynamic coordination in the integration of sensory information, with an emphasis on why and how the brain integrates sensory signals. It also considers how the brain knows when to integrate signals and when to treat them separately.

Comparative biology can offer important insights into the evolution of dynamic coordination in the brain. This chapter explores the neural machinery and computations shared by all nervous systems across the animal kingdom, taking into account the fact the complex relationships in coordination between nervous systems and the behavior they produce. It discusses the comparative approaches used to probe brain structure and function, and examines whether there are any fundamental “phase transitions” occurring across groups of organisms in the basic components that build neural circuits and in the kind of computations that these can perform. It also considers the fundamental unity of the functional aspects of neurons, neural circuits, and neural computations in animals such as mammals and birds. Finally, it explains how brain similarities and differences can be used to elucidate complex brain-behavior relationships.

Researchers in human cognitive neuroscience have long been trying to understand how thoughts can arise from billions of interconnected neurons. Despite the significant advances in human brain imaging over the last two decades, how activity is coordinated in the activated network remains unknown. This chapter examines the different modes of neural coordination that have been analyzed theoretically and experimentally and outlines the distinction between well-learned behavior and dynamic neural coordination in flexibly defined neural ensembles. Well-learned behavior can take advantage of prespecified neural routes while dynamic neural coordination can give rise to new percepts and/or creative behaviors. The chapter discusses the role of brain rhythms in human cognition in the context of dynamic coordination. It also explains why different frequency bands coexist and interact using a hypothetical but comprehensive schema.

Computational modeling is used in neuroscience to connect cortical microscopic processes at the cellular and synaptic level with large-scale cortical dynamics and coordination that underlie perceptual and cognitive functions. The holistic processing and global coordination characteristic of cortical information processing and dynamics may be explained with the aid of the Hebbian cell assembly and attractor network paradigm. Using a large-scale model of cortical layers 2/3, this chapter explores the fundamental holistic perceptual and associative memory functions performed by the neocortex, focusing on cortical local subnetworks and functional microcircuits. It looks at the possible roles of oscillations and synchrony for processing and dynamic coordination in the brain, along with cortical connectivity, modularization, and layered structure. It also discusses plasticity in the microcircuit and global network.

This chapter discusses some of the basic features of neuronal circuits underlying the population rhythms that can be generated in very small areas of the cortex from slow waves up to extremely fast oscillations. These rhythms are produced via intrinsic neuronal properties combined with chemical and electrical synaptic connectivity profiles. Multiple concurrently generated cortical rhythms can exhibit various forms of coordination, such as concatenation. After providing an overview of how rhythms are generated in the cortex, the chapter examines features of local cortical circuits that generate rhythms. It also discusses synaptic excitation and synaptic inhibition, gap junctions, and features involved in dynamic coordination in the brain.

This chapter explores the nature and meaning of local brain states that must be brought together when trying to apply our everyday concept of dynamic coordination to the brain, along with the nature of meaningful structural relationships and how they are learned by the brain. It also discusses the role of focal attention, how the brain evaluates its current level of coordination, how brain states address goals, the nature of our environment’s statistics and how it is captured by the brain, and the mechanisms that endow brain dynamics with a tendency to fall into coordinated states.

This chapter examines dynamic coordination in Gestalt perception by focusing on the problem of accumulating local errors and the trade-off between spatial and temporal resolution in pictorial representations. It illustrates the first issue by using an old and wonderful architectural mystery, the enigma of the Florence Dome, and the second issue using the history of photography. The solutions to both problems are based on global geometry. The two classic examples are accompanied by visual phenomena showing the relevance of symmetry-based representations in the dynamic coordination of visual perception. The chapter also looks at the unsolved problem of segmentation in vision, contrast sensitivity maps and image compression in the visual cortex, and how synchronous firing of orientation-tuned neurons mediates the compression and provides “hot-spots” in the neural representation of the segmented visual input.