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This chapter presents an overview of the physical and physiological bases for the electric potentials recorded from the human scalp, called electroencephalography or EEG. EEG applications in the clinical and cognitive sciences are outlined. Several dynamic properties of important phenomena—the human alpha rhythms—are outlined. A proposed conceptual framework for EEG generation is based on cell assemblies (neural networks) embedded in synaptic action fields, analogous to social networks embedded in a culture. Excitatory and inhibitory synaptic action fields are defined simply in terms of the number of active synapses in each cortical tissue mass or volume element, irrespective of functional significance. Interactions between the synaptic fields and networks provide a means for disparate networks to couple, a picture directly addressing the so-called binding problem of brain science. Elementary features of volume conduction are also discussed: currents, voltages, Ohm's law, reference electrodes, current sources, dipoles, dipole layers, source synchrony, and reference electrodes.

The temporal coding hypothesis suggests that not only changes in firing rate but also precise spike timing, especially synchrony, constitute an important part of the representational substrate for perception and action. In this framework, the concept of cell assemblies uses synchrony as an additional dimension to firing rate, as a candidate for information processing. Consequently, the observation of spike synchrony between neurons might be interpreted as an activation of a functional cell assembly. When, in an instructed delay task, prior information is provided about movement parameters, such as movement direction (spatial parameters) or the moment when to move (temporal parameters), movement initiation is faster. Cortical neurons selectively modulate their activity in relation to this information. To indicate the end of an instructed delay, motor cortical neurons synchronize significantly their activity at the moment of signal expectancy, often without any detectable modulation in firing rate. The observed increase of the temporal precision of synchrony toward the end of an instructed delay is interpreted to facilitate the efficiency of the motor output, leading to an increase of performance speed. Finally, the chapter shows that the timing of the task is dynamically represented in the temporal structure of significant spike synchrony at the population level, which is shaped by learning and practice. The emergence of significant synchrony becomes more structured; that, is it becomes stronger and more localized in time with practice, in parallel with a decrease in firing rate and an improvement of the behavioral performance. Performance optimization through practice might therefore be achieved by boosting the computational contribution of spike synchrony, allowing an overall reduction in population activity.

In addition to principal cells, the cerebral cortex contains diverse classes of interneurons that selectively and discriminately innervate various parts of principal cells and each other. The hypothesized “goal” of the daunting connectionist schemes of interneurons is to provide maximum functional complexity. Without inhibition and dedicated interneurons, excitatory circuits cannot accomplish anything useful. Interneurons provide autonomy and independence to neighboring principal cells but at the same time also offer useful temporal coordination. The functional diversity of principal cells is enhanced by the domain-specific actions of GABAergic interneurons, which can dynamically alter the qualities of the principal cells. The balance between excitation and inhibition is often accomplished by oscillations. Connections among interneurons, including electrical gap junctions, are especially suitable for maintaining clocking actions. Thus, the cerebral cortex is not only a complex system with complicated interactions among identical constituents but also has developed a diverse system of components.

This chapter contributes to the current debate about symbols and embodiment by pointing out the perspective of a neural modeller. It demonstrates that the default definitions of ‘symbol’, ‘embodiment’, ‘meaning’, and ‘grounding’ in the context of detailed neural network models, i.e. on a level more detailed than common connectionist approaches. This chapter's arguments are based on Hebbian neuronal or cell assemblies and detailed models of the cortical microcircuitry. It notes that these models have been employed to implement a large-scale cortical architecture to enable a robot to perform simple tasks such as understanding and reacting to simple spoken commands. It discusses the relations between embodiment, grounding, anchoring, binding, and the invariant recognition in distributed hierarchical systems.

To effectively send a message, a single neuron must cooperate with its peers. Such cooperation can be achieved by synchronizing their spikes together within the time window limited by the ability of the downstream reader neuron to integrate the incoming signals. Therefore, the cell assembly, defined from the point of view of the reader neuron, can be considered as a unit of neuronal communication, a “neuronal letter.” Acting in assemblies has several advantages. A cooperative assembly partnership tolerates spike rate variation in individual cells effectively because the total excitatory effect of the assembly is what matters to the reader mechanism. Interacting assembly members can compute probabilities rather than convey deterministic information and can robustly tolerate noise even if the individual members respond probabilistically.

The chapter Micro- and nanotechnology for living machines describes research on new biohybrid technologies, engineered at the micro- and nano-scales, that combine some of the benefits of mechanical and electronic systems with those of biological systems. The chapter begins by reviewing some of the challenges of building devices at very small physical scales and discusses how new fabrication methodologies could impact on different classes of industrial, daily life, and biomedical products. We next explain how progress is being achieved through advances in micro- and nanomechatronics, particularly in the field of nanorobotic manipulation. Finally, we summarize recent progress towards building biohybrid living machines that combine nanomaterials with biological cells and outline the design of a micro- and nanorobotic manipulation system for cell assembly called the nanolaboratory.

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 presents some ideas about Ersatz Brain Theory, which generalizes models presented in the book. It is based on three equal components: computation, cognition, and neuroscience. In the Ersatz Brain, the basic computing elements are locally interconnected groups of neurons, for example, cortical columns, and not single neurons. Columns are more powerful than neurons alone because of the potential for selectivity and reliability. A “network of networks” modular architecture is formed from interconnected groups. Response selection emerges from the stability properties of dynamical systems. Traveling waves and interference patterns also grow naturally out of dynamics and local connections. The resulting systems operate using similar rules at multiple spatial scales for different levels of integration.