The neocortex of awake animals displays an activated state in whichcortical activity manifests highly complex, seemingly noisybehavior. At the level of single neurons the activity is characterizedby strong subthreshold fluctuations and irregular firing at lowrate. At the network level, the activity is weakly synchronized andexhibits a chaotic dynamics. Yet, it is within this regime thatinformation is processed reliably through neural networks. This regimeis thus crucial to neural computation. In this thesis, we contributeto its understanding by investigating how the biophysical propertiesat the cellular level combined with the properties of the networkarchitecture shapes this asynchronous dynamics.This thesis builds up on the so-called mean-field models of networkdynamics, a theoretical formalism that describes population dynamicsvia a self-consistency approach. At the core of this formalism lie theneuronal transfer function: the input-output description of individualneurons. The first part of this thesis focuses on derivingbiologically-realistic neuronal transfer functions. We firstformulate a two step procedure to incorporate biological details (suchas an extended dendritic structure and the effect of various ionicchannels) into this transfer function based on experimentalcharacterizations.First, we investigated in vitro how layer V pyramidal neocorticalneurons respond to membrane potential fluctuations on a cell-by-cellbasis. We found that, not only individual neurons strongly differ interms of their excitability, but also, and unexpectedly, in theirsensitivities to fluctuations. In addition, using theoreticalmodeling, we attempted to reproduce these results. The model predictsthat heterogeneous levels of biophysical properties such as sodiuminactivation, sharpness of sodium activation and spike frequencyadaptation account for the observed diversity of firing rateresponses.Then, we studied theoretically how dendritic integration in branchedstructures shape the membrane potential fluctuations at the soma. Wefound that, depending on the type of presynaptic activity, variouscomodulations of the membrane potential fluctuations could beachieved. We showed that, when combining this observation with theheterogeneous firing responses found experimentally, individual neuronsdifferentially responded to the different types of presynapticactivities. We thus propose that, because this mechanism offers a wayto produce specific activation as a function of the input properties,biophysical heterogeneity might contribute to the encoding of the stimulusproperties during sensory processing in neural networks.The second part of this thesis investigates how circuit properties,such as recurrent connectivity and lateral connectivity, combine withbiophysical properties to impact sensory responses through effectsmediated by population dynamics.We first investigated what was the effect of a high level of ongoingdynamics (the Up-state compared to the Down-state) on the scaling ofpost-synaptic responses. We found that the competition between therecruitment within the active recurrent network (in favor of highresponses in the Up-state) and the increased conductance level due tobackground activity (in favor of reduced responses in the Up-state)predicted a non trivial stimulus-response relationship as a functionof the intensity of the stimulation. This prediction was shown toaccurately capture measurements of post-synaptic membrane potentialresponses in response to cortical, thalamic or auditory stimulation inrat auditory cortex in vivo.Finally, by taking advantage of the mean-field approach, weconstructed a tractable large-scale model of the layer II-III networkincluding the horizontal fiber network. We investigate thespatio-temporal properties of this large-scale model and we compareits predictions with voltage sensitive dye imaging in awake fixatingmonkey...

The most important operations of the brain, from vision and audition to complex movements and decision making, necessitate coordinated activity in populations of neurons. Within an area of neocortex, nearby neurons form complex networks of connections that help shape the activity patterns we observe. Since neurons are inextricably embedded within their local population, we must consider neural populations as a whole if we are to understand how the information in the outside world is interpreted and represented in the brain. In this thesis, I present two complementary approaches to study how groups of neurons cooperatively generate patterns of activity. Together, the results demonstrate how trial-to-trial variability of population activity can be explained by functional relationships within groups of neurons. The first approach identifies small ensembles of neurons that reliably spike in sequence to investigate spike propagation in emergent, spontaneous activity. By resolving spatiotemporal spike patterns into sequences, I describe single trial variability and substantiate evidence for neocortical assembly phase sequences. Secondly, I construct a functional graph from correlated activity between pairs of neurons to holistically represent shared variability in visually-evoked population dynamics. These graphs displayed a specific pattern of functional connections underlying accurate predictions of trial-to-trial variability, illustrating a signature of informative correlations in neocortical networks. These analyses delineate the ways in which ensembles of neurons coordinate their activity to shape population dynamics. Extending models of neural activity from single cells to the networks they form have been difficult. By strengthening the lexicon used to capture variability and describe population activity on single trials, we can better investigate the biological sources and behavioral consequences of variability.

The leading reference on electroencephalography since 1982, Niedermeyer's Electroencephalography is now in its thoroughly updated Sixth Edition. An international group of experts provides comprehensive coverage of the neurophysiologic and technical aspects of EEG, evoked potentials, and magnetoencephalography, as well as the clinical applications of these studies in neonates, infants, children, adults, and older adults. This edition's new lead editor, Donald Schomer, MD, has updated the technical information and added a major new chapter on artifacts. Other highlights include complete coverage of EEG in the intensive care unit and new chapters on integrating other recording devices with EEG; transcranial electrical and magnetic stimulation; EEG/TMS in evaluation of cognitive and mood disorders; and sleep in premature infants, children and adolescents, and the elderly. A companion website includes fully searchable text and image bank.

This book presents thoroughly revised tutorial papers based on lectures given by leading researchers at the 8th International Summer School on Neural Networks in Erice, Italy, in October/November 2003. The eight tutorial papers presented provide competent coverage of the field of cortical dynamics, consolidating recent theoretical and experimental results on the processing, transmission, and imprinting of information in the brain as well as on important functions of the cortical area, such as cortical rhythms, cortical neural plasticity, and their structural basis and functional significance. The book is divided in two topical sections on fundamentals of cortical dynamics and mathematical models of cortical dynamics.

This book brings together leading investigators who represent various aspects of brain dynamics with the goal of presenting state-of-the-art current progress and address future developments. The individual chapters cover several fascinating facets of contemporary neuroscience from elementary computation of neurons, mesoscopic network oscillations, internally generated assembly sequences in the service of cognition, large-scale neuronal interactions within and across systems, the impact of sleep on cognition, memory, motor-sensory integration, spatial navigation, large-scale computation and consciousness. Each of these topics require appropriate levels of analyses with sufficiently high temporal and spatial resolution of neuronal activity in both local and global networks, supplemented by models and theories to explain how different levels of brain dynamics interact with each other and how the failure of such interactions results in neurologic and mental disease. While such complex questions cannot be answered exhaustively by a dozen or so chapters, this volume offers a nice synthesis of current thinking and work-in-progress on micro-, meso- and macro- dynamics of the brain.

Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Seventh Edition keeps the clinical neurophysiologist on the forefront of medical advancements. This authoritative text covers basic neurophysiology, neuroanatomy, and neuroimaging to provide a better understanding of clinical neurophysiological findings. This edition further delves into current state-of-the-art recording EEG activity both in the normal clinical environment and unique situations such as the intensive care unit, operating rooms, and epilepsy monitoring suites. As computer technology evolves, so does the integration of analytical methods that significantly affect the reader's interpretations of waveforms and trends that are occurring on long-term monitoring sessions. Compiled and edited by Donald L. Schomer and Fernando H. Lopes da Silva, along with a global team of experts, they collectively bring insight to crucial sections including basic principles of EEG and MEG, normal EEG, EEG in a clinical setting, clinical EEG in seizures and epilepsy, complementary and special techniques, event-related EEG phenomena, and shed light on the future of EEG and clinical neurophysiology. Akin to an encyclopedia of everything EEG, this comprehensive work is perfect for neurophysiology fellows, as well as neurology, neurosurgery, and general medical residents, and for the interns and medical students, and is a one-stop-shop for anyone training in EEG or preparing for neurophysiology or epilepsy board exams.

The authoritative reference on NEURON, the simulation environment for modeling biological neurons and neural networks that enjoys wide use in the experimental and computational neuroscience communities. This book shows how to use NEURON to construct and apply empirically based models. Written primarily for neuroscience investigators, teachers, and students, it assumes no previous knowledge of computer programming or numerical methods. Readers with a background in the physical sciences or mathematics, who have some knowledge about brain cells and circuits and are interested in computational modeling, will also find it helpful. The NEURON Book covers material that ranges from the inner workings of this program, to practical considerations involved in specifying the anatomical and biophysical properties that are to be represented in models. It uses a problem-solving approach, with many working examples that readers can try for themselves.

A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

By means of quantitative analysis of the tissue components in the cortex of the mouse, this book presents an overall picture of the cortical network which is then related to various theories on cortical function. Centering around the idea of a diffuse network in a fairly homogeneous population of excitatory neurons, that of the pyramidal cells, it shows that the whole organisation in the cortical skeleton of pryramidal cells corresponds well with the idea of an associative memory and with the theory of cell assemblies. Provides the reader with information on quantitative neuroanatomy and also on the methods used, in particular those that vary from the norm.