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dynamic neural field

Moritz Augustin, Josef Ladenbauer, Fabian Baumann, Klaus Obermayer
The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated...
June 23, 2017: PLoS Computational Biology
Gabriel Koch Ocker, Krešimir Josić, Eric Shea-Brown, Michael A Buice
Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing...
June 23, 2017: PLoS Computational Biology
David L Boothe, Alfred B Yu, Pawel Kudela, William S Anderson, Jean M Vettel, Piotr J Franaszczuk
Within multiscale brain dynamics, the structure-function relationship between cellular changes at a lower scale and coordinated oscillations at a higher scale is not well understood. This relationship may be particularly relevant for understanding functional impairments after a mild traumatic brain injury (mTBI) when current neuroimaging methods do not reveal morphological changes to the brain common in moderate to severe TBI such as diffuse axonal injury or gray matter lesions. Here, we created a physiology-based model of cerebral cortex using a publicly released modeling framework (GEneral NEural SImulation System) to explore the possibility that performance deficits characteristic of blast-induced mTBI may reflect dysfunctional, local network activity influenced by microscale neuronal damage at the cellular level...
2017: Frontiers in Neurology
E J Müller, S J van Albada, J W Kim, P A Robinson
The mechanisms underlying pathologically synchronized neural oscillations in Parkinson's disease (PD) and generalized epilepsies are explored in parallel via a physiologically-based neural field model of the corticothalamic-basal ganglia (CTBG) system. The basal ganglia (BG) are approximated as a single effective population and their roles in the modulation of oscillatory dynamics of the corticothalamic (CT) system and vice versa are analyzed. In addition to normal EEG rhythms, enhanced activity around 4 Hz and 20 Hz exists in the model, consistent with the characteristic frequencies observed in PD...
June 17, 2017: Journal of Theoretical Biology
Charalambos Sigalas, Eleni Konsolaki, Irini Skaliora
BACKGROUND: Several molecular and cellular processes in the vertebrate brain exhibit differences between males and females, leading to sexual dimorphism in the formation of neural circuits and brain organization. While studies on large-scale brain networks provide ample evidence for both structural and functional sex differences, smaller-scale local networks have remained largely unexplored. In the current study, we investigate sexual dimorphism in cortical dynamics by means of spontaneous Up/Down states, a type of network activity that is exhibited during slow-wave sleep, quiet wakefulness, and anesthesia and is thought to represent the default activity of the cortex...
2017: Biology of Sex Differences
S Visser, R Nicks, O Faugeras, S Coombes
The Nunez model for the generation of electroencephalogram (EEG) signals is naturally described as a neural field model on a sphere with space-dependent delays. For simplicity, dynamical realisations of this model either as a damped wave equation or an integro-differential equation, have typically been studied in idealised one dimensional or planar settings. Here we revisit the original Nunez model to specifically address the role of spherical topology on spatio-temporal pattern generation. We do this using a mixture of Turing instability analysis, symmetric bifurcation theory, centre manifold reduction and direct simulations with a bespoke numerical scheme...
June 15, 2017: Physica D. Nonlinear Phenomena
Matthew Chalk, Paul Masset, Boris Gutkin, Sophie Deneve
In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others...
June 16, 2017: PLoS Computational Biology
Ryszard Auksztulewicz, Nicolas Barascud, Gerald Cooray, Anna Christina Nobre, Maria Chait, Karl Friston
Stimulus predictability can lead to substantial modulations of brain activity, such as shifts in sustained magnetic field amplitude, measured with magnetoencephalography. Here, we provide a mechanistic explanation of these effects using MEG data acquired from healthy human volunteers (N=13, 7 female). In a source-level analysis of induced responses, we established the effects of orthogonal predictability manipulations of rapid tone-pip sequences (namely, sequence regularity and alphabet size) along the auditory processing stream...
June 8, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Yi Sun, Aljoscha Nern, Romain Franconville, Hod Dana, Eric R Schreiter, Loren L Looger, Karel Svoboda, Douglas S Kim, Ann M Hermundstad, Vivek Jayaraman
Many animals orient using visual cues, but how a single cue is selected from among many is poorly understood. Here we show that Drosophila ring neurons-central brain neurons implicated in navigation-display visual stimulus selection. Using in vivo two-color two-photon imaging with genetically encoded calcium indicators, we demonstrate that individual ring neurons inherit simple-cell-like receptive fields from their upstream partners. Stimuli in the contralateral visual field suppressed responses to ipsilateral stimuli in both populations...
June 12, 2017: Nature Neuroscience
Jiagui Wu, Shu-Wei Huang, Yongjun Huang, Hao Zhou, Jinghui Yang, Jia-Ming Liu, Mingbin Yu, Guoqiang Lo, Dim-Lee Kwong, Shukai Duan, Chee Wei Wong
Chaos has revolutionized the field of nonlinear science and stimulated foundational studies from neural networks, extreme event statistics, to physics of electron transport. Recent studies in cavity optomechanics provide a new platform to uncover quintessential architectures of chaos generation and the underlying physics. Here, we report the generation of dynamical chaos in silicon-based monolithic optomechanical oscillators, enabled by the strong and coupled nonlinearities of two-photon absorption induced Drude electron-hole plasma...
June 9, 2017: Nature Communications
Jan Hahne, David Dahmen, Jannis Schuecker, Andreas Frommer, Matthias Bolten, Moritz Helias, Markus Diesmann
Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes...
2017: Frontiers in Neuroinformatics
P Tsakanikas, C Sigalas, P Rigas, I Skaliora
Synchronized brain activity in the form of alternating epochs of massive persistent network activity and periods of generalized neural silence, has been extensively studied as a fundamental form of circuit dynamics, important for many cognitive functions including short-term memory, memory consolidation, or attentional modulation. A key element in such studies is the accurate determination of the timing and duration of those network events. The local field potential (LFP) is a particularly attractive method for recording network activity, because it allows for long and stable recordings from multiple sites, allowing researchers to estimate the functional connectivity of local networks...
June 8, 2017: Scientific Reports
Matthew G Perich, Lee E Miller
Although primary motor cortex (M1) is intimately involved in the dynamics of limb movement, its inputs may be more closely related to higher-order aspects of movement and multi-modal sensory feedback. Motor learning is thought to result from the adaption of internal models that compute transformations between these representations. While the psychophysics of motor learning has been studied in many experiments, the particular role of M1 in the process remains the subject of debate. Studies of learning-related changes in the spatial tuning of M1 neurons have yielded conflicting results...
June 6, 2017: Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
Meng Cui, Yifeng Zhou, Bowen Wei, Xiao-Hong Zhu, Wei Zhu, Mark A Sanders, Kamil Ugurbil, Wei Chen
Functional magnetic resonance imaging (fMRI) based on the blood oxygen level dependent (BOLD) contrast has gained a prominent position in neuroscience for imaging neuronal activity and studying effective brain connectivity under working state and functional connectivity at resting state. However, the fundamental questions in regards to fMRI technology: how the BOLD signal inferences the underlying microscopic neuronal activity and physiological changes and what is the ultimate specificity of fMRI for functional mapping of microcircuits, remain unanswered...
June 2, 2017: Scientific Reports
Nir Grossman, David Bono, Nina Dedic, Suhasa B Kodandaramaiah, Andrii Rudenko, Ho-Jun Suk, Antonino M Cassara, Esra Neufeld, Niels Kuster, Li-Huei Tsai, Alvaro Pascual-Leone, Edward S Boyden
We report a noninvasive strategy for electrically stimulating neurons at depth. By delivering to the brain multiple electric fields at frequencies too high to recruit neural firing, but which differ by a frequency within the dynamic range of neural firing, we can electrically stimulate neurons throughout a region where interference between the multiple fields results in a prominent electric field envelope modulated at the difference frequency. We validated this temporal interference (TI) concept via modeling and physics experiments, and verified that neurons in the living mouse brain could follow the electric field envelope...
June 1, 2017: Cell
Frédéric D Broccard, Siddharth Joshi, Jun Wang, Gert Cauwenberghs
OBJECTIVE: Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks...
June 2, 2017: Journal of Neural Engineering
Gustavo Zampier Dos Santos Lima, Sergio Roberto Lopes, Thiago Lima Prado, Bruno Lobao-Soares, George C do Nascimento, John Fontenele-Araujo, Gilberto Corso
Arousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies...
2017: PloS One
Debajit Saha, Wensheng Sun, Chao Li, Srinath Nizampatnam, William Padovano, Zhengdao Chen, Alex Chen, Ege Altan, Ray Lo, Dennis L Barbour, Baranidharan Raman
Even simple sensory stimuli evoke neural responses that are dynamic and complex. Are the temporally patterned neural activities important for controlling the behavioral output? Here, we investigated this issue. Our results reveal that in the insect antennal lobe, due to circuit interactions, distinct neural ensembles are activated during and immediately following the termination of every odorant. Such non-overlapping response patterns are not observed even when the stimulus intensity or identities were changed...
May 23, 2017: Nature Communications
Brian K Taylor, Sönke Johnsen, Kenneth J Lohmann
Diverse taxa use Earth's magnetic field to aid both short- and long-distance navigation. Study of these behaviors has led to a variety of postulated sensory and processing mechanisms that remain unconfirmed. Although several models have been proposed to explain and understand these mechanisms' underpinnings, they have not necessarily connected a putative sensory signal to the nervous system. Using mathematical software simulation, hardware testing and the computational neuroscience tool of dynamic neural fields, the present work implements a previously developed conceptual model for processing magnetite-based magnetosensory data...
May 19, 2017: Bioinspiration & Biomimetics
Luca Bertolaccini, Piergiorgio Solli, Alessandro Pardolesi, Antonello Pasini
The artificial neural networks (ANNs) are statistical models where the mathematical structure reproduces the biological organisation of neural cells simulating the learning dynamics of the brain. Although definitions of the term ANN could vary, the term usually refers to a neural network used for non-linear statistical data modelling. The neural models applied today in various fields of medicine, such as oncology, do not aim to be biologically realistic in detail but just efficient models for nonlinear regression or classification...
April 2017: Journal of Thoracic Disease
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