1,894 research outputs found
Synthetic reverberating activity patterns embedded in networks of cortical neurons
Synthetic reverberating activity patterns are experimentally generated by
stimulation of a subset of neurons embedded in a spontaneously active network
of cortical cells in-vitro. The neurons are artificially connected by means of
conditional stimulation matrix, forming a synthetic local circuit with a
predefined programmable connectivity and time-delays. Possible uses of this
experimental design are demonstrated, analyzing the sensitivity of these
deterministic activity patterns to transmission delays and to the nature of
ongoing network dynamics.Comment: 8 pages, 5 figure
Development of bipedal and quadrupedal locomotion in humans from a dynamical systems perspective
The first phase in the development 0f locomotion, pr,öary variability would occur in normal fetuses and infants, and those with Uner Tan syndrome. The neural networks for quadrupedal locomotion have apparently been transmitted epigenetically through many species since about 400 MYA.\ud
The second phase is the neuronal selection process. During infancy, the most effective motor pattern(s) and their associated neuronal group(s) are selected through experience.\ud
The third phase, secondary or adaptive variability, starts to bloom at two to three years of age and matures in adolescence. This third phase may last much longer in some patients with Uner Tan syndrome, with a considerably delay in selection of the well-balanced quadrupedal locomotion, which may emerge very late in adolescence in these cases
Mammalian Brain As a Network of Networks
Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD
Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control
It is widely accepted that the complex dynamics characteristic of recurrent
neural circuits contributes in a fundamental manner to brain function. Progress
has been slow in understanding and exploiting the computational power of
recurrent dynamics for two main reasons: nonlinear recurrent networks often
exhibit chaotic behavior and most known learning rules do not work in robust
fashion in recurrent networks. Here we address both these problems by
demonstrating how random recurrent networks (RRN) that initially exhibit
chaotic dynamics can be tuned through a supervised learning rule to generate
locally stable neural patterns of activity that are both complex and robust to
noise. The outcome is a novel neural network regime that exhibits both
transiently stable and chaotic trajectories. We further show that the recurrent
learning rule dramatically increases the ability of RRNs to generate complex
spatiotemporal motor patterns, and accounts for recent experimental data
showing a decrease in neural variability in response to stimulus onset
Understanding Epileptiform After-Discharges as Rhythmic Oscillatory Transients
Electro-cortical activity in patients with epilepsy may show abnormal
rhythmic transients in response to stimulation. Even when using the same
stimulation parameters in the same patient, wide variability in the duration of
transient response has been reported. These transients have long been
considered important for the mapping of the excitability levels in the
epileptic brain but their dynamic mechanism is still not well understood.
To understand the occurrence of abnormal transients dynamically, we use a
thalamo-cortical neural population model of epileptic spike-wave activity and
study the interaction between slow and fast subsystems.
In a reduced version of the thalamo-cortical model, slow wave oscillations
arise from a fold of cycles (FoC) bifurcation. This marks the onset of a region
of bistability between a high amplitude oscillatory rhythm and the background
state. In vicinity of the bistability in parameter space, the model has
excitable dynamics, showing prolonged rhythmic transients in response to
suprathreshold pulse stimulation. We analyse the state space geometry of the
bistable and excitable states, and find that the rhythmic transient arises when
the impending FoC bifurcation deforms the state space and creates an area of
locally reduced attraction to the fixed point. This area essentially allows
trajectories to dwell there before escaping to the stable steady state, thus
creating rhythmic transients. In the full thalamo-cortical model, we find a
similar FoC bifurcation structure.
Based on the analysis, we propose an explanation of why stimulation induced
epileptiform activity may vary between trials, and predict how the variability
could be related to ongoing oscillatory background activity.Comment: http://journal.frontiersin.org/article/10.3389/fncom.2017.00025/ful
Detecting event-related recurrences by symbolic analysis: Applications to human language processing
Quasistationarity is ubiquitous in complex dynamical systems. In brain
dynamics there is ample evidence that event-related potentials reflect such
quasistationary states. In order to detect them from time series, several
segmentation techniques have been proposed. In this study we elaborate a recent
approach for detecting quasistationary states as recurrence domains by means of
recurrence analysis and subsequent symbolisation methods. As a result,
recurrence domains are obtained as partition cells that can be further aligned
and unified for different realisations. We address two pertinent problems of
contemporary recurrence analysis and present possible solutions for them.Comment: 24 pages, 6 figures. Draft version to appear in Proc Royal Soc
Computational physics of the mind
In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures
Synaptic Plasticity and Hebbian Cell Assemblies
Synaptic dynamics are critical to the function of neuronal circuits on multiple timescales. In the first part of this dissertation, I tested the roles of action potential timing and NMDA receptor composition in long-term modifications to synaptic efficacy. In a computational model I showed that the dynamics of the postsynaptic [Ca2+] time course can be used to map the timing of pre- and postsynaptic action potentials onto experimentally observed changes in synaptic strength. Using dual patch-clamp recordings from cultured hippocampal neurons, I found that NMDAR subtypes can map combinations of pre- and postsynaptic action potentials onto either long-term potentiation (LTP) or depression (LTD). LTP and LTD could even be evoked by the same stimuli, and in such cases the plasticity outcome was determined by the availability of NMDAR subtypes. The expression of LTD was increasingly presynaptic as synaptic connections became more developed. Finally, I found that spike-timing-dependent potentiability is history-dependent, with a non-linear relationship to the number of pre- and postsynaptic action potentials. After LTP induction, subsequent potentiability recovered on a timescale of minutes, and was dependent on the duration of the previous induction. While activity-dependent plasticity is putatively involved in circuit development, I found that it was not required to produce small networks capable of exhibiting rhythmic persistent activity patterns called reverberations. However, positive synaptic scaling produced by network inactivity yielded increased quantal synaptic amplitudes, connectivity, and potentiability, all favoring reverberation. These data suggest that chronic inactivity upregulates synaptic efficacy by both quantal amplification and by the addition of silent synapses, the latter of which are rapidly activated by reverberation. Reverberation in previously inactivated networks also resulted in activity-dependent outbreaks of spontaneous network activity. Applying a model of short-term synaptic dynamics to the network level, I argue that these experimental observations can be explained by the interaction between presynaptic calcium dynamics and short-term synaptic depression on multiple timescales. Together, the experiments and modeling indicate that ongoing activity, synaptic scaling and metaplasticity are required to endow networks with a level of synaptic connectivity and potentiability that supports stimulus-evoked persistent activity patterns but avoids spontaneous activity
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