29,420 research outputs found
Sustained synchronized neuronal network activity in a human astrocyte co-culture system
Impaired neuronal network function is a hallmark of neurodevelopmental and neurodegenerative disorders such as autism, schizophrenia, and Alzheimer's disease and is typically studied using genetically modified cellular and animal models. Weak predictive capacity and poor translational value of these models urge for better human derived in vitro models. The implementation of human induced pluripotent stem cells (hiPSCs) allows studying pathologies in differentiated disease-relevant and patient-derived neuronal cells. However, the differentiation process and growth conditions of hiPSC-derived neurons are non-trivial. In order to study neuronal network formation and (mal) function in a fully humanized system, we have established an in vitro co-culture model of hiPSC-derived cortical neurons and human primary astrocytes that recapitulates neuronal network synchronization and connectivity within three to four weeks after final plating. Live cell calcium imaging, electrophysiology and high content image analyses revealed an increased maturation of network functionality and synchronicity over time for co-cultures compared to neuronal monocultures. The cells express GABAergic and glutamatergic markers and respond to inhibitors of both neurotransmitter pathways in a functional assay. The combination of this co-culture model with quantitative imaging of network morphofunction is amenable to high throughput screening for lead discovery and drug optimization for neurological diseases
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
A great improvement to the insight on brain function that we can get from
fMRI data can come from effective connectivity analysis, in which the flow of
information between even remote brain regions is inferred by the parameters of
a predictive dynamical model. As opposed to biologically inspired models, some
techniques as Granger causality (GC) are purely data-driven and rely on
statistical prediction and temporal precedence. While powerful and widely
applicable, this approach could suffer from two main limitations when applied
to BOLD fMRI data: confounding effect of hemodynamic response function (HRF)
and conditioning to a large number of variables in presence of short time
series. For task-related fMRI, neural population dynamics can be captured by
modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI
on the other hand, the absence of explicit inputs makes this task more
difficult, unless relying on some specific prior physiological hypothesis. In
order to overcome these issues and to allow a more general approach, here we
present a simple and novel blind-deconvolution technique for BOLD-fMRI signal.
Coming to the second limitation, a fully multivariate conditioning with short
and noisy data leads to computational problems due to overfitting. Furthermore,
conceptual issues arise in presence of redundancy. We thus apply partial
conditioning to a limited subset of variables in the framework of information
theory, as recently proposed. Mixing these two improvements we compare the
differences between BOLD and deconvolved BOLD level effective networks and draw
some conclusions
Brain covariance selection: better individual functional connectivity models using population prior
Spontaneous brain activity, as observed in functional neuroimaging, has been
shown to display reproducible structure that expresses brain architecture and
carries markers of brain pathologies. An important view of modern neuroscience
is that such large-scale structure of coherent activity reflects modularity
properties of brain connectivity graphs. However, to date, there has been no
demonstration that the limited and noisy data available in spontaneous activity
observations could be used to learn full-brain probabilistic models that
generalize to new data. Learning such models entails two main challenges: i)
modeling full brain connectivity is a difficult estimation problem that faces
the curse of dimensionality and ii) variability between subjects, coupled with
the variability of functional signals between experimental runs, makes the use
of multiple datasets challenging. We describe subject-level brain functional
connectivity structure as a multivariate Gaussian process and introduce a new
strategy to estimate it from group data, by imposing a common structure on the
graphical model in the population. We show that individual models learned from
functional Magnetic Resonance Imaging (fMRI) data using this population prior
generalize better to unseen data than models based on alternative
regularization schemes. To our knowledge, this is the first report of a
cross-validated model of spontaneous brain activity. Finally, we use the
estimated graphical model to explore the large-scale characteristics of
functional architecture and show for the first time that known cognitive
networks appear as the integrated communities of functional connectivity graph.Comment: in Advances in Neural Information Processing Systems, Vancouver :
Canada (2010
Neural Avalanches at the Critical Point between Replay and Non-Replay of Spatiotemporal Patterns
We model spontaneous cortical activity with a network of coupled spiking
units, in which multiple spatio-temporal patterns are stored as dynamical
attractors. We introduce an order parameter, which measures the overlap
(similarity) between the activity of the network and the stored patterns. We
find that, depending on the excitability of the network, different working
regimes are possible. For high excitability, the dynamical attractors are
stable, and a collective activity that replays one of the stored patterns
emerges spontaneously, while for low excitability, no replay is induced.
Between these two regimes, there is a critical region in which the dynamical
attractors are unstable, and intermittent short replays are induced by noise.
At the critical spiking threshold, the order parameter goes from zero to one,
and its fluctuations are maximized, as expected for a phase transition (and as
observed in recent experimental results in the brain). Notably, in this
critical region, the avalanche size and duration distributions follow power
laws. Critical exponents are consistent with a scaling relationship observed
recently in neural avalanches measurements. In conclusion, our simple model
suggests that avalanche power laws in cortical spontaneous activity may be the
effect of a network at the critical point between the replay and non-replay of
spatio-temporal patterns
Anticipatory Semantic Processes
Why anticipatory processes correspond to cognitive abilities of living systems? To be adapted to an environment, behaviors need at least i) internal representations of events occurring in the external environment; and ii) internal anticipations of possible events to occur in the external environment. Interactions of these two opposite but complementary cognitive properties lead to various patterns of experimental data on semantic processing.
How to investigate dynamic semantic processes? Experimental studies in cognitive psychology offer several interests such as: i) the control of the semantic environment such as words embedded in sentences; ii) the methodological tools allowing the observation of anticipations and adapted oculomotor behavior during reading; and iii) the analyze of different anticipatory processes within the theoretical framework of semantic processing.
What are the different types of semantic anticipations? Experimental data show that semantic anticipatory processes involve i) the coding in memory of sequences of words occurring in textual environments; ii) the anticipation of possible future words from currently perceived words; and iii) the selection of anticipated words as a function of the sequences of perceived words, achieved by anticipatory activations and inhibitory selection processes.
How to modelize anticipatory semantic processes? Localist or distributed neural networks models can account for some types of semantic processes, anticipatory or not. Attractor neural networks coding temporal sequences are presented as good candidate for modeling anticipatory semantic processes, according to specific properties of the human brain such as i) auto-associative memory; ii) learning and memorization of sequences of patterns; and iii) anticipation of memorized patterns from previously perceived patterns
Detection of subthreshold pulses in neurons with channel noise
Neurons are subject to various kinds of noise. In addition to synaptic noise,
the stochastic opening and closing of ion channels represents an intrinsic
source of noise that affects the signal processing properties of the neuron. In
this paper, we studied the response of a stochastic Hodgkin-Huxley neuron to
transient input subthreshold pulses. It was found that the average response
time decreases but variance increases as the amplitude of channel noise
increases. In the case of single pulse detection, we show that channel noise
enables one neuron to detect the subthreshold signals and an optimal membrane
area (or channel noise intensity) exists for a single neuron to achieve optimal
performance. However, the detection ability of a single neuron is limited by
large errors. Here, we test a simple neuronal network that can enhance the
pulse detecting abilities of neurons and find dozens of neurons can perfectly
detect subthreshold pulses. The phenomenon of intrinsic stochastic resonance is
also found both at the level of single neurons and at the level of networks. At
the network level, the detection ability of networks can be optimized for the
number of neurons comprising the network.Comment: 14 pages, 9 figure
Stimulus sensitivity of a spiking neural network model
Some recent papers relate the criticality of complex systems to their maximal
capacity of information processing. In the present paper, we consider high
dimensional point processes, known as age-dependent Hawkes processes, which
have been used to model spiking neural networks. Using mean-field
approximation, the response of the network to a stimulus is computed and we
provide a notion of stimulus sensitivity. It appears that the maximal
sensitivity is achieved in the sub-critical regime, yet almost critical for a
range of biologically relevant parameters
Neuronal Synchronization Can Control the Energy Efficiency of Inter-Spike Interval Coding
The role of synchronous firing in sensory coding and cognition remains
controversial. While studies, focusing on its mechanistic consequences in
attentional tasks, suggest that synchronization dynamically boosts sensory
processing, others failed to find significant synchronization levels in such
tasks. We attempt to understand both lines of evidence within a coherent
theoretical framework. We conceptualize synchronization as an independent
control parameter to study how the postsynaptic neuron transmits the average
firing activity of a presynaptic population, in the presence of
synchronization. We apply the Berger-Levy theory of energy efficient
information transmission to interpret simulations of a Hodgkin-Huxley-type
postsynaptic neuron model, where we varied the firing rate and synchronization
level in the presynaptic population independently. We find that for a fixed
presynaptic firing rate the simulated postsynaptic interspike interval
distribution depends on the synchronization level and is well-described by a
generalized extreme value distribution. For synchronization levels of 15% to
50%, we find that the optimal distribution of presynaptic firing rate,
maximizing the mutual information per unit cost, is maximized at ~30%
synchronization level. These results suggest that the statistics and energy
efficiency of neuronal communication channels, through which the input rate is
communicated, can be dynamically adapted by the synchronization level.Comment: 47 pages, 14 figures, 2 Table
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