1,032 research outputs found
Inferring collective dynamical states from widely unobserved systems
When assessing spatially-extended complex systems, one can rarely sample the
states of all components. We show that this spatial subsampling typically leads
to severe underestimation of the risk of instability in systems with
propagating events. We derive a subsampling-invariant estimator, and
demonstrate that it correctly infers the infectiousness of various diseases
under subsampling, making it particularly useful in countries with unreliable
case reports. In neuroscience, recordings are strongly limited by subsampling.
Here, the subsampling-invariant estimator allows to revisit two prominent
hypotheses about the brain's collective spiking dynamics:
asynchronous-irregular or critical. We identify consistently for rat, cat and
monkey a state that combines features of both and allows input to reverberate
in the network for hundreds of milliseconds. Overall, owing to its ready
applicability, the novel estimator paves the way to novel insight for the study
of spatially-extended dynamical systems.Comment: 7 pages + 12 pages supplementary information + 7 supplementary
figures. Title changed to match journal referenc
Self-organization without conservation: Are neuronal avalanches generically critical?
Recent experiments on cortical neural networks have revealed the existence of
well-defined avalanches of electrical activity. Such avalanches have been
claimed to be generically scale-invariant -- i.e. power-law distributed -- with
many exciting implications in Neuroscience. Recently, a self-organized model
has been proposed by Levina, Herrmann and Geisel to justify such an empirical
finding. Given that (i) neural dynamics is dissipative and (ii) there is a
loading mechanism "charging" progressively the background synaptic strength,
this model/dynamics is very similar in spirit to forest-fire and earthquake
models, archetypical examples of non-conserving self-organization, which have
been recently shown to lack true criticality. Here we show that cortical neural
networks obeying (i) and (ii) are not generically critical; unless parameters
are fine tuned, their dynamics is either sub- or super-critical, even if the
pseudo-critical region is relatively broad. This conclusion seems to be in
agreement with the most recent experimental observations. The main implication
of our work is that, if future experimental research on cortical networks were
to support that truly critical avalanches are the norm and not the exception,
then one should look for more elaborate (adaptive/evolutionary) explanations,
beyond simple self-organization, to account for this.Comment: 28 pages, 11 figures, regular pape
Adaptive self-organization in a realistic neural network model
Information processing in complex systems is often found to be maximally
efficient close to critical states associated with phase transitions. It is
therefore conceivable that also neural information processing operates close to
criticality. This is further supported by the observation of power-law
distributions, which are a hallmark of phase transitions. An important open
question is how neural networks could remain close to a critical point while
undergoing a continual change in the course of development, adaptation,
learning, and more. An influential contribution was made by Bornholdt and
Rohlf, introducing a generic mechanism of robust self-organized criticality in
adaptive networks. Here, we address the question whether this mechanism is
relevant for real neural networks. We show in a realistic model that
spike-time-dependent synaptic plasticity can self-organize neural networks
robustly toward criticality. Our model reproduces several empirical
observations and makes testable predictions on the distribution of synaptic
strength, relating them to the critical state of the network. These results
suggest that the interplay between dynamics and topology may be essential for
neural information processing.Comment: 6 pages, 4 figure
Network self-organization explains the statistics and dynamics of synaptic connection strengths in cortex
The information processing abilities of neural circuits arise from their synaptic connection patterns. Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strengths of local excitatory connections in cortex and hippocampus is long-tailed, exhibiting a small number of synaptic connections of very large efficacy. At the same time, new synaptic connections are constantly being created and individual synaptic connection strengths show substantial fluctuations across time. It remains unclear through what mechanisms these properties of neural circuits arise and how they contribute to learning and memory. In this study we show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity. In the network, associative synaptic plasticity in the form of STDP induces a rich-get-richer dynamics among synapses, while homeostatic mechanisms induce competition. Under distinctly different initial conditions, the ensuing self-organization produces long-tailed synaptic strength distributions matching experimental findings. We show that this self-organization can take place with a purely additive STDP mechanism and that multiplicative weight dynamics emerge as a consequence of network interactions. The observed patterns of fluctuation of synaptic strengths, including elimination and generation of synaptic connections and long-term persistence of strong connections, are consistent with the dynamics of dendritic spines found in rat hippocampus. Beyond this, the model predicts an approximately power-law scaling of the lifetimes of newly established synaptic connection strengths during development. Our results suggest that the combined action of multiple forms of neuronal plasticity plays an essential role in the formation and maintenance of cortical circuits
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Impact of Excitation-Inhibition Balance/Imbalance on Dynamics of Cortical Neural Networks
The purpose of this research is to study the implications of Excitation/Inhibition balance and imbalance on the dynamics of ongoing (spontaneous) neural activity in the cerebral cortex region of the brain.
The first research work addresses the question that why among the continuum of Excitation-Inhibition balance configurations, particular configuration should be favored? We calculate the entropy of neural network dynamics by studying an analytically tractable network of binary neurons. Our main result from this work is that the entropy maximizes at regime which is neither excitation-dominant nor inhibition-dominant but at the boundary of both. Along this boundary we see there is a trade-off between high and robust entropy. Weak synapse strengths yield entropy which is high but drops rapidly under parameter change. Strong synapse strengths, on the other hand yield a lower, but more robust, network entropy.
The second research work is motivated from experiments suggest that the cerebral cortex can also operate near a critical phase transition. It has been observed in many physical systems that the governing physical laws obey a fractal symmetry near critical phase transition. This symmetry exists irrespective of the observational length-scale. Thus, we hypothesize that the laws governing cortical dynamics may obey scale-change symmetry. We test and confirm this hypothesis using two different computational models. Further, we extend the transformational scheme show that as a mouse awakens from anesthesia, scale-change symmetry emerges.
The third research project is motivated by experimental observations from in motor cortex under modulation of inhibitory inputs. We found that low intensity increase (decrease) in overall inhibition in cortex causes decrease (increase) in spiking activity for some neurons. Even though, the population level activity largely unchanged. This behavior is paradoxical when compared to the status quo that says that increase (decrease) inhibition should lead to decrease (increase) in neural spiking activity. We simulated similar dynamical change to inhibitory signal modulation in neural network model. We found that this paradoxical behavior arises due to sparse connectivity and inhomogeneity in inhibitory weights
Model-free reconstruction of neuronal network connectivity from calcium imaging signals
A systematic assessment of global neural network connectivity through direct
electrophysiological assays has remained technically unfeasible even in
dissociated neuronal cultures. We introduce an improved algorithmic approach
based on Transfer Entropy to reconstruct approximations to network structural
connectivities from network activity monitored through calcium fluorescence
imaging. Based on information theory, our method requires no prior assumptions
on the statistics of neuronal firing and neuronal connections. The performance
of our algorithm is benchmarked on surrogate time-series of calcium
fluorescence generated by the simulated dynamics of a network with known
ground-truth topology. We find that the effective network topology revealed by
Transfer Entropy depends qualitatively on the time-dependent dynamic state of
the network (e.g., bursting or non-bursting). We thus demonstrate how
conditioning with respect to the global mean activity improves the performance
of our method. [...] Compared to other reconstruction strategies such as
cross-correlation or Granger Causality methods, our method based on improved
Transfer Entropy is remarkably more accurate. In particular, it provides a good
reconstruction of the network clustering coefficient, allowing to discriminate
between weakly or strongly clustered topologies, whereas on the other hand an
approach based on cross-correlations would invariantly detect artificially high
levels of clustering. Finally, we present the applicability of our method to
real recordings of in vitro cortical cultures. We demonstrate that these
networks are characterized by an elevated level of clustering compared to a
random graph (although not extreme) and by a markedly non-local connectivity.Comment: 54 pages, 8 figures (+9 supplementary figures), 1 table; submitted
for publicatio
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