1,840 research outputs found
Emergent complex neural dynamics
A large repertoire of spatiotemporal activity patterns in the brain is the
basis for adaptive behaviour. Understanding the mechanism by which the brain's
hundred billion neurons and hundred trillion synapses manage to produce such a
range of cortical configurations in a flexible manner remains a fundamental
problem in neuroscience. One plausible solution is the involvement of universal
mechanisms of emergent complex phenomena evident in dynamical systems poised
near a critical point of a second-order phase transition. We review recent
theoretical and empirical results supporting the notion that the brain is
naturally poised near criticality, as well as its implications for better
understanding of the brain
Neutral theory and scale-free neural dynamics
Avalanches of electrochemical activity in brain networks have been
empirically reported to obey scale-invariant behavior --characterized by
power-law distributions up to some upper cut-off-- both in vitro and in vivo.
Elucidating whether such scaling laws stem from the underlying neural dynamics
operating at the edge of a phase transition is a fascinating possibility, as
systems poised at criticality have been argued to exhibit a number of important
functional advantages. Here we employ a well-known model for neural dynamics
with synaptic plasticity, to elucidate an alternative scenario in which
neuronal avalanches can coexist, overlapping in time, but still remaining
scale-free. Remarkably their scale-invariance does not stem from underlying
criticality nor self-organization at the edge of a continuous phase transition.
Instead, it emerges from the fact that perturbations to the system exhibit a
neutral drift --guided by demographic fluctuations-- with respect to endogenous
spontaneous activity. Such a neutral dynamics --similar to the one in neutral
theories of population genetics-- implies marginal propagation of activity,
characterized by power-law distributed causal avalanches. Importantly, our
results underline the importance of considering causal information --on which
neuron triggers the firing of which-- to properly estimate the statistics of
avalanches of neural activity. We discuss the implications of these findings
both in modeling and to elucidate experimental observations, as well as its
possible consequences for actual neural dynamics and information processing in
actual neural networks.Comment: Main text: 8 pages, 3 figures. Supplementary information: 5 pages, 4
figure
Metastability, Criticality and Phase Transitions in brain and its Models
This essay extends the previously deposited paper "Oscillations, Metastability and Phase Transitions" to incorporate the theory of Self-organizing Criticality. The twin concepts of Scaling and Universality of the theory of nonequilibrium phase transitions is applied to the role of reentrant activity in neural circuits of cerebral cortex and subcortical neural structures
Why have asset price properties changed so little in 200 years
We first review empirical evidence that asset prices have had episodes of
large fluctuations and been inefficient for at least 200 years. We briefly
review recent theoretical results as well as the neurological basis of trend
following and finally argue that these asset price properties can be attributed
to two fundamental mechanisms that have not changed for many centuries: an
innate preference for trend following and the collective tendency to exploit as
much as possible detectable price arbitrage, which leads to destabilizing
feedback loops.Comment: 16 pages, 4 figure
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)
This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness
Optimal percentage of inhibitory synapses in multi-task learning
Performing more tasks in parallel is a typical feature of complex brains.
These are characterized by the coexistence of excitatory and inhibitory
synapses, whose percentage in mammals is measured to have a typical value of
20-30\%. Here we investigate parallel learning of more Boolean rules in
neuronal networks. We find that multi-task learning results from the
alternation of learning and forgetting of the individual rules. Interestingly,
a fraction of 30\% inhibitory synapses optimizes the overall performance,
carving a complex backbone supporting information transmission with a minimal
shortest path length. We show that 30\% inhibitory synapses is the percentage
maximizing the learning performance since it guarantees, at the same time, the
network excitability necessary to express the response and the variability
required to confine the employment of resources.Comment: 5 pages, 5 figure
The resilient brain and the guardians of sleep: new perspectives on old assumptions
Resilience is the capacity of a system, enterprise or a person to maintain its core purpose and integrity in the face of dramatically changed circumstances. In human physiology, resilience is the capacity of adaptively overcoming stress and adversity while maintaing normal psychological and physical functioning. In this review, we investigate the resilient strategies of sleep. First, we discuss the concept of brain resilience, highlighting the modular structure of small-world networking, neuronal plasticity and critical brain behaviour. Second, we explore the contribution of sleep to brain resilience listing the putative factors that impair sleep quality and predict susceptibility to sleep disorders. The third part details the manifold mechanisms acting as guardians of sleep, i.e., homeostatic, circadian and ultradian processes, sleep microstructure (K-complexes, delta bursts, arousals, cyclic alternating pattern, spindles), gravity, muscle tone and dreams. Mapping and pooling together the guardians of sleep in a dynamic integrated framework might lead towards an objective measure of sleep resilience and identify effective personalized strategies (biological, pharmacological, behavioral) to restore or protect the core properties of healthy sleep
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