609 research outputs found
Hierarchical Features of Large-Scale Cortical Connectivity
The analysis of complex networks has revealed patterns of organization in a
variety of natural and artificial systems, including neuronal networks of the
brain at multiple scales. In this paper, we describe a novel analysis of the
large-scale connectivity between regions of the mammalian cerebral cortex,
utilizing a set of hierarchical measurements proposed recently. We examine
previously identified functional clusters of brain regions in macaque visual
cortex and cat cortex and find significant differences between such clusters in
terms of several hierarchical measures, revealing differences in how these
clusters are embedded in the overall cortical architecture. For example, the
ventral cluster of visual cortex maintains structurally more segregated, less
divergent connections than the dorsal cluster, which may point to functionally
different roles of their constituent brain regions.Comment: 17 pages, 6 figure
The Non-Random Brain: Efficiency, Economy, and Complex Dynamics
Modern anatomical tracing and imaging techniques are beginning to reveal the structural anatomy of neural circuits at small and large scales in unprecedented detail. When examined with analytic tools from graph theory and network science, neural connectivity exhibits highly non-random features, including high clustering and short path length, as well as modules and highly central hub nodes. These characteristic topological features of neural connections shape non-random dynamic interactions that occur during spontaneous activity or in response to external stimulation. Disturbances of connectivity and thus of neural dynamics are thought to underlie a number of disease states of the brain, and some evidence suggests that degraded functional performance of brain networks may be the outcome of a process of randomization affecting their nodes and edges. This article provides a survey of the non-random structure of neural connectivity, primarily at the large scale of regions and pathways in the mammalian cerebral cortex. In addition, we will discuss how non-random connections can give rise to differentiated and complex patterns of dynamics and information flow. Finally, we will explore the idea that at least some disorders of the nervous system are associated with increased randomness of neural connections
Neural Dynamics and Cognitive Synthesis
Nervous systems facing complex environments have to balance two seemingly opposing requirements. First, there is a need quickly and reliably to extract important features from sensory inputs. This is accomplished by functionally segregated (specialized) sets of neurons, e.g. those found in different cortical areas. Second, there is a need to generate coherent perceptual and cognitive states allowing an organism to respond to objects and events, representing conjunctions of numerous individual features. This is accomplished by functional integration of the activity of specialized neurons through their dynamic interactions. These interactions produce patterns of temporal correlations or functional connectivity involving distributed neuronal populations, both within and across cortical areas. Empirical and computational studies suggesting that changes in functional connectivity may underlie specific perceptual and cognitive states involving the integration of information across specialized areas of the brain offer new insights into the linkage between neural dynamics and cognitive synthesis
Mapping Information Flow in Sensorimotor Networks
Biological organisms continuously select and sample information used by their neural structures for perception and action, and for creating coherent cognitive states guiding their autonomous behavior. Information processing, however, is not solely an internal function of the nervous system. Here we show, instead, how sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture, and how the flow of information between sensors, neural units, and effectors is actively shaped by the interaction with the environment. We analyze sensory and motor data collected from real and simulated robots and reveal the presence of information structure and directed information flow induced by dynamically coupled sensorimotor activity, including effects of motor outputs on sensory inputs. We find that information structure and information flow in sensorimotor networks (a) is spatially and temporally specific; (b) can be affected by learning, and (c) can be affected by changes in body morphology. Our results suggest a fundamental link between physical embeddedness and information, highlighting the effects of embodied interactions on internal (neural) information processing, and illuminating the role of various system components on the generation of behavior
Mechanisms of Zero-Lag Synchronization in Cortical Motifs
Zero-lag synchronization between distant cortical areas has been observed in
a diversity of experimental data sets and between many different regions of the
brain. Several computational mechanisms have been proposed to account for such
isochronous synchronization in the presence of long conduction delays: Of
these, the phenomenon of "dynamical relaying" - a mechanism that relies on a
specific network motif - has proven to be the most robust with respect to
parameter mismatch and system noise. Surprisingly, despite a contrary belief in
the community, the common driving motif is an unreliable means of establishing
zero-lag synchrony. Although dynamical relaying has been validated in empirical
and computational studies, the deeper dynamical mechanisms and comparison to
dynamics on other motifs is lacking. By systematically comparing
synchronization on a variety of small motifs, we establish that the presence of
a single reciprocally connected pair - a "resonance pair" - plays a crucial
role in disambiguating those motifs that foster zero-lag synchrony in the
presence of conduction delays (such as dynamical relaying) from those that do
not (such as the common driving triad). Remarkably, minor structural changes to
the common driving motif that incorporate a reciprocal pair recover robust
zero-lag synchrony. The findings are observed in computational models of
spiking neurons, populations of spiking neurons and neural mass models, and
arise whether the oscillatory systems are periodic, chaotic, noise-free or
driven by stochastic inputs. The influence of the resonance pair is also robust
to parameter mismatch and asymmetrical time delays amongst the elements of the
motif. We call this manner of facilitating zero-lag synchrony resonance-induced
synchronization, outline the conditions for its occurrence, and propose that it
may be a general mechanism to promote zero-lag synchrony in the brain.Comment: 41 pages, 12 figures, and 11 supplementary figure
Zipf's Law Leads to Heaps' Law: Analyzing Their Relation in Finite-Size Systems
Background: Zipf's law and Heaps' law are observed in disparate complex
systems. Of particular interests, these two laws often appear together. Many
theoretical models and analyses are performed to understand their co-occurrence
in real systems, but it still lacks a clear picture about their relation.
Methodology/Principal Findings: We show that the Heaps' law can be considered
as a derivative phenomenon if the system obeys the Zipf's law. Furthermore, we
refine the known approximate solution of the Heaps' exponent provided the
Zipf's exponent. We show that the approximate solution is indeed an asymptotic
solution for infinite systems, while in the finite-size system the Heaps'
exponent is sensitive to the system size. Extensive empirical analysis on tens
of disparate systems demonstrates that our refined results can better capture
the relation between the Zipf's and Heaps' exponents. Conclusions/Significance:
The present analysis provides a clear picture about the relation between the
Zipf's law and Heaps' law without the help of any specific stochastic model,
namely the Heaps' law is indeed a derivative phenomenon from Zipf's law. The
presented numerical method gives considerably better estimation of the Heaps'
exponent given the Zipf's exponent and the system size. Our analysis provides
some insights and implications of real complex systems, for example, one can
naturally obtained a better explanation of the accelerated growth of scale-free
networks.Comment: 15 pages, 6 figures, 1 Tabl
Measuring information integration
BACKGROUND: To understand the functioning of distributed networks such as the brain, it is important to characterize their ability to integrate information. The paper considers a measure based on effective information, a quantity capturing all causal interactions that can occur between two parts of a system. RESULTS: The capacity to integrate information, or Φ, is given by the minimum amount of effective information that can be exchanged between two complementary parts of a subset. It is shown that this measure can be used to identify the subsets of a system that can integrate information, or complexes. The analysis is applied to idealized neural systems that differ in the organization of their connections. The results indicate that Φ is maximized by having each element develop a different connection pattern with the rest of the complex (functional specialization) while ensuring that a large amount of information can be exchanged across any bipartition of the network (functional integration). CONCLUSION: Based on this analysis, the connectional organization of certain neural architectures, such as the thalamocortical system, are well suited to information integration, while that of others, such as the cerebellum, are not, with significant functional consequences. The proposed analysis of information integration should be applicable to other systems and networks
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