8,552 research outputs found
Developmental time windows for axon growth influence neuronal network topology
Early brain connectivity development consists of multiple stages: birth of
neurons, their migration and the subsequent growth of axons and dendrites. Each
stage occurs within a certain period of time depending on types of neurons and
cortical layers. Forming synapses between neurons either by growing axons
starting at similar times for all neurons (much-overlapped time windows) or at
different time points (less-overlapped) may affect the topological and spatial
properties of neuronal networks. Here, we explore the extreme cases of axon
formation especially concerning short-distance connectivity during early
development, either starting at the same time for all neurons (parallel, i.e.
maximally-overlapped time windows) or occurring for each neuron separately one
neuron after another (serial, i.e. no overlaps in time windows). For both
cases, the number of potential and established synapses remained comparable.
Topological and spatial properties, however, differed: neurons that started
axon growth early on in serial growth achieved higher out-degrees, higher local
efficiency, and longer axon lengths while neurons demonstrated more homogeneous
connectivity patterns for parallel growth. Second, connection probability
decreased more rapidly with distance between neurons for parallel growth than
for serial growth. Third, bidirectional connections were more numerous for
parallel growth. Finally, we tested our predictions with C. elegans data.
Together, this indicates that time windows for axon growth influence the
topological and spatial properties of neuronal networks opening the possibility
to a posteriori estimate developmental mechanisms based on network properties
of a developed network.Comment: Biol Cybern. 2015 Jan 30. [Epub ahead of print
A simple rule for axon outgrowth and synaptic competition generates realistic connection lengths and filling fractions
Neural connectivity at the cellular and mesoscopic level appears very
specific and is presumed to arise from highly specific developmental
mechanisms. However, there are general shared features of connectivity in
systems as different as the networks formed by individual neurons in
Caenorhabditis elegans or in rat visual cortex and the mesoscopic circuitry of
cortical areas in the mouse, macaque, and human brain. In all these systems,
connection length distributions have very similar shapes, with an initial large
peak and a long flat tail representing the admixture of long-distance
connections to mostly short-distance connections. Furthermore, not all
potentially possible synapses are formed, and only a fraction of axons (called
filling fraction) establish synapses with spatially neighboring neurons. We
explored what aspects of these connectivity patterns can be explained simply by
random axonal outgrowth. We found that random axonal growth away from the soma
can already reproduce the known distance distribution of connections. We also
observed that experimentally observed filling fractions can be generated by
competition for available space at the target neurons--a model markedly
different from previous explanations. These findings may serve as a baseline
model for the development of connectivity that can be further refined by more
specific mechanisms.Comment: 31 pages (incl. supplementary information); Cerebral Cortex Advance
Access published online on May 12, 200
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
Mechanics of Morphogenesis in Neural Development: in vivo, in vitro, and in silico
Morphogenesis in the central nervous system has received intensive attention
as elucidating fundamental mechanisms of morphogenesis will shed light on the
physiology and pathophysiology of the developing central nervous system.
Morphogenesis of the central nervous system is of a vast topic that includes
important morphogenetic events such as neurulation and cortical folding. Here
we review three types of methods used to improve our understanding of
morphogenesis of the central nervous system: in vivo experiments, organoids (in
vitro), and computational models (in silico). The in vivo experiments are used
to explore cellular- and tissue-level mechanics and interpret them on the roles
of neurulation morphogenesis. Recent advances in human brain organoids have
provided new opportunities to study morphogenesis and neurogenesis to
compensate for the limitations of in vivo experiments, as organoid models are
able to recapitulate some critical neural morphogenetic processes during early
human brain development. Due to the complexity and costs of in vivo and in
vitro studies, a variety of computational models have been developed and used
to explain the formation and morphogenesis of brain structures. We review and
discuss the Pros and Cons of these methods and their usage in the studies on
morphogenesis of the central nervous system. Notably, none of these methods
alone is sufficient to unveil the biophysical mechanisms of morphogenesis, thus
calling for the interdisciplinary approaches using a combination of these
methods in order to test hypotheses and generate new insights on both normal
and abnormal development of the central nervous system
Bringing Anatomical Information into Neuronal Network Models
For constructing neuronal network models computational neuroscientists have
access to wide-ranging anatomical data that nevertheless tend to cover only a
fraction of the parameters to be determined. Finding and interpreting the most
relevant data, estimating missing values, and combining the data and estimates
from various sources into a coherent whole is a daunting task. With this
chapter we aim to provide guidance to modelers by describing the main types of
anatomical data that may be useful for informing neuronal network models. We
further discuss aspects of the underlying experimental techniques relevant to
the interpretation of the data, list particularly comprehensive data sets, and
describe methods for filling in the gaps in the experimental data. Such methods
of `predictive connectomics' estimate connectivity where the data are lacking
based on statistical relationships with known quantities. It is instructive,
and in certain cases necessary, to use organizational principles that link the
plethora of data within a unifying framework where regularities of brain
structure can be exploited to inform computational models. In addition, we
touch upon the most prominent features of brain organization that are likely to
influence predicted neuronal network dynamics, with a focus on the mammalian
cerebral cortex. Given the still existing need for modelers to navigate a
complex data landscape full of holes and stumbling blocks, it is vital that the
field of neuroanatomy is moving toward increasingly systematic data collection,
representation, and publication
Linking Visual Cortical Development to Visual Perception
Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657
Human metabolic adaptations and prolonged expensive neurodevelopment: A review
1.	After weaning, human hunter-gatherer juveniles receive substantial (≈3.5-7 MJ day^-1^), extended (≈15 years) and reliable (kin and nonkin food pooling) energy provision.
2.	The childhood (pediatric) and the adult human brain takes a very high share of both basal metabolic rate (BMR) (child: 50-70%; adult: ≈20%) and total energy expenditure (TEE) (child: 30-50%; adult: ≈10%).
3.	The pediatric brain for an extended period (≈4-9 years-of-age) consumes roughly 50% more energy than the adult one, and after this, continues during adolescence, at a high but declining rate. Within the brain, childhood cerebral gray matter has an even higher 1.9 to 2.2-fold increased energy consumption. 
4.	This metabolic expensiveness is due to (i) the high cost of synapse activation (74% of brain energy expenditure in humans), combined with (ii), a prolonged period of exuberance in synapse numbers (up to double the number present in adults). Cognitive development during this period associates with volumetric changes in gray matter (expansion and contraction due to metabolic related size alterations in glial cells and capillary vascularization), and in white matter (expansion due to myelination). 
5.	Amongst mammals, anatomically modern humans show an unique pattern in which very slow musculoskeletal body growth is followed by a marked adolescent size/stature spurt. This pattern of growth contrasts with nonhuman primates that have a sustained fast juvenile growth with only a minor period of puberty acceleration. The existence of slow childhood growth in humans has been shown to date back to 160,000 BP. 
6.	Human children physiologically have a limited capacity to protect the brain from plasma glucose fluctuations and other metabolic disruptions. These can arise in adults, during prolonged strenuous exercise when skeletal muscle depletes plasma glucose, and produces other metabolic disruptions upon the brain (hypoxia, hyperthermia, dehydration and hyperammonemia). These are proportional to muscle mass.
7.	Children show specific adaptations to minimize such metabolic disturbances. (i) Due to slow body growth and resulting small body size, they have limited skeletal muscle mass. (ii) They show other adaptations such as an exercise specific preference for free fatty acid metabolism. (iii) While children are generally more active than adolescents and adults, they avoid physically prolonged intense exertion. 
8.	Childhood has a close relationship to high levels of energy provision and metabolic adaptations that support prolonged synaptic neurodevelopment. 

Wiring optimization explanation in neuroscience: What is Special about it?
This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system
Morphological plasticity of astroglia: Understanding synaptic microenvironment
Memory formation in the brain is thought to rely on the remodeling of synaptic connections which eventually results in neural network rewiring. This remodeling is likely to involve ultrathin astroglial protrusions which often occur in the immediate vicinity of excitatory synapses. The phenomenology, cellular mechanisms, and causal relationships of such astroglial restructuring remain, however, poorly understood. This is in large part because monitoring and probing of the underpinning molecular machinery on the scale of nanoscopic astroglial compartments remains a challenge. Here we briefly summarize the current knowledge regarding the cellular organisation of astroglia in the synaptic microenvironment and discuss molecular mechanisms potentially involved in use-dependent astroglial morphogenesis. We also discuss recent observations concerning morphological astroglial plasticity, the respective monitoring methods, and some of the newly emerging techniques that might help with conceptual advances in the area. GLIA 2015
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