26 research outputs found
Global and regional brain metabolic scaling and its functional consequences
Background: Information processing in the brain requires large amounts of
metabolic energy, the spatial distribution of which is highly heterogeneous
reflecting complex activity patterns in the mammalian brain.
Results: Here, it is found based on empirical data that, despite this
heterogeneity, the volume-specific cerebral glucose metabolic rate of many
different brain structures scales with brain volume with almost the same
exponent around -0.15. The exception is white matter, the metabolism of which
seems to scale with a standard specific exponent -1/4. The scaling exponents
for the total oxygen and glucose consumptions in the brain in relation to its
volume are identical and equal to , which is significantly larger
than the exponents 3/4 and 2/3 suggested for whole body basal metabolism on
body mass.
Conclusions: These findings show explicitly that in mammals (i)
volume-specific scaling exponents of the cerebral energy expenditure in
different brain parts are approximately constant (except brain stem
structures), and (ii) the total cerebral metabolic exponent against brain
volume is greater than the much-cited Kleiber's 3/4 exponent. The
neurophysiological factors that might account for the regional uniformity of
the exponents and for the excessive scaling of the total brain metabolism are
discussed, along with the relationship between brain metabolic scaling and
computation.Comment: Brain metabolism scales with its mass well above 3/4 exponen
On How Network Architecture Determines the Dominant Patterns of Spontaneous Neural Activity
In the absence of sensory stimulation, neocortical circuits display complex patterns of neural activity. These patterns are thought to reflect relevant properties of the network, including anatomical features like its modularity. It is also assumed that the synaptic connections of the network constrain the repertoire of emergent, spontaneous patterns. Although the link between network architecture and network activity has been extensively investigated in the last few years from different perspectives, our understanding of the relationship between the network connectivity and the structure of its spontaneous activity is still incomplete. Using a general mathematical model of neural dynamics we have studied the link between spontaneous activity and the underlying network architecture. In particular, here we show mathematically how the synaptic connections between neurons determine the repertoire of spatial patterns displayed in the spontaneous activity. To test our theoretical result, we have also used the model to simulate spontaneous activity of a neural network, whose architecture is inspired by the patchy organization of horizontal connections between cortical columns in the neocortex of primates and other mammals. The dominant spatial patterns of the spontaneous activity, calculated as its principal components, coincide remarkably well with those patterns predicted from the network connectivity using our theory. The equivalence between the concept of dominant pattern and the concept of attractor of the network dynamics is also demonstrated. This in turn suggests new ways of investigating encoding and storage capabilities of neural networks
High-pressure structural studies of energetic compounds
The phase diagram of the energetic material hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) has been explored using X-ray and neutron diffraction from ambient pressure to 8GPa. Two phases of RDX have been structurally characterized. The high-pressure -polymorph exists above 4GPa while the metastable -polymorph has been isolated from the high boiling point solvent nitrobenzene. Interestingly, neutron diffraction measurements at high pressures and high temperatures show that the -polymorph isolated under ambient conditions is not the same polymorph as can be accessed at high pressures and temperatures