Controlled motion of Janus particles in periodically phase-separating binary fluids

We numerically investigate the propelled motions of a Janus particle in a periodically phase-separating binary fluid mixture. In this study, the surface of the particle tail prefers one of the binary fluid components and the particle head is neutral in the wettability. During the demixing period, the more wettable phase is selectively adsorbed to the particle tail. Growths of the adsorbed domains induce the hydrodynamic flow in the vicinity of the particle tail, and this asymmetric pumping flow drives the particle toward the particle head. During the mixing period, the particle motion almost ceases because the mixing primarily occurs via diffusion and the resulting hydrodynamic flow is negligibly small. Repeating this cycle unboundedly moves the Janus particle toward the head. The dependencies of the composition and the repeat frequency on the particle motion are discussed.Comment: 11 pages, 9 figure

Spontaneous and stimulus-induced coherent states of critically balanced neuronal networks

How the information microscopically processed by individual neurons is integrated and used in organizing the behavior of an animal is a central question in neuroscience. The coherence of neuronal dynamics over different scales has been suggested as a clue to the mechanisms underlying this integration. Balanced excitation and inhibition may amplify microscopic fluctuations to a macroscopic level, thus providing a mechanism for generating coherent multiscale dynamics. Previous theories of brain dynamics, however, were restricted to cases in which inhibition dominated excitation and suppressed fluctuations in the macroscopic population activity. In the present study, we investigate the dynamics of neuronal networks at a critical point between excitation-dominant and inhibition-dominant states. In these networks, the microscopic fluctuations are amplified by the strong excitation and inhibition to drive the macroscopic dynamics, while the macroscopic dynamics determine the statistics of the microscopic fluctuations. Developing a novel type of mean-field theory applicable to this class of interscale interactions, we show that the amplification mechanism generates spontaneous, irregular macroscopic rhythms similar to those observed in the brain. Through the same mechanism, microscopic inputs to a small number of neurons effectively entrain the dynamics of the whole network. These network dynamics undergo a probabilistic transition to a coherent state, as the magnitude of either the balanced excitation and inhibition or the external inputs is increased. Our mean-field theory successfully predicts the behavior of this model. Furthermore, we numerically demonstrate that the coherent dynamics can be used for state-dependent read-out of information from the network. These results show a novel form of neuronal information processing that connects neuronal dynamics on different scales.Comment: 20 pages 12 figures (main text) + 23 pages 6 figures (Appendix); Some of the results have been removed in the revision in order to reduce the volume. See the previous version for more result

Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly nonrandom, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance

Trace Analysis of Marine Organisms: A Comparison of Activation Analysis and Conventional Methods

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109766/1/lno1959440398.pd

Amino acid concentra­tion in different parts of the dog brain

The present paper describes each pattern of the free amino acids in different parts of the dog brain determined by ion-exchange chromatography. The parts examined have been the cerebral cortex, cerebral white matter, cerebellar hemisphere, cerebellar vermis, caudate nucleus, thalamus, hypothalamus, and medulla oblongata. Gamma-aminobutyric acid concentration was the highest in the hypothalamus. Glutamic acid showed lower values in the white matter, hypothalamus, and medulla oblongata. Aspartic acid showed lower values in the white matter and caudate nucleus and higher values in the medulla oblongata. Glutathione and cystathionine showed higher values in the thalamus. N-Acetylaspartic acid showed lower values in the white matter and medulla oblongata. Glycine and alanine showed higher values in the medulla oblongata.</p