Thinning and thickening in active microrheology

When pulling a probe particle in a many-particle system with fixed velocity, the probe's effective friction, defined as average pulling force over its velocity, $\gamma_{eff}:=\langle F_{ex}\rangle/u$, first keeps constant (linear response), then decreases (thinning) and finally increases (thickening). We propose a three-time-scales picture (TTSP) to unify thinning and thickening behaviour. The points of the TTSP are that there are three distinct time scales of bath particles: diffusion, damping, and single probe-bath (P-B) collision; the dominating time scales, which are controlled by the pulling velocity, determine the behaviour of the probe's friction. We confirm the TTSP by Langevin dynamics simulation. Microscopically, we find that for computing the effective friction, Maxwellian distribution of bath particles' velocities works in low Reynolds number (Re) but fails in high Re. It can be understood based on the microscopic mechanism of thickening obtained in the $T=0$ limit. Based on the TTSP, we explain different thinning and thickening observations in some earlier literature

A Unified Quantitative Model of Vision and Audition

We have put forwards a unified quantitative framework of vision and audition, based on existing data and theories. According to this model, the retina is a feedforward network self-adaptive to inputs in a specific period. After fully grown, cells become specialized detectors based on statistics of stimulus history. This model has provided explanations for perception mechanisms of colour, shape, depth and motion. Moreover, based on this ground we have put forwards a bold conjecture that single ear can detect sound direction. This is complementary to existing theories and has provided better explanations for sound localization.Comment: 7 pages, 3 figure

Motor Learning Mechanism on the Neuron Scale

Based on existing data, we wish to put forward a biological model of motor system on the neuron scale. Then we indicate its implications in statistics and learning. Specifically, neuron firing frequency and synaptic strength are probability estimates in essence. And the lateral inhibition also has statistical implications. From the standpoint of learning, dendritic competition through retrograde messengers is the foundation of conditional reflex and grandmother cell coding. And they are the kernel mechanisms of motor learning and sensory motor integration respectively. Finally, we compare motor system with sensory system. In short, we would like to bridge the gap between molecule evidences and computational models.Comment: 8 pages, 4 figure