Decorrelation and efficient coding by retinal ganglion cells

An influential theory of visual processing asserts that retinal center-surround receptive fields remove spatial correlations in the visual world, producing ganglion cell spike trains that are less redundant than the corresponding image pixels. For bright, high-contrast images, this decorrelation would enhance coding efficiency in optic nerve fibers of limited capacity. We tested the central prediction of the theory and found that the spike trains of retinal ganglion cells were indeed decorrelated compared with the visual input. However, most of the decorrelation was accomplished not by the receptive fields, but by nonlinear processing in the retina. We found that a steep response threshold enhanced efficient coding by noisy spike trains and that the effect of this nonlinearity was near optimal in both salamander and macaque retina. These results offer an explanation for the sparseness of retinal spike trains and highlight the importance of treating the full nonlinear character of neural codes

Designing nonlinear thermal devices and metamaterials under the Fourier's law: A route to nonlinear thermotics

Nonlinear heat transfer can be exploited to reveal novel transport phenomena and thus enhance peo-ple's ability to manipulate heat flux at will. However, there hasn't been a mature discipline called nonlinear thermotics like its counterpart in optics or acoustics to make a systematic summary of rele-vant researches. In the current review, we focus on recent progress in an important part of nonlinear heat transfer, i.e., tailoring nonlinear thermal devices and metamaterials under the Fourier's law, especially with temperature-dependent thermal conductivities. We will present the basic designing techniques including solving the equation directly and the transformation theory. Tuning nonlinearity coming from multi-physical effects, and how to calculate effective properties of nonlinear conductive composites using the effective medium theory are also included. Based on these theories, researchers have successfully designed various functional materials and devices such as the thermal diodes, thermal transistors, thermal memory elements, energy-free thermostats, and intelligent thermal materials, and some of them have also been realized in experiments. Further, these phenomenological works can provide a feasible route for the development of nonlinear thermotics

Fluctuating noise drives Brownian transport

The transport properties of Brownian ratchet was studied in the presence of stochastic intensity noise (SIN) in both overdamped and underdamped regimes. In the overdamped case, analytical solution using the matrix continued fraction method revealed the existence of a maximum current when the noise intensity fluctuates on intermediate time scale regions. Similar effects were observed for the underdamped case by Monte Carlo simulations. The optimal time-correlation for the Brownian transport coincided with the experimentally observed time-correlation of the extrinsic noise in Esherichia coli gene expression and implied the importance of environmental noise for molecular mechanisms.Comment: 22 pages, 8 figure

Predicting the Electrophysiological Responses of Murine Alpha Retinal Ganglion Cells to Artificial and Natural Visual Stimuli

The retina sends many parallel channels of visual information to the brain through the axons of >20 retinal ganglion cell (RGC) populations. The purpose of these distinct circuits for vision remains an open question. Recent results suggest that each cell type responds selectively to a specific feature of the visual scene. These conclusions are derived primarily from experiments with artificial visual stimuli. It is unknown whether the insights gathered under such conditions extend to the natural environment in which the retina evolved. One can address this question by building a mathematical model of RGC responses to artificial stimuli and then testing how well that same model performs with natural visual input. For several RGC types this exercise has failed dramatically, indicating an imperfect understanding of their neural code. Here we focus on the mouse alpha RGCs, which possess large cell bodies, stout axons, and wide receptive fields. Three subtypes had been previously defined based on their responses to light steps: On-Sustained, Off-Sustained, and Off-Transient. We targeted these RGCs for recording using a transgenic mouse line in which GFP is expressed in all alpha subtypes. Quantitative analysis of the recorded light responses revealed four distinct physiological cell types: an On-Transient alpha RGC in addition to the other three type previously identified. Using both artificial stimuli and natural movies, we measured the visual responses of the mouse alpha cells. We then constructed a simple cascade-style model to link the stimulus to the firing rate. Based on electrophysiological recording and modeling, we found the visual messages the four alpha RGCs send to the brain to be similar in that they are minimally processed versions of the visual scene. Spatial averaging minimally influenced the responses of the alpha RGCs to the natural movies. Additionally, a simple linear- nonlinear model accounted very well for the visual responses of all four alpha RGC subtypes, correctly predicting at least 70% of the variance in firing. The same model worked for both artificial stimuli (e.g. random flicker) and natural stimuli (mouse-cam and simulated-mouse movies). This successful account of alpha cell function will be valuable as a retina model for understanding cortical vision in the behaving mouse

Modeling of real pH Neutralization Process using Multiple Neural Networks (MNN) Combination Technique.

Combining multiple neural networks appears to be a very promising approach in improving neural network generalisation since it is very difficult, if not impossible, to develop a perfect single neural network (SNN) especially when dealing with a real time data

Feedback from retinal ganglion cells to the inner retina

Retinal ganglion cells (RGCs) are thought to be strictly postsynaptic within the retina. They carry visual signals from the eye to the brain, but do not make chemical synapses onto other retinal neurons. Nevertheless, they form gap junctions with other RGCs and amacrine cells, providing possibilities for RGC signals to feed back into the inner retina. Here we identified such feedback circuitry in the salamander and mouse retinas. First, using biologically inspired circuit models, we found mutual inhibition among RGCs of the same type. We then experimentally determined that this effect is mediated by gap junctions with amacrine cells. Finally, we found that this negative feedback lowers RGC visual response gain without affecting feature selectivity. The principal neurons of the retina therefore participate in a recurrent circuit much as those in other brain areas, not being a mere collector of retinal signals, but are actively involved in visual computations