A Moving Bump in a Continuous Manifold: A Comprehensive Study of the Tracking Dynamics of Continuous Attractor Neural Networks

Understanding how the dynamics of a neural network is shaped by the network structure, and consequently how the network structure facilitates the functions implemented by the neural system, is at the core of using mathematical models to elucidate brain functions. This study investigates the tracking dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of neuronal recurrent interactions, CANNs can hold a continuous family of stationary states. They form a continuous manifold in which the neural system is neutrally stable. We systematically explore how this property facilitates the tracking performance of a CANN, which is believed to have clear correspondence with brain functions. By using the wave functions of the quantum harmonic oscillator as the basis, we demonstrate how the dynamics of a CANN is decomposed into different motion modes, corresponding to distortions in the amplitude, position, width or skewness of the network state. We then develop a perturbative approach that utilizes the dominating movement of the network's stationary states in the state space. This method allows us to approximate the network dynamics up to an arbitrary accuracy depending on the order of perturbation used. We quantify the distortions of a Gaussian bump during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable and the reaction time for the network to catch up with an abrupt change in the stimulus.Comment: 43 pages, 10 figure

Minimizing Unsatisfaction in Colourful Neighbourhoods

Colouring sparse graphs under various restrictions is a theoretical problem of significant practical relevance. Here we consider the problem of maximizing the number of different colours available at the nodes and their neighbourhoods, given a predetermined number of colours. In the analytical framework of a tree approximation, carried out at both zero and finite temperatures, solutions obtained by population dynamics give rise to estimates of the threshold connectivity for the incomplete to complete transition, which are consistent with those of existing algorithms. The nature of the transition as well as the validity of the tree approximation are investigated.Comment: 28 pages, 12 figures, substantially revised with additional explanatio

Dissociation cross sections of ground-state and excited charmonia with light mesons in the quark model

We present numerical results for the dissociation cross sections of ground-state, orbitally- and radially-excited charmonia in collisions with light mesons. Our results are derived using the nonrelativistic quark model, so all parameters are determined by fits to the experimental meson spectrum. Examples of dissociation into both exclusive and inclusive final states are considered. The dissociation cross sections of several C=(+) charmonia may be of considerable importance for the study of heavy ion collisions, since these states are expected to be produced more copiously than the J/psi. The relative importance of the productions of ground-state and orbitally-excited charmed mesons in a pion-charmonium collision is demonstrated through the $\sqrt {s}$-dependent charmonium dissociation cross sections.Comment: 9 pages, 8 figure

Dynamical and Stationary Properties of On-line Learning from Finite Training Sets

The dynamical and stationary properties of on-line learning from finite training sets are analysed using the cavity method. For large input dimensions, we derive equations for the macroscopic parameters, namely, the student-teacher correlation, the student-student autocorrelation and the learning force uctuation. This enables us to provide analytical solutions to Adaline learning as a benchmark. Theoretical predictions of training errors in transient and stationary states are obtained by a Monte Carlo sampling procedure. Generalization and training errors are found to agree with simulations. The physical origin of the critical learning rate is presented. Comparison with batch learning is discussed throughout the paper.Comment: 30 pages, 4 figure

Supersymmetry Breaking in the Early Universe

Supersymmetry breaking in the early universe induces scalar soft potentials with curvature of order the Hubble constant. This has a dramatic effect on the coherent production of scalar fields along flat directions. For the moduli problem it generically gives a concrete realization of the problem by determining the field value subsequent to inflation. However it might suggest a solution if the minimum of the induced potential coincides with the true minimum. The induced Hubble scale mass also has important implications for the Affleck-Dine mechanism of baryogenesis. This mechanism requires large squark or slepton expectation values to develop along flat directions in the early universe. This is generally not the case if the induced mass squared is positive, but does occur if it is negative. The resulting baryon to entropy ratio depends mainly on the dimension of the nonrenormalizable operator in the superpotential which stabilizes the flat direction, and the reheat temperature after inflation. Unlike the original scenario, it is possible to obtain an acceptable baryon asymmetry without subsequent entropy releases.Comment: 11 pages, requires phyzz

Next nearest neighbour Ising models on random graphs

This paper develops results for the next nearest neighbour Ising model on random graphs. Besides being an essential ingredient in classic models for frustrated systems, second neighbour interactions interactions arise naturally in several applications such as the colour diversity problem and graphical games. We demonstrate ensembles of random graphs, including regular connectivity graphs, that have a periodic variation of free energy, with either the ratio of nearest to next nearest couplings, or the mean number of nearest neighbours. When the coupling ratio is integer paramagnetic phases can be found at zero temperature. This is shown to be related to the locked or unlocked nature of the interactions. For anti-ferromagnetic couplings, spin glass phases are demonstrated at low temperature. The interaction structure is formulated as a factor graph, the solution on a tree is developed. The replica symmetric and energetic one-step replica symmetry breaking solution is developed using the cavity method. We calculate within these frameworks the phase diagram and demonstrate the existence of dynamical transitions at zero temperature for cases of anti-ferromagnetic coupling on regular and inhomogeneous random graphs.Comment: 55 pages, 15 figures, version 2 with minor revisions, to be published J. Stat. Mec

Statistical mechanics of image restoration and error-correcting codes

We develop a statistical-mechanical formulation for image restoration and error-correcting codes. These problems are shown to be equivalent to the Ising spin glass with ferromagnetic bias under random external fields. We prove that the quality of restoration/decoding is maximized at a specific set of parameter values determined by the source and channel properties. For image restoration in mean-field system a line of optimal performance is shown to exist in the parameter space. These results are illustrated by solving exactly the infinite-range model. The solutions enable us to determine how precisely one should estimate unknown parameters. Monte Carlo simulations are carried out to see how far the conclusions from the infinite-range model are applicable to the more realistic two-dimensional case in image restoration.Comment: 20 pages, 9 figures, ReVTe

Improving Signal and Photobleaching Characteristics of Temporal Focusing Microscopy with the Increase in Pulse Repetition Rate

Wide-field temporal focused (WF-TeFo) two-photon microscopy allows for the simultaneous imaging of a large planar area, with a potential order of magnitude enhancement in the speed of volumetric imaging. To date, low repetition rate laser sources with over half a millijoule per pulse have been required in order to provide the high peak power densities for effective two-photon excitation over the large area. However, this configuration suffers from reduced signal intensity due to the low repetition rate, saturation effects due to increased excitation fluences, as well as faster photobleaching of the fluorescence probe. In contrast, with the recent advent of high repetition rate, high pulse energy laser systems could potentially provide the advantages of high repetition rate systems that are seen in traditional two-photon microscopes, while minimizing the negatives of high fluences in WF-TeFo setups to date. Here, we use a 100 microjoule/high repetition rate (50-100 kHz) laser system to investigate the performance of a WF-TeFo two-photon microscope. While using micro-beads as a sample, we demonstrate a proportionate increase in signal intensity with repetition rate, at no added cost in photobleaching. By decreasing pulse intensity, via a corresponding increase in repetition rate to maintain fluorescence signal intensity, we find that the photobleaching rate is reduced by ~98.4%. We then image live C. elegans at a high repetition rate for 25 min. as a proof-of-principle. Lastly, we identify the steady state temperature increase as the limiting process in further increasing the repetition rate, and we estimate that repetition rate in the range between 0.5 and 5 MHz is ideal for live imaging with a simple theoretical model. With new generation low-cost fiber laser systems offering high pulse energy/high repetition rates in what is essentially a turn-key solution, we anticipate increased adoption of this microscopy technique by the neuroscience community