ConversationPiece II: Displaced and Rehacked

Abstract: Conversations are amazing! Although we usually find the experience enjoyable and even relaxing, when one considers the difficulties of simultaneously generating sig- nals that convey an intended message while at the same time trying to understand the messages of another, then the pleasures of conversation may seem rather surprising. We manage to communicate with each other without knowing quite what will happen next. We quickly manufacture precisely timed sounds and gestures on the fly, which we exchange with each other without clashing—even managing to slip in some imita- tions as we go along! Yet usually meaning is all we really notice. In the Conversa- tionPiece project, we aim to transform conversations into musical sounds using neuro-inspired technology to expose the amazing world of sounds people create when talking with others. Sounds from a microphone are separated into different fre- quency bands by a computer-simulated “ear” (more precisely “basilar membrane”) and analyzed for tone onsets using a lateral-inhibition network, similar to some cor- tical neural networks. The detected events are used to generate musical notes played on a synthesizer either instantaneously or delayed. The first option allows for ex- changing timed sound events between two speakers with a speech-like structure, but without conveying (much) meaning. Delayed feedback further allows self-exploration of one’s own speech. We discuss the current setup (ConversationPiece version II), in- sights from first experiments, and options for future applications

Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity.

It has previously been shown that by using spike-timing-dependent plasticity (STDP), neurons can adapt to the beginning of a repeating spatio-temporal firing pattern in their input. In the present work, we demonstrate that this mechanism can be extended to train recognizers for longer spatio-temporal input signals. Using a number of neurons that are mutually connected by plastic synapses and subject to a global winner-takes-all mechanism, chains of neurons can form where each neuron is selective to a different segment of a repeating input pattern, and the neurons are feed-forwardly connected in such a way that both the correct input segment and the firing of the previous neurons are required in order to activate the next neuron in the chain. This is akin to a simple class of finite state automata. We show that nearest-neighbor STDP (where only the pre-synaptic spike most recent to a post-synaptic one is considered) leads to "nearest-neighbor" chains where connections only form between subsequent states in a chain (similar to classic "synfire chains"). In contrast, "all-to-all spike-timing-dependent plasticity" (where all pre- and post-synaptic spike pairs matter) leads to multiple connections that can span several temporal stages in the chain; these connections respect the temporal order of the neurons. It is also demonstrated that previously learnt individual chains can be "stitched together" by repeatedly presenting them in a fixed order. This way longer sequence recognizers can be formed, and potentially also nested structures. Robustness of recognition with respect to speed variations in the input patterns is shown to depend on rise-times of post-synaptic potentials and the membrane noise. It is argued that the memory capacity of the model is high, but could theoretically be increased using sparse codes

A robust sound perception model suitable for neuromorphic implementation

Coath M, Sheik S, Chicca E, Indiveri G, Denham S, Wennekers T. A robust sound perception model suitable for neuromorphic implementation. Neuromorphic Engineering. 2014;7(278):1-10.We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to “noisy” stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds

Light hadron and diquark spectroscopy in quenched QCD with overlap quarks on a large lattice

A simulation of quenched QCD with the overlap Dirac operator has been completed using 100 Wilson gauge configurations at beta = 6 on an 18^3 x 64 lattice and at beta = 5.85 on a 14^3 x 48 lattice, both in Landau gauge. We present results for light meson and baryon masses, meson final state "wave functions," and other observables, as well as some details on the numerical techniques that were used. Our results indicate that scaling violations, if any, are small. We also present an analysis of diquark correlations using the quark propagators generated in our simulation.Comment: 28 LaTeX pages, 41 figures, v2: minor updates, version published in JHE

Occupational choice, number of entrepreneurs and output: theory and empirical evidence with Spanish data

This paper extends the (Lucas, Bell J Econ 9:508–523,1978) model of occupational choices by individuals with different skills, beyond the simple options of self-employment or wage-employment, by including a second choice for the self-employed. That is, an option to hire employees and so become self-employed with employees (SEWEs), or to be self-employed without employees (SEWNEs). We solve for the market equilibrium and examine the sensitivity of relative sizes of occupational groups, and of the level of productivity, to changes in the exogenous parameters. The results show that the positive (negative) association between number of SEWEs (SEWNEs) and productivity, observed in the Spanish data, can be explained, under certain conditions, as the result of cross-region and time differences in average skills. These findings point to the importance of distinguishing between SEWEs and SEWNEs in drawing valid conclusions concerning any link between entrepreneurship and economic development

Economic Development and business Ownership: An Analysis Using Data of 23 OECD Countries in the Period 1976-1996

In the present paper we address the relationship between business ownership and economic development. We will focus upon three issues. First, how is the equilibrium rate of business ownership related to the stage of economic development? Second, what is the speed of convergence towards the equilibrium rate when the rate of business ownership is out-of-equilibrium? Third, to what extent does deviating from the equilibrium rate of business ownership hamper economic growth? Hypotheses concerning all three issues are formulated in the framework of a new two-equation model. We find confirmation for the hypothesized economic growth penalty on deviations from the equilibrium rate of business ownership using a data panel of 23 OECD countries. An important policy implication of our exercises is that low barriers to entry and exit of businesses are necessary conditions for the equilibrium seeking mechanisms that are vital for a sound economic development

Visual attention and object naming in humanoid robots using a bio-inspired spiking neural network

© 2018 The Authors Recent advances in behavioural and computational neuroscience, cognitive robotics, and in the hardware implementation of large-scale neural networks, provide the opportunity for an accelerated understanding of brain functions and for the design of interactive robotic systems based on brain-inspired control systems. This is especially the case in the domain of action and language learning, given the significant scientific and technological developments in this field. In this work we describe how a neuroanatomically grounded spiking neural network for visual attention has been extended with a word learning capability and integrated with the iCub humanoid robot to demonstrate attention-led object naming. Experiments were carried out with both a simulated and a real iCub robot platform with successful results. The iCub robot is capable of associating a label to an object with a ‘preferred’ orientation when visual and word stimuli are presented concurrently in the scene, as well as attending to said object, thus naming it. After learning is complete, the name of the object can be recalled successfully when only the visual input is present, even when the object has been moved from its original position or when other objects are present as distractors

Exploring Topology Conserving Gauge Actions for Lattice QCD

We explore gauge actions for lattice QCD, which are constructed such that the occurrence of small plaquette values is strongly suppressed. By choosing strong bare gauge couplings we arrive at values for the physical lattice spacings of O(0.1 fm). Such gauge actions tend to confine the Monte Carlo history to a single topological sector. This topological stability facilitates the collection of a large set of configurations in a specific sector, which is profitable for numerical studies in the epsilon-regime. The suppression of small plaquette values is also expected to be favourable for simulations with dynamical quarks. We use a local Hybrid Monte Carlo algorithm to simulate such actions, and we present numerical results for the static potential, the physical scale, the topological stability and the kernel condition number of the overlap Dirac operator. In addition we discuss the question of reflection positivity for a class of such gauge actions.Comment: 28 pages, 8 figure

Testing chiral effective theory with quenched lattice QCD

We investigate two-point correlation functions of left-handed currents computed in quenched lattice QCD with the Neuberger-Dirac operator. We consider two lattice spacings a~0.09,0.12 fm and two different lattice extents L~ 1.5, 2.0 fm; quark masses span both the p- and the epsilon-regimes. We compare the results with the predictions of quenched chiral perturbation theory, with the purpose of testing to what extent the effective theory reproduces quenched QCD at low energy. In the p-regime we test volume and quark mass dependence of the pseudoscalar decay constant and mass; in the epsilon-regime, we investigate volume and topology dependence of the correlators. While the leading order behaviour predicted by the effective theory is very well reproduced by the lattice data in the range of parameters that we explored, our numerical data are not precise enough to test next-to-leading order effects