Determination of the ΔS=1\Delta S = 1 weak Hamiltonian in the SU(4) chiral limit through topological zero-mode wave functions

A new method to determine the low-energy couplings of the $\Delta S=1$ weak Hamiltonian is presented. It relies on a matching of the topological poles in $1/m^2$ of three-point correlators of two pseudoscalar densities and a four-fermion operator, measured in lattice QCD, to the same observables computed in the $\epsilon$-regime of chiral perturbation theory. We test this method in a theory with a light charm quark, i.e. with an SU(4) flavour symmetry. Quenched numerical measurements are performed in a 2 fm box, and chiral perturbation theory predictions are worked out up to next-to-leading order. The matching of the two sides allows to determine the weak low-energy couplings in the SU(4) limit. We compare the results with a previous determination, based on three-point correlators containing two left-handed currents, and discuss the merits and drawbacks of the two procedures.Comment: 38 pages, 9 figure

Weak low-energy couplings from topological zero-mode wavefunctions

We discuss a new method to determine the low-energy couplings of the $\Delta S=1$ weak Hamiltonian in the $\epsilon$-regime. It relies on a matching of the topological poles in $1/m^2$ of three-point functions of two pseudoscalar densities and a four-fermion operator computed in lattice QCD, to the same observables in the Chiral Effective Theory. We present the results of a NLO computation in chiral perturbation theory of these correlation functions together with some preliminary numerical results.Comment: 7 pages. Contribution to Lattice 200

K-->pipi amplitudes from lattice QCD with a light charm quark

We compute the leading-order low-energy constants of the DeltaS=1 effective weak Hamiltonian in the quenched approximation of QCD with up, down, strange, and charm quarks degenerate and light. They are extracted by comparing the predictions of finite volume chiral perturbation theory with lattice QCD computations of suitable correlation functions carried out with quark masses ranging from a few MeV up to half of the physical strange mass. We observe a large DeltaI=1/2 enhancement in this corner of the parameter space of the theory. Although matching with the experimental result is not observed for the DeltaI=1/2 amplitude, our computation suggests large QCD contributions to the physical DeltaI=1/2 rule in the GIM limit, and represents the first step to quantify the role of the charm quark-mass in K-->pipi amplitudes.Comment: 4 pages, 1 figure. Minor modifications. Final version to appear on PR

Recruitment of visual cortex for language processing in blind individuals: A neurobiological model

After sensory deprivation, the visual cortex is functionally recruited into non-visual cognitive language and semantic processing. Why this functional organization takes place and how its underlying mechanisms work at the neuronal circuit level is still unclear. Here, we use a biologically constrained network model implementing anatomical structure, neurophysiological function and connectivity of the fronto-tempo-occipital cortex to simulate word-meaning acquisition in visually deprived and undeprived (‘healthy control’) brains. Whereas in the ‘undeprived’ simulations only words denoting visual entities grew into the visual domain, the ‘blind’ models unexpectedly produced word-related neuronal circuits extending into visual cortex for all semantic categories (and especially for those carrying action-related meaning). Additionally, during word recognition, the blind model showed long-lasting spiking neural activity compared to the sighted model, a sign for enhanced verbal working memory due to the additional neural recruitment. Three factors are crucial for explaining this deprivation-related growth: (i) changes in the network’s activity balance brought about by the absence of uncorrelated sensory input, (ii) the connectivity structure of the network, and (iii) Hebbian correlation learning. By offering a neurobiological account for neural changes of language processing due to visual deprivation, our model bridges the gap between cellular-level mechanisms and system-level language function in blind humans

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

On the determination of low-energy constants for ΔS=1\Delta S=1 transitions

We present our preliminary results for three-point correlation functions involving the operators entering the $\Delta{S}=1$ effective Hamiltonian with an active charm quark, obtained using overlap fermions in the quenched approximation. This is the first computation carried out for valence quark masses small enough so as to permit a matching to Quenched Chiral Perturbation Theory in the $\epsilon$-regime. The commonly observed large statistical fluctuations are tamed by means of low-mode averaging techniques, combined with restrictions to individual topological sectors. We also discuss the matching of the resulting hadronic matrix elements to the effective low-energy constants for $\Delta{S}=1$ transitions. This involves (a) finite-volume corrections which can be evaluated at NLO in Quenched Chiral Perturbation Theory, and (b) the short-distance renormalization of the relevant four-quark operators in discretizations based on the overlap operator. We discuss perturbative estimates for the renormalization factors and possible strategies for their non-perturbative evaluation. Our results can be used to isolate the long-distance contributions to the $\Delta I=1/2$ rule, coming from physics effects around the intrinsic QCD scale.Comment: 11 pages, 2 figures, talks presented at Lattice 2005 (Weak matrix elements

Correlation functions at small quark masses with overlap fermions

We report on recent work on the determination of low-energy constants describing Delta{S}=1 weak transitions, in order to investigate the origins of the Delta{I}=1/2 rule. We focus on numerical techniques designed to enhance the statistical signal in three-point correlation functions computed with overlap fermions near the chiral limit.Comment: Talk presented at Lattice2004(weak), Fermilab, 21-26 June 2004, 3 pages, 2 figure

Thinking in circuits: toward neurobiological explanation in cognitive neuroscience

Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with a neurobiological model that makes concrete the neuronal circuits that carry thoughts and meaning. Brain theory, in particular the Hebb-inspired neurocybernetic proposals by Braitenberg, now offers an avenue toward explaining brain–mind relationships and to spell out cognition in terms of neuron circuits in a neuromechanistic sense. Central to this endeavor is the theoretical construct of an elementary functional neuronal unit above the level of individual neurons and below that of whole brain areas and systems: the distributed neuronal assembly (DNA) or thought circuit (TC). It is shown that DNA/TC theory of cognition offers an integrated explanatory perspective on brain mechanisms of perception, action, language, attention, memory, decision and conceptual thought. We argue that DNAs carry all of these functions and that their inner structure (e.g., core and halo subcomponents), and their functional activation dynamics (e.g., ignition and reverberation processes) answer crucial localist questions, such as why memory and decisions draw on prefrontal areas although memory formation is normally driven by information in the senses and in the motor system. We suggest that the ability of building DNAs/TCs spread out over different cortical areas is the key mechanism for a range of specifically human sensorimotor, linguistic and conceptual capacities and that the cell assembly mechanism of overlap reduction is crucial for differentiating a vocabulary of actions, symbols and concepts

Evolution of network structure by temporal learning

We study the effect of learning dynamics on network topology. A network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity. This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution.Comment: revised manuscript in communicatio

Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning

In blind people, the visual cortex takes on higher cognitive functions, including language. Why this functional organisation mechanistically emerges at the neuronal circuit level is still unclear. Here, we use a biologically constrained network model implementing features of anatomical structure, neurophysiological function and connectivity of fronto-temporal-occipital areas to simulate word-meaning acquisition in visually deprived and undeprived brains. We observed that, only under visual deprivation, distributed word-related neural circuits ‘grew into’ the deprived visual areas, which therefore adopted a linguistic-semantic role. Three factors are crucial for explaining this deprivation-related growth: changes in the network’s activity balance brought about by the absence of uncorrelated sensory input, the connectivity structure of the network, and Hebbian correlation learning. In addition, the blind model revealed long-lasting spiking neural activity compared to the sighted model during word recognition, which is a neural correlate of enhanced verbal working memory. The present neurocomputational model offers a neurobiological account for neural changes followed by sensory deprivation, thus closing the gap between cellular-level mechanisms, system-level linguistic and semantic function