Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Motif Statistics and Spike Correlations in Neuronal Networks

Motifs are patterns of subgraphs of complex networks. We studied the impact of such patterns of connectivity on the level of correlated, or synchronized, spiking activity among pairs of cells in a recurrent network model of integrate and fire neurons. For a range of network architectures, we find that the pairwise correlation coefficients, averaged across the network, can be closely approximated using only three statistics of network connectivity. These are the overall network connection probability and the frequencies of two second-order motifs: diverging motifs, in which one cell provides input to two others, and chain motifs, in which two cells are connected via a third intermediary cell. Specifically, the prevalence of diverging and chain motifs tends to increase correlation. Our method is based on linear response theory, which enables us to express spiking statistics using linear algebra, and a resumming technique, which extrapolates from second order motifs to predict the overall effect of coupling on network correlation. Our motif-based results seek to isolate the effect of network architecture perturbatively from a known network state

How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A model-based argumentation

This paper addresses two questions in the context of neuronal networks dynamics, using methods from dynamical systems theory and statistical physics: (i) How to characterize the statistical properties of sequences of action potentials ("spike trains") produced by neuronal networks ? and; (ii) what are the effects of synaptic plasticity on these statistics ? We introduce a framework in which spike trains are associated to a coding of membrane potential trajectories, and actually, constitute a symbolic coding in important explicit examples (the so-called gIF models). On this basis, we use the thermodynamic formalism from ergodic theory to show how Gibbs distributions are natural probability measures to describe the statistics of spike trains, given the empirical averages of prescribed quantities. As a second result, we show that Gibbs distributions naturally arise when considering "slow" synaptic plasticity rules where the characteristic time for synapse adaptation is quite longer than the characteristic time for neurons dynamics.Comment: 39 pages, 3 figure

Quantum Gravity Constraints on Inflation

We study quantum gravity constraints on inflationary model building. Our approach is based on requiring the entropy associated to a given inflationary model to be less than that of the de Sitter entropy. We give two prescriptions for determining the inflationary entropy, based on either `bits per unit area' or entanglement entropy. The existence of transPlanckian flat directions, necessary for large tensor modes in the CMB, correlates with an inflationary entropy greater than that allowed by de Sitter space. Independently these techniques also constrain or exclude de Sitter models with large-rank gauge groups and high UV cutoffs, such as racetrack inflation or the KKLT construction.Comment: 22 pages; v2 references adde

Stable Propagation of a Burst Through a One-Dimensional Homogeneous Excitatory Chain Model of Songbird Nucleus HVC

We demonstrate numerically that a brief burst consisting of two to six spikes can propagate in a stable manner through a one-dimensional homogeneous feedforward chain of non-bursting neurons with excitatory synaptic connections. Our results are obtained for two kinds of neuronal models, leaky integrate-and-fire (LIF) neurons and Hodgkin-Huxley (HH) neurons with five conductances. Over a range of parameters such as the maximum synaptic conductance, both kinds of chains are found to have multiple attractors of propagating bursts, with each attractor being distinguished by the number of spikes and total duration of the propagating burst. These results make plausible the hypothesis that sparse precisely-timed sequential bursts observed in projection neurons of nucleus HVC of a singing zebra finch are intrinsic and causally related.Comment: 13 pages, 6 figure

Rhythmogenic neuronal networks, pacemakers, and k-cores

Neuronal networks are controlled by a combination of the dynamics of individual neurons and the connectivity of the network that links them together. We study a minimal model of the preBotzinger complex, a small neuronal network that controls the breathing rhythm of mammals through periodic firing bursts. We show that the properties of a such a randomly connected network of identical excitatory neurons are fundamentally different from those of uniformly connected neuronal networks as described by mean-field theory. We show that (i) the connectivity properties of the networks determines the location of emergent pacemakers that trigger the firing bursts and (ii) that the collective desensitization that terminates the firing bursts is determined again by the network connectivity, through k-core clusters of neurons.Comment: 4+ pages, 4 figures, submitted to Phys. Rev. Let

Transplanckian axions !?

We discuss quantum gravitational effects in Einstein theory coupled to periodic axion scalars to analyze the viability of several proposals to achieve superplanckian axion periods (aka decay constants) and their possible application to large field inflation models. The effects we study correspond to the nucleation of euclidean gravitational instantons charged under the axion, and our results are essentially compatible with (but independent of) the Weak Gravity Conjecture, as follows: Single axion theories with superplanckian periods contain gravitational instantons inducing sizable higher harmonics in the axion potential, which spoil superplanckian inflaton field range. A similar result holds for multi-axion models with lattice alignment (like the Kim-Nilles-Peloso model). Finally, theories with $N$ axions can still achieve a moderately superplanckian periodicity (by a $\sqrt{N}$ factor) with no higher harmonics in the axion potential. The Weak Gravity Conjecture fails to hold in this case due to the absence of some instantons, which are forbidden by a discrete $\mathbf{Z}_N$ gauge symmetry. Finally we discuss the realization of these instantons as euclidean D-branes in string compactifications.Comment: 46 pages, 6 figures. Added references, clarifications, and missing factor of 1/2 to instanton action. Conclusions unchange

Multifield Dynamics in Higgs-otic Inflation

In Higgs-otic inflation a complex neutral scalar combination of the $h^0$ and $H^0$ MSSM Higgs fields plays the role of inflaton in a chaotic fashion. The potential is protected from large trans-Planckian corrections at large inflaton if the system is embedded in string theory so that the Higgs fields parametrize a D-brane position. The inflaton potential is then given by a DBI+CS D-brane action yielding an approximate linear behaviour at large field. The inflaton scalar potential is a 2-field model with specific non-canonical kinetic terms. Previous computations of the cosmological parameters (i.e. scalar and tensor perturbations) did not take into account the full 2-field character of the model, ignoring in particular the presence of isocurvature perturbations and their coupling to the adiabatic modes. It is well known that for generic 2-field potentials such effects may significantly alter the observational signatures of a given model. We perform a full analysis of adiabatic and isocurvature perturbations in the Higgs-otic 2-field model. We show that the predictivity of the model is increased compared to the adiabatic approximation. Isocurvature perturbations moderately feed back into adiabatic fluctuations. However, the isocurvature component is exponentially damped by the end of inflation. The tensor to scalar ratio varies in a region $r=0.08-0.12$, consistent with combined Planck/BICEP results.Comment: 35 pages, 11 figure

Hydrodynamic gene delivery in human skin using a hollow microneedle device

Microneedle devices have been proposed as a minimally invasive delivery system for the intradermal administration of nucleic acids, both plasmid DNA (pDNA) and siRNA, to treat localised disease or provide vaccination. Different microneedle types and application methods have been investigated in the laboratory, but limited and irreproducible levels of gene expression have proven to be significant challenges to pre-clinical to clinical progression. This study is the first to explore the potential of a hollow microneedle device for the delivery and subsequent expression of pDNA in human skin. The regulatory approved MicronJet600® (MicronJet hereafter) device was used to deliver reporter plasmids (pCMVβ and pEGFP-N1) into viable excised human skin. Exogenous gene expression was subsequently detected at multiple locations that were distant from the injection site but within the confines of the bleb created by the intradermal bolus. The observed levels of gene expression in the tissue are at least comparable to that achieved by the most invasive microneedle application methods e.g. lateral application of a microneedle. Gene expression was predominantly located in the epidermis, although also evident in the papillary dermis. Optical coherence tomography permitted real time visualisation of the sub-surface skin architecture and, unlike a conventional intradermal injection, MicronJet administration of a 50 μL bolus appears to create multiple superficial microdisruptions in the papillary dermis and epidermis. These were co-localised with expression of the pCMVβ reporter plasmid. We have therefore shown, for the first time, that a hollow microneedle device can facilitate efficient and reproducible gene expression of exogenous naked pDNA in human skin using volumes that are considered to be standard for intradermal administration, and postulate a hydrodynamic effect as the mechanism of gene delivery