Positive Correlations in Tunneling through coupled Quantum Dots

Due to the Fermi-Dirac statistics of electrons the temporal correlations of tunneling events in a double barrier setup are typically negative. Here, we investigate the shot noise behavior of a system of two capacitively coupled quantum dot states by means of a Master equation model. In an asymmetric setup positive correlations in the tunneling current can arise due to the bunching of tunneling events. The underlying mechanism will be discussed in detail in terms of the current-current correlation function and the frequency-dependent Fano factor.Comment: HCIS 13 in Moden

Super-poissonian noise, negative differential conductance, and relaxation effects in transport through molecules, quantum dots and nanotubes

We consider charge transport through a nanoscopic object, e.g. single molecules, short nanotubes, or quantum dots, that is weakly coupled to metallic electrodes. We account for several levels of the molecule/quantum dot with level-dependent coupling strengths, and allow for relaxation of the excited states. The current-voltage characteristics as well as the current noise are calculated within first-order perturbation expansion in the coupling strengths. For the case of asymmetric coupling to the leads we predict negative-differential-conductance accompanied with super-poissonian noise. Both effects are destroyed by fast relaxation processes. The non-monotonic behavior of the shot noise as a function of bias and relaxation rate reflects the details of the electronic structure and level-dependent coupling strengths.Comment: 8 pages, 7 figures, submitted to Phys. Rev. B, added reference

Inhibitory top-down projections from zona incerta mediate neocortical memory

Top-down projections convey a family of signals encoding previous experiences and current aims to the sensory neocortex, where they converge with external bottom-up information to enable perception and memory. Whereas top-down control has been attributed to excitatory pathways, the existence, connectivity, and information content of inhibitory top-down projections remain elusive. Here, we combine synaptic two-photon calcium imaging, circuit mapping, cortex-dependent learning, and chemogenetics in mice to identify GABAergic afferents from the subthalamic zona incerta as a major source of top-down input to the neocortex. Incertocortical transmission undergoes robust plasticity during learning that improves information transfer and mediates behavioral memory. Unlike excitatory pathways, incertocortical afferents form a disinhibitory circuit that encodes learned top-down relevance in a bidirectional manner where the rapid appearance of negative responses serves as the main driver of changes in stimulus representation. Our results therefore reveal the distinctive contribution of long-range (dis)inhibitory afferents to the computational flexibility of neocortical circuits

Coulomb effects in tunneling through a quantum dot stack

Tunneling through two vertically coupled quantum dots is studied by means of a Pauli master equation model. The observation of double peaks in the current-voltage characteristic in a recent experiment is analyzed in terms of the tunnel coupling between the quantum dots and the coupling to the contacts. Different regimes for the emitter chemical potential indicating different peak scenarios in the tunneling current are discussed in detail. We show by comparison with a density matrix approach that the interplay of coherent and incoherent effects in the stationary current can be fully described by this approach.Comment: 6 pages, 6 figure

Inhibitory synaptic plasticity: spike timing-dependence and putative network function

While the plasticity of excitatory synaptic connections in the brain has been widely studied, the plasticity of inhibitory connections is much less understood. Here, we present recent experimental and theoretical findings concerning the rules of spike timing-dependent inhibitory plasticity and their putative network function. This is a summary of a workshop at the COSYNE conference 2012

STDP Allows Fast Rate-Modulated Coding with Poisson-Like Spike Trains

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (∼10–20 ms) for sufficiently many inputs (∼100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks

Paradoxical Evidence Integration in Rapid Decision Processes

Decisions about noisy stimuli require evidence integration over time. Traditionally, evidence integration and decision making are described as a one-stage process: a decision is made when evidence for the presence of a stimulus crosses a threshold. Here, we show that one-stage models cannot explain psychophysical experiments on feature fusion, where two visual stimuli are presented in rapid succession. Paradoxically, the second stimulus biases decisions more strongly than the first one, contrary to predictions of one-stage models and intuition. We present a two-stage model where sensory information is integrated and buffered before it is fed into a drift diffusion process. The model is tested in a series of psychophysical experiments and explains both accuracy and reaction time distributions

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)