Contributions to models of single neuron computation in striatum and cortex

A deeper understanding is required of how a single neuron utilizes its nonlinear subcellular devices to generate complex neuronal dynamics. Two compartmental models of cortex and striatum are accurately formulated and firmly grounded in the experimental reality of electrophysiology to address the questions: how striatal projection neurons implement location-dependent dendritic integration to carry out association-based computation and how cortical pyramidal neurons strategically exploit the type and location of synaptic contacts to enrich its computational capacities.Neuronale Zellen transformieren kontinuierliche Signale in diskrete Zeitserien von Aktionspotentialen und kodieren damit Perzeptionen und interne Zustände. Kompartiment-Modelle werden formuliert von Nervenzellen im Kortex und Striatum, die elektrophysiologisch fundiert sind, um spezifische Fragen zu adressieren: i) Inwiefern implementieren Projektionen vom Striatum ortsabhängige dendritische Integration, um Assoziationens-basierte Berechnungen zu realisieren? ii) Inwiefern nutzen kortikale Zellen den Typ und den Ort, um die durch sie realisierten Berechnungen zu optimieren

Long memory in financial markets: A heterogeneous agent model perspective

During last decades, studies on asset pricing models witnessed a paradigm shift from rational expectation and representative agent to an alternative, behavioral view, where agents are heterogeneous and boundedly rational. In this paper, we model the financial market as an interaction of two types of boundedly rational investors — fundamentalists and chartists. We examine the dynamics of the market price and market behavior, which depend on investors' behavior and the interaction of the two types of investors. Numerical simulations of the corresponding stochastic model demonstrate that the model is able to replicate the stylized facts of financial time series, in particular the long-term dependence (long memory) of asset return volatilities. We further investigate the source of the long memory according to asset pricing mechanism of our model, and provide evidences of long memory by applying the modified R/S analysis. Our results demonstrate that the key parameter that has impact on the long memory is the speed of the price adjustment of the market maker at the equilibrium of demand and supply

Heterogeneous agent models in financial markets: A nonlinear dynamics approach

Studies on financial markets have accumulated consistent evidences of stylized facts and anomalies, which can be characterized by stochastic switching among different co-existing market states but yet difficult to reconcile with traditionally rational expectation theory. When agents are heterogeneous and boundedly rational, recent developments on the role of the adaptive behavior of interacting heterogeneous agents in financial markets have provided a nonlinear dynamics channel to such co-existence of different market states, shedding light into these stylized facts and anomalies. This survey focuses on the nonlinear dynamics approach to model the feedback of evolutionary dynamics of heterogeneous agents and to characterize the underlying mechanisms of the stylized facts and anomalies in financial markets, of which the authors and several coauthors have contributed in several papers

Experiments on bright field and dark field high energy electron imaging with thick target material

Using a high energy electron beam for the imaging of high density matter with both high spatial-temporal and areal density resolution under extreme states of temperature and pressure is one of the critical challenges in high energy density physics . When a charged particle beam passes through an opaque target, the beam will be scattered with a distribution that depends on the thickness of the material. By collecting the scattered beam either near or off axis, so-called bright field or dark field images can be obtained. Here we report on an electron radiography experiment using 45 MeV electrons from an S-band photo-injector, where scattered electrons, after interacting with a sample, are collected and imaged by a quadrupole imaging system. We achieved a few micrometers (about 4 micrometers) spatial resolution and about 10 micrometers thickness resolution for a silicon target of 300-600 micron thickness. With addition of dark field images that are captured by selecting electrons with large scattering angle, we show that more useful information in determining external details such as outlines, boundaries and defects can be obtained.Comment: 7pages, 7 figure

Shaping synaptic learning by the duration of postsynaptic action potential in a new STDP model.

Single spikes and their timing matter in changing synaptic efficacy, which is known as spike-timing-dependent plasticity (STDP). Most previous studies treated spikes as all-or-none events, and considered their duration and magnitude as negligible. Here we explore the effects of action potential (AP) duration on synaptic plasticity in a simplified model neuron using computer simulations. We propose a novel STDP model that depresses synapses using an AP duration dependent LTD window and induces potentiation of synaptic strength when presynaptic spikes arrive before and during a postsynaptic AP (dSTDP). We demonstrate that AP duration is another key factor for insensitizing the postsynaptic neural firing and for controlling the shape of synaptic weight distribution. Extended AP durations produce a wide unimodal weight distribution that resembles the ones reported experimentally and make the postsynaptic neuron tranquil when disturbed by poisson noise spike trains, while equivalently sensitive to the synchronized. Our results suggest that the impact of AP duration, modeled here as an AP-dependent STDP window, on synaptic plasticity can be dramatic and should motivate future STDP studies