F.: Comparison of supervised learning methods for spike time coding in spiking neural networks

In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods? In order to answer this question, we discuss various approaches to the learning task considered. We shortly describe the particular learning algorithms and report the results of experiments. Finally, we discuss the properties, assumptions and limitations of each method. We complete this review with a comprehensive list of pointers to the literature

Flexible Temperature Control Solution for Integrated Circuits Testing—Silicon Creations Thermal Elephant

Both scientific and industrial applications require temperature stabilization and enforcement for testing purposes. In this study, we present a solution capable of handling socket-based IC test systems enabling packages from QFN up to FCBGA, or even COB solutions. The temperature range covers the full-range industrial temperature range (−40 °C to +125 °C). The extended temperature range of −55 °C to +150 °C is conditionally possible. Solution supports dry-air installation, safety mechanisms and flexible thermal head assemblies. We present the key features and architecture of the solution named “Thermal Elephant” that found applications in the industrial (characterization of the IP hard macros) and scientific applications (radiation imaging ICs)

Generalization Properties of Spiking Neurons Trained with ReSuMe Method

Abstract. In this paper we demonstrate the generalization property of spiking neurons trained with ReSuMe method. We show in a set of experiments that the learning neuron can approximate the input-output transformations defined by another- reference neuron with a high precision and that the learning process converges very quickly. We discuss the relationship between the neuron I/O properties and the weight distribution of its input connections. Finally, we discuss the conditions under which the neuron can approximate some given I/O transformations.

Comparison Of Supervised Learning Methods For Spike Time Coding in . . .

In this review we focus our attention on the supervised learning methods for spike time coding in Spiking Neural Networks (SNN). This study is motivated by the recent experimental results on information coding in the biological neural systems which suggest that precise timing of individual spikes may be essential for e#cient computation in the brain. We pose

Synthesis and Characterization of New Biodegradable Injectable Thermosensitive Smart Hydrogels for 5-Fluorouracil Delivery

In this paper, injectable, thermosensitive smart hydrogel local drug delivery systems (LDDSs) releasing the model antitumour drug 5-fluorouracil (5-FU) were developed. The systems were based on biodegradable triblock copolymers synthesized via ring opening polymerization (ROP) of ε-caprolactone (CL) in the presence of poly(ethylene glycol) (PEG) and zirconium(IV) acetylacetonate (Zr(acac)4), as co-initiator and catalyst, respectively. The structure, molecular weight (Mn) and molecular weight distribution (Đ) of the synthesized materials was studied in detail using nuclear magnetic resonance (NMR) and gel permeation chromatography (GPC) techniques; the optimal synthesis conditions were determined. The structure corresponded well to the theoretical assumptions. The produced hydrogels demonstrated a sharp sol–gel transition at temperature close to physiological value, forming a stable gel with good mechanical properties at 37 °C. The kinetics and mechanism of in vitro 5-FU release were characterized by zero order, first order, Higuchi and Korsmeyer–Peppas mathematical models. The obtained results indicate good release control; the kinetics were generally defined as first order according to the predominant diffusion mechanism; and the total drug release time was approximately 12 h. The copolymers were considered to be biodegradable and non-toxic; the resulting hydrogels appear to be promising as short-term LDDSs, potentially useful in antitumor therapy

Dual-Stimuli-Sensitive Smart Hydrogels Containing Magnetic Nanoparticles as Antitumor Local Drug Delivery Systems—Synthesis and Characterization

The aim of this study was to develop an innovative, dual-stimuli-responsive smart hydrogel local drug delivery system (LDDS), potentially useful as an injectable simultaneous chemotherapy and magnetic hyperthermia (MHT) antitumor treatment device. The hydrogels were based on a biocompatible and biodegradable poly(ε-caprolactone-co-rac-lactide)-b-poly(ethylene glycol)-b-poly(ε-caprolactone-co-rac-lactide) (PCLA-PEG-PCLA, PCLA) triblock copolymer, synthesized via ring-opening polymerization (ROP) in the presence of a zirconium(IV) acetylacetonate (Zr(acac)4) catalyst. The PCLA copolymers were successfully synthesized and characterized using NMR and GPC techniques. Furthermore, the gel-forming and rheological properties of the resulting hydrogels were thoroughly investigated, and the optimal synthesis conditions were determined. The coprecipitation method was applied to create magnetic iron oxide nanoparticles (MIONs) with a low diameter and a narrow size distribution. The magnetic properties of the MIONs were close to superparamagnetic upon TEM, DLS, and VSM analysis. The particle suspension placed in an alternating magnetic field (AMF) of the appropriate parameters showed a rapid increase in temperature to the values desired for hyperthermia. The MIONs/hydrogel matrices were evaluated for paclitaxel (PTX) release in vitro. The release was prolonged and well controlled, displaying close to zero-order kinetics; the drug release mechanism was found to be anomalous. Furthermore, it was found that the simulated hyperthermia conditions had no effect on the release kinetics. As a result, the synthesized smart hydrogels were discovered to be a promising antitumor LDDS, allowing simultaneous chemotherapy and hyperthermia treatment