Context-dependent human extinction memory is mediated by a ventromedial prefrontal and hippocampal network.

In fear extinction, an animal learns that a conditioned stimulus (CS) no longer predicts a noxious stimulus [unconditioned stimulus (UCS)] to which it had previously been associated, leading to inhibition of the conditioned response (CR). Extinction creates a new CS-noUCS memory trace, competing with the initial fear (CS-UCS) memory. Recall of extinction memory and, hence, CR inhibition at later CS encounters is facilitated by contextual stimuli present during extinction training. In line with theoretical predictions derived from animal studies, we show that, after extinction, a CS-evoked engagement of human ventromedial prefrontal cortex (VMPFC) and hippocampus is context dependent, being expressed in an extinction, but not a conditioning, context. Likewise, a positive correlation between VMPFC and hippocampal activity is extinction context dependent. Thus, a VMPFC-hippocampal network provides for context-dependent recall of human extinction memory, consistent with a view that hippocampus confers context dependence on VMPFC.

Observations on some German contributions to engineering design in memory of professor Wolfgang Beitz

This paper, written in memory of Professor Wolfgang Beitz, discusses some of the influences of the work undertaken in Germany on systematic engineering design. It highlights differences between the language regions, and gives examples of design research and design education linked to Konstruktionslehre - the standard text on systematic engineering design for which Professor Beitz was most widely recognised outside Germany. The paper finishes with a plea for a greater exchange of ideas.

Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks

Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. The introduction of inhibitory plasticity in suitable recurrent networks provides a homeostatic mechanism that leads to asynchronous irregular network states. Further, it can accommodate synaptic memories with activity patterns that become indiscernible from the background state but can be reactivated by external stimuli. Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry.

A printable optical time-temperature integrator based on shape memory in a chiral nematic polymer network

An optical and irreversible temperature sensor (e.g., a time-temperature integrator) is reported based on a mechanically embossed chiral-nematic polymer network. The polymer consists of a chemical and a physical (hydrogen-bonded) network and has a reflection band in the visible wavelength range. The sensors are produced by mechanical embossing at elevated temperatures. A relative large compressive deformation (up to 10%) is obtained inducing a shift to shorter wavelength of the reflection band (>30 nm). After embossing, a temperature sensor is obtained that exhibits an irreversible optical response. A permanent color shift to longer wavelengths (red) is observed upon heating of the polymer material to temperatures above the glass transition temperature. It is illustrated that the observed permanent color shift is related to shape memory in the polymer material. The films can be printed on a foil, thus showing that these sensors are potentially interesting as time-temperature integrators for applications in food and pharmaceutical products. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Factorial comparison of working memory models.

Three questions have been prominent in the study of visual working memory limitations: (a) What is the nature of mnemonic precision (e.g., quantized or continuous)? (b) How many items are remembered? (c) To what extent do spatial binding errors account for working memory failures? Modeling studies have typically focused on comparing possible answers to a single one of these questions, even though the result of such a comparison might depend on the assumed answers to both others. Here, we consider every possible combination of previously proposed answers to the individual questions. Each model is then a point in a 3-factor model space containing a total of 32 models, of which only 6 have been tested previously. We compare all models on data from 10 delayed-estimation experiments from 6 laboratories (for a total of 164 subjects and 131,452 trials). Consistently across experiments, we find that (a) mnemonic precision is not quantized but continuous and not equal but variable across items and trials; (b) the number of remembered items is likely to be variable across trials, with a mean of 6.4 in the best model (median across subjects); (c) spatial binding errors occur but explain only a small fraction of responses (16.5% at set size 8 in the best model). We find strong evidence against all 6 documented models. Our results demonstrate the value of factorial model comparison in working memory.

High performance non-volatile ferroelectric copolymer memory based on a ZnO nanowire transistor fabricated on a transparent substrate

A high performance ferroelectric non-volatile memory device based on a top-gate ZnO nanowire (NW) transistor fabricated on a glass substrate is demonstrated. The ZnO NW channel was spin-coated with a poly (vinylidenefluoride-co-trifluoroethylene) (P(VDF-TrFE)) layer acting as a top-gate dielectric without buffer layer. Electrical conductance modulation and memory hysteresis are achieved by a gate electric field induced reversible electrical polarization switching of the P(VDF-TrFE) thin film. Furthermore, the fabricated device exhibits a memory window of ∼16.5 V, a high drain current on/off ratio of ∼105, a gate leakage current below ∼300 pA, and excellent retention characteristics for over 104 s. © 2014 AIP Publishing LLC.

"Plateau"-related summary statistics are uninformative for comparing working memory models.

Performance on visual working memory tasks decreases as more items need to be remembered. Over the past decade, a debate has unfolded between proponents of slot models and slotless models of this phenomenon (Ma, Husain, Bays (Nature Neuroscience 17, 347-356, 2014). Zhang and Luck (Nature 453, (7192), 233-235, 2008) and Anderson, Vogel, and Awh (Attention, Perception, Psychophys 74, (5), 891-910, 2011) noticed that as more items need to be remembered, "memory noise" seems to first increase and then reach a "stable plateau." They argued that three summary statistics characterizing this plateau are consistent with slot models, but not with slotless models. Here, we assess the validity of their methods. We generated synthetic data both from a leading slot model and from a recent slotless model and quantified model evidence using log Bayes factors. We found that the summary statistics provided at most 0.15 % of the expected model evidence in the raw data. In a model recovery analysis, a total of more than a million trials were required to achieve 99 % correct recovery when models were compared on the basis of summary statistics, whereas fewer than 1,000 trials were sufficient when raw data were used. Therefore, at realistic numbers of trials, plateau-related summary statistics are highly unreliable for model comparison. Applying the same analyses to subject data from Anderson et al. (Attention, Perception, Psychophys 74, (5), 891-910, 2011), we found that the evidence in the summary statistics was at most 0.12 % of the evidence in the raw data and far too weak to warrant any conclusions. The evidence in the raw data, in fact, strongly favored the slotless model. These findings call into question claims about working memory that are based on summary statistics.

Correlations strike back (again): The case of associative memory retrieval

It has long been recognised that statistical dependencies in neuronal activity need to be taken into account when decoding stimuli encoded in a neural population. Less studied, though equally pernicious, is the need to take account of dependencies between synaptic weights when decoding patterns previously encoded in an auto-associative memory. We show that activity-dependent learning generically produces such correlations, and failing to take them into account in the dynamics of memory retrieval leads to catastrophically poor recall. We derive optimal network dynamics for recall in the face of synaptic correlations caused by a range of synaptic plasticity rules. These dynamics involve well-studied circuit motifs, such as forms of feedback inhibition and experimentally observed dendritic nonlinearities. We therefore show how addressing the problem of synaptic correlations leads to a novel functional account of key biophysical features of the neural substrate.

Single-electron multiple-valued memory using ultra-small floating gate metal-oxide-semiconductor field effect transistor

This paper proposes a novel single-electron multiple-valued memory. It is a metal-oxide-semiconductor field effect transistor (MOS)-type memory with multiple separate control gates and floating gate layer, which consists of nano-crystal grains. The electron can tunnel among the grains (floating gates) and between the floating gate layer and the MOS channel. The memory can realize operations of 'write', 'store' and 'erase' of multiple-valued signals exceeding three values by controlling the single electron tunneling behavior. We use Monte Carlo method to simulate the operation of single-electron four-valued memory. The simulation results show that it can operate well at room temperature.

A nanowire magnetic memory cell based on a periodic magnetic superlattice

We analyse the operation of a semiconductor nanowire-based memory cell. Large changes in the nanowire conductance result when the magnetization of a periodic array of nanoscale magnetic gates, which comprise the other key component of the memory cell, is switched between distinct configurations by an external magnetic field. The resulting conductance change provides the basis for a robust memory effect, which can be implemented in a semiconductor structure compatible with conventional semiconductor integrated circuits.

Memory effect in a system of zincblende Mn-rich Mn(Ga)As nanoclusters embedded in GaAs

Zincblende Mn-rich Mn(Ga)As nanoclusters embedded in GaAs matrices are fabricated by in situ postgrowth annealing diluted magnetic semiconductor (Ga,Mn)As films with Mn concentration ranging from 2.6% to 8% at 650 degrees C. Magnetization measurements show that memory effect and slow magnetic relaxation, the typical characteristics of the spin-glass-like phase, occur below the blocking temperature of 45 K in samples with high Mn concentration, while for samples with low Mn concentration, ferromagnetic order remains up to 360 K. The behavior of low-temperature spin dynamics can be explained by the hierarchical model. (c) 2007 American Institute of Physics.

A time-varying controllable fault-tolerant field associative memory model and its simulation

A time-varying controllable fault-tolerant field associative memory model and the realization algorithms are proposed. On the one hand, this model simulates the time-dependent changeability character of the fault-tolerant field of human brain's associative memory. On the other hand, fault-tolerant fields of the memory samples of the model can be controlled, and we can design proper fault-tolerant fields for memory samples at different time according to the essentiality of memory samples. Moreover, the model has realized the nonlinear association of infinite value pattern from n dimension space to m dimension space. And the fault-tolerant fields of the memory samples are full of the whole real space R-n. The simulation shows that the model has the above characters and the speed of associative memory about the model is faster.

The design algorithm of an associative memory neural network

A design algorithm of an associative memory neural network is proposed. The benefit of this design algorithm is to make the designed associative memory model can implement the hoped situation. On the one hand, the designed model has realized the nonlinear association of infinite value pattern from n dimension space to m dimension space. The result has improved the ones of some old associative memory neural network. On the other hand, the memory samples are in the centers of the fault-tolerant. In average significance the radius of the memory sample fault-tolerant field is maximum.

Study on Mg memory effect in npn type AlGaN/GaN HBT structures grown by MOCVD

AlGaN/GaN npn heterojunction bipolar transistor structures were grown by low-pressure MOCVD. Secondary ion mass spectroscopy (SIMS) measurements were carried out to study the Mg memory effect and redistribution in the emitter-base junction. The results indicated that there is a Mg-rich film formed in the ongrowing layer after the Cp2Mg source is switched off. The Mg-rich film can be confined in the base section by switching off the Cp2Mg source for appropriate time before the end of base growth. Low temperature growth of the undoped GaN spacer suppresses the Mg redistribution from Mg rich film. The delay rate of the Mg profile in sample C with spacer growing in low temperature is about 56 nm/decade, which becomes sharper than 80 nm/decade of the samples A and B without low temperature spacer. (C) 2005 Elsevier Ltd. All rights reserved.