Prediction-error signals in anterior cingulate cortex drive task-switching

Task-switching is a fundamental cognitive ability that allows animals to update their knowledge of current rules or contexts. Detecting discrepancies between predicted and observed events is essential for this process. However, little is known about how the brain computes cognitive prediction-errors and whether neural prediction-error signals are causally related to task-switching behaviours. Here we trained mice to use a prediction-error to switch, in a single trial, between responding to the same stimuli using two distinct rules. Optogenetic silencing and un-silencing, together with widefield and two-photon calcium imaging revealed that the anterior cingulate cortex (ACC) was specifically required for this rapid task-switching, but only when it exhibited neural prediction-error signals. These prediction-error signals were projection-target dependent and were larger preceding successful behavioural transitions. An all-optical approach revealed a disinhibitory interneuron circuit required for successful prediction-error computation. These results reveal a circuit mechanism for computing prediction-errors and transitioning between distinct cognitive states

Continuous-time spike-based reinforcement learning for working memory tasks

As the brain purportedly employs on-policy reinforcement learning compatible with SARSA learning, and most interesting cognitive tasks require some form of memory while taking place in continuous-time, recent work has developed plausible reinforcement learning schemes that are compatible with these requirements. Lacking is a formulation of both computation and learning in terms of spiking neurons. Such a formulation creates both a closer mapping to biology, and also expresses such learning in terms of asynchronous and sparse neural computation. We present a spiking neural network with memory that learns cognitive tasks in continuous time. Learning is biologically plausibly implemented using the AuGMeNT framework, and we show how separate spiking forward and feedback networks suffice for learning the tasks just as fast the analog CT-AuGMeNT counterpart, while computing efficiently using very few spikes: 1–20 Hz on average

Reservoir computing for temporal data classification using a dynamic solid electrolyte ZnO thin film transistor

The processing of sequential and temporal data is essential to computer vision and speech recognition, two of the most common applications of artificial intelligence (AI). Reservoir computing (RC) is a branch of AI that offers a highly efficient framework for processing temporal inputs at a low training cost compared to conventional Recurrent Neural Networks (RNNs). However, despite extensive effort, two-terminal memristor-based reservoirs have, until now, been implemented to process sequential data by reading their conductance states only once, at the end of the entire sequence. This method reduces the dimensionality, related to the number of signals from the reservoir and thereby lowers the overall performance of reservoir systems. Higher dimensionality facilitates the separation of originally inseparable inputs by reading out from a larger set of spatiotemporal features of inputs. Moreover, memristor-based reservoirs either use multiple pulse rates, fast or slow read (immediately or with a delay introduced after the end of the sequence), or excitatory pulses to enhance the dimensionality of reservoir states. This adds to the complexity of the reservoir system and reduces power efficiency. In this paper, we demonstrate the first reservoir computing system based on a dynamic three terminal solid electrolyte ZnO/Ta2O5 Thin-film Transistor fabricated at less than 100°C. The inherent nonlinearity and dynamic memory of the device lead to a rich separation property of reservoir states that results in, to our knowledge, the highest accuracy of 94.44%, using electronic charge-based system, for the classification of hand-written digits. This improvement is attributed to an increase in the dimensionality of the reservoir by reading the reservoir states after each pulse rather than at the end of the sequence. The third terminal enables a read operation in the off state, that is when no pulse is applied at the gate terminal, via a small read pulse at the drain. This fundamentally allows multiple read operations without increasing energy consumption, which is not possible in the conventional two-terminal memristor counterpart. Further, we have also shown that devices do not saturate even after multiple write pulses which demonstrates the device’s ability to process longer sequences

Evidence for a companion to BM Gem, a silicate carbon star

Balmer and Paschen continuum emission as well as Balmer series lines of P Cygni-type profile from H_gamma through H_23 are revealed in the violet spectra of BM Gem, a carbon star associated with an oxygen-rich circumstellar shell (`silicate carbon star') observed with the high dispersion spectrograph (HDS) on the Subaru telescope. The blue-shifted absorption in the Balmer lines indicates the presence of an outflow, the line of sight velocity of which is at least 400 km s^-1, which is the highest outflow velocity observed to date in a carbon star. We argue that the observed unusual features in BM Gem are strong evidence for the presence of a companion, which should form an accretion disk that gives rise to both an ionized gas region and a high velocity, variable outflow. The estimated luminosity of ~0.2 (0.03-0.6) L_sun for the ionized gas can be maintained by a mass accretion rate to a dwarf companion of ~10^-8 M_sun yr^-1, while ~10^-10 M_sun yr^-1 is sufficient for accretion to a white dwarf companion. These accretion rates are feasible for some detached binary configurations on the basis of the Bond-Hoyle type accretion process. We concluded that the carbon star BM Gem is in a detached binary system with a companion of low mass and low luminosity. However, we are unable to determine whether this companion object is a dwarf or a white dwarf. The upper limits for binary separation are 210 AU and 930 AU for a dwarf and a white dwarf, respectively. We also note that the observed features of BM Gem mimic those of Mira (omi Cet), which may suggest actual similarities in their binary configurations and circumstellar structures.Comment: 11 pages, 2 figures, 1 table, accepted for publication in Ap

Reservoir computing for temporal data classification using a dynamic solid electrolyte ZnO thin film transistor

The processing of sequential and temporal data is essential to computer vision and speech recognition, two of the most common applications of artificial intelligence (AI). Reservoir computing (RC) is a branch of AI that offers a highly efficient framework for processing temporal inputs at a low training cost compared to conventional Recurrent Neural Networks (RNNs). However, despite extensive effort, two-terminal memristor-based reservoirs have, until now, been implemented to process sequential data by reading their conductance states only once, at the end of the entire sequence. This method reduces the dimensionality, related to the number of signals from the reservoir and thereby lowers the overall performance of reservoir systems. Higher dimensionality facilitates the separation of originally inseparable inputs by reading out from a larger set of spatiotemporal features of inputs. Moreover, memristor-based reservoirs either use multiple pulse rates, fast or slow read (immediately or with a delay introduced after the end of the sequence), or excitatory pulses to enhance the dimensionality of reservoir states. This adds to the complexity of the reservoir system and reduces power efficiency. In this paper, we demonstrate the first reservoir computing system based on a dynamic three terminal solid electrolyte ZnO/Ta2O5 Thin-film Transistor fabricated at less than 100°C. The inherent nonlinearity and dynamic memory of the device lead to a rich separation property of reservoir states that results in, to our knowledge, the highest accuracy of 94.44%, using electronic charge-based system, for the classification of hand-written digits. This improvement is attributed to an increase in the dimensionality of the reservoir by reading the reservoir states after each pulse rather than at the end of the sequence. The third terminal enables a read operation in the off state, that is when no pulse is applied at the gate terminal, via a small read pulse at the drain. This fundamentally allows multiple read operations without increasing energy consumption, which is not possible in the conventional two-terminal memristor counterpart. Further, we have also shown that devices do not saturate even after multiple write pulses which demonstrates the device’s ability to process longer sequences

Melting as a String-Mediated Phase Transition

We present a theory of the melting of elemental solids as a dislocation-mediated phase transition. We model dislocations near melt as non-interacting closed strings on a lattice. In this framework we derive simple expressions for the melting temperature and latent heat of fusion that depend on the dislocation density at melt. We use experimental data for more than half the elements in the Periodic Table to determine the dislocation density from both relations. Melting temperatures yield a dislocation density of (0.61\pm 0.20) b^{-2}, in good agreement with the density obtained from latent heats, (0.66\pm 0.11) b^{-2}, where b is the length of the smallest perfect-dislocation Burgers vector. Melting corresponds to the situation where, on average, half of the atoms are within a dislocation core.Comment: 18 pages, LaTeX, 3 eps figures, to appear in Phys. Rev.

Unusual Dust Emission from Planetary Nebulae in the Magellanic Clouds

We present a Spitzer Space Telescope spectroscopic study of a sample of 25 planetary nebulae in the Magellanic Clouds. The low-resolution modules are used to analyze the dust features present in the infrared spectra. This study complements a previous work by the same authors where the same sample was analyzed in terms of neon and sulfur abundances. Over half of the objects (14) show emission of polycyclic aromatic hydrocarbons, typical of carbon-rich dust environments. We compare the hydrocarbon emission in our objects to those of Galactic HII regions and planetary nebulae, and LMC/SMC HII regions. Amorphous silicates are seen in just two objects, enforcing the now well-known-fact that oxygen-rich dust is less common at low metallicities. Besides these common features, some planetary nebulae show very unusual dust. Nine objects show a strong silicon carbide feature at 11um and twelve of them show magnesium sulfide emission starting at 25um. The high percentage of spectra with silicon carbide in the Magellanic Clouds is not common. Two objects show a broad band which may be attributed to hydrogenated amorphous carbon and weak low-excitation atomic lines. It is likely that these nebulae are very young. The spectra of the remaining eight nebulae are dominated by the emission of fine-structure lines with a weak continuum due to thermal emission of dust, although in a few cases the S/N in the spectra is low, and weak dust features may not have been detected.Comment: 13 pages, 2 tables, 7 figures, Accepted for publication in Ap

Optical properties of dust

http://arxiv.org/abs/0808.4123Except in a few cases cosmic dust can be studied in situ or in terrestrial laboratories, essentially all of our information concerning the nature of cosmic dust depends upon its interaction with electromagnetic radiation. This chapter presents the theoretical basis for describing the optical properties of dust -- how it absorbs and scatters starlight and reradiates the absorbed energy at longer wavelengths.Partial support by a Chandra Theory program and HST Theory Programs is gratefully acknowledged

Interstellar Grains -- The 75th Anniversary

The year of 2005 marks the 75th anniversary since Trumpler (1930) provided the first definitive proof of interstellar grains by demonstrating the existence of general absorption and reddening of starlight in the galactic plane. This article reviews our progressive understanding of the nature of interstellar dust.Comment: invited review article for the "Light, Dust and Chemical Evolution" conference (Gerace, Italy, 26--30 September 2004), edited by F. Borghese and R. Saija, 2005, in pres

NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics