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Hippocampal area CA3 performs the critical auto-associative function underlying pattern completion in episodic memory. Without external inputs, the electrical activity of this neural circuit reflects the spontaneous spiking interplay among glutamatergic Pyramidal neurons and GABAergic interneurons. However, the network mechanisms underlying these resting-state firing patterns are poorly understood. Leveraging the Hippocampome.org knowledge base, we developed a data-driven, large-scale spiking neural network (SNN) model of mouse CA3 with 8 neuron types, 90,000 neurons, 51 neuron-type specific connections, and 250,000,000 synapses. We instantiated the SNN in the CARLsim4 multi-GPU simulation environment using the Izhikevich and Tsodyks-Markram formalisms for neuronal and synaptic dynamics, respectively. We analyzed the resultant population activity upon transient activation. The SNN settled into stable oscillations with a biologically plausible grand-average firing frequency, which was robust relative to a wide range of transient activation. The diverse firing patterns of individual neuron types were consistent with existing knowledge of cell type-specific activity in vivo. Altered network structures that lacked neuron- or connection-type specificity were neither stable nor robust, highlighting the importance of neuron type circuitry. Additionally, external inputs reflecting dentate mossy fibers shifted the observed rhythms to the gamma band. We freely released the CARLsim4-Hippocampome framework on GitHub to test hippocampal hypotheses. Our SNN may be useful to investigate the circuit mechanisms underlying the computational functions of CA3. Moreover, our approach can be scaled to the whole hippocampal formation, which may contribute to elucidating how the unique neuronal architecture of this system subserves its crucial cognitive roles. [ABSTRACT FROM AUTHOR]

In this paper, an attempt was made to synthesize LaNiO3, CeNiO3, and BaNiO3, and Ce3+ and Ba2+ co-substituted LaNiO3. These samples were further subjected to various material characterization techniques in order to evaluate their physio-chemical properties. Scanning electron microscope (SEM) images showed large chunks of aggregated nanoparticles with minute voids. The EDX-derived atomic composition deviated from the nominal composition suggesting the occurrence of multiple phases. In addition, transmission electron microscope (TEM) images revealed that the samples exhibit uneven spherical shape with a high degree of aggregation. The Fourier transformed-infrared (FT-IR) spectra of the synthesized samples show vibrations of the BO6 octahedral indicating the presence of Ni–O bonds. In addition, metal-carboxyl vibrations were identified from the peaks at 1400 and 860 cm−1. Optical diffuse reflectance spectra (DRS) showed certain peaks originating from the O2− (2p) → Ni2+ (3d) charge transfer. The X-ray powder diffraction (XRPD) analysis revealed the existence of multiple phases for the samples CeNiO3, BaNiO3, and La(Ce, Ba)NiO3. Moreover, La(Ce, Ba)NiO3 contained four phases showing that the co-substitution of Ba2+ and Ce3+ into LaNiO3 may require more sophisticated methodologies. The sample BaNiO3 showed maximum weight loss, due to the existence of carbonate phase. The dielectric properties decreased with increasing frequency, while the ac electrical conductivity enhanced with increasing frequencies obeying the Maxwell–Wagner two-layer model in accordance with Koop's phenomenological theory. [ABSTRACT FROM AUTHOR]

Perovskite-type oxides La1-xCaxFeO3 (x = 0.0–1.0) were synthesized using combustion technique. The samples have been investigated using scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, ultraviolet–visible diffuse reflectance spectroscopy (DRS), X-ray powder diffraction (XRPD) using Rietveld refinement, thermogravimetric analysis (TGA) and dielectric spectroscopy. The SEM images showed spherical-shaped agglomerates having a non-uniform distribution of size. The EDX results suggested that the experimental chemical composition was in good accordance with the nominal values. FT-IR revealed multiple peaks in the range of 580–510 cm−1 due to asymmetric stretching of the octahedron. The reflectance spectra showed characteristic d → d transitions arising from the Fe 3d and the highest optical band gap energy ( E g ) of 2.57 eV was exhibited by LaFeO3, while the lowest E g (1.41 eV) was shown by La1-xCaxFeO3 with x = 0.6. The results suggest that these samples are suitable for photocatalytic applications. From phase evaluation of the diffraction patterns, it was found that five distinct phases exist in the series. A phase transformation from orthorhombic ( Pnma ) to cubic ( P m 3 ¯ m ) was found when x was increased from 0.4 to 0.6. TGA showed that LaFeO3 was the most stable with a residual mass of ~ 97 wt% at 900 ∘ C compared to CaFeO3 (~ 70 wt% at 900 ∘ C ). The evaluation of frequency-dependent (20 Hz–20 MHz) dielectric properties agreed with the Maxwell–Wagner two-layer model. After Ca2+ substitution, tan δ at 20 Hz significantly reduced from ~ 103 for x = 0 to ~ 100 for x = 1.0. The high polarization observed was due to e− hopping between Fe2+ ↔ Fe3+. The electrical models of the samples show non-Debye type relaxation behavior, while the ac conductivity enhanced with increasing frequency which was in accordance with Koop's phenomenological theory. [ABSTRACT FROM AUTHOR]

This special issue brings together contributions from some of the workshop participants and additional researchers on how ideas learned from visual neuroscience can improve computer vision systems. Briden and Norouzi apply deep neural network techniques to analyze electroencephalograms (EEG) and predict human perceptual decision-making confidence (Briden & Norouzi [1]). What can computer vision learn from visual neuroscience?. [Extracted from the article]