RESUMEN
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different model on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the Sensorium 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with the behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 competition track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at www.sensorium-competition.net.
RESUMEN
Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input (https://www.sensorium-competition.net/). This competition includes the collection of a new large-scale dataset from the primary visual cortex of five mice, containing responses from over 38,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
RESUMEN
Recently, doping metals into graphitic carbon nitride (g-C3N4) is considered for environmental applications and organic reactions. In this study, we used ferrocene as a source of Fe3+ to dope iron onto g-C3N4. The scaffold of the internal electric field is presented as an impressive strategy to increase photocatalytic activities. Fe3+ was doped onto graphitic carbon nitride (FeIII/g-C3N4) by the calcination method, which was well characterized by FT-IR, Raman, XRF, XRD, XPS, UV-visible DRS, photo-luminescence (PL), photocurrent, SEM, HR-TEM, EDX, BET, EIS, and cyclic voltammetry analyses. The synthesis of benzimidazole derivatives as pharmaceutically active compounds was introduced by using a suitable method under mild reaction conditions without using a base, oxidant, and other reagents or additives. The modification by using iron had a considerable effect on the optical and electronic characteristics in contrast to g-C3N4. The nanocomposite FeIII/g-C3N4 could be employed as a multifunctional photocatalyst to perform the tandem process, oxidation of toluene, and then cyclization with o-phenylenediamines to prepare benzimidazoles under visible light conditions. The existence of the dynamic equilibrium of Fe3+/Fe2+ helped in significantly improving the activity. By changing the reaction conditions and different control experiments as well as Mott-Schottky plot analysis, the superoxide ion (O2Ë-) can be obtained as the reactive species in this reaction. The photocatalytic activity of FeIII/g-C3N4 for this one-pot reaction is also investigated in detail.
RESUMEN
Computational human head models have been used in electrophysiological studies, and they have been able to provide useful information that is unable or difficult to acquire from experimental or imaging studies. However, most of these models are purely volume conductor models that overlooked the electric excitability of axons in the white matter of the brain. This study combined a finite element (FE) model of electroconvulsive therapy (ECT) with a whole-brain tractography analysis as well as the cable theory of neuronal excitation. We have reconstructed a whole-brain tractogram with 500 neural fibres from the diffusion-weighted magnetic resonance scans, and extracted the information on electrical potential from the FE ECT model of the same head. We then calculated the first and second spatial derivatives of the electrical potential, which describes the activating function for homogenous axons and investigated sensitive regions of white matter activation.
