Given the massive transformation of all areas of society by AI, it is becoming essential to integrate AI literacy into the various school curricula from an early age. However, teaching the basic concepts of AI and Machine Learning (e.g. training a model; artificial neural networks) at the K-12 level might seem too abstract, whereas teaching only how to use AI fails to really "open the black box". To overcome these difficulties, we have developed AlphAI, a software resource designed to make the understanding of AI algorithms accessible and attractive to the general public and children as young as 8 years old. This is achieved by making AI very concrete, first by manipulating the learning of educational robots that users train for different behaviors, such as circuit racing, using either supervised or reinforcement learning; second by visualizing in real time in a graphical interface the details of AI algorithms (neural networks, k-nearest neighbors, Q-learning, etc). In addition, the use of the software is not limited to beginners, since it allows to write one's own AI in Python to control the robots. In this paper, we present the basic principles of the software, its graphical interface, how to use it with various educational robots, and example activities with classes from Elementary school to University. AlphAI software and robotic kits are commercially available from Learning Robots.

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

Measuring erythrocyte velocity in individual microvessels has important applications for biomedical and functional imaging. Recent multiphoton fluorescence microscopy approaches require injecting fluorescent tracers; moreover, only one or few vessels can be imaged at a time. To overcome these shortcomings, we used CCD-based optical imaging of intrinsic absorption changes in macroscopic vascular networks to record erythrocytes' trajectories over several mm (2) of cortical surface. We then demonstrate the feasibility of erythrocyte velocity estimation from such wide-field data, using two robust, independent, algorithms. The first one is a recently published Radon transform-based algorithm that estimates erythrocyte velocity locally. We adapt it to data obtained in wide-field imaging and show, for the first time, its performance on such datasets. The second ("fasttrack") algorithm is novel. It is based on global energy minimization techniques to estimate the full spatiotemporal erythrocytes' trajectories inside vessels. We test the two algorithms on both simulated and biological data, obtained in rat cerebral cortex in a spreading depression experiment. On vessels with medium-slow erythrocyte velocities both algorithms performed well, allowing their usage as benchmark one for another. However, our novel fasttrack algorithm outperformed the other one for higher velocities, as encountered in the arterial network.

We address here the use of EEG and fMRI, and their combination, in order to estimate the full spatiotemporal patterns of activity on the cortical surface in the absence of any particular assumptions on this activity such as stimulation times. For handling such a high-dimension inverse problem, we propose the use of (1) a global forward model of how these measures are functions of the "neural activity" of a large number of sources distributed on the cortical surface, formalized as a dynamical system, and (2) adaptive filters, as a natural solution to solve this inverse problem iteratively along the temporal dimension. This estimation framework relies on realistic physiological models, uses EEG and fMRI in a symmetric manner, and takes into account both their temporal and spatial information. We use the Kalman filter and smoother to perform such an estimation on realistic artificial data and demonstrate that the algorithm can handle the high dimensionality of these data and that it succeeds in solving this inverse problem, combining efficiently the information provided by the two modalities (this information being naturally predominantly temporal for EEG and spatial for fMRI). It performs particularly well in reconstructing a random temporally and spatially smooth activity spread over the cortex. The Kalman filter and smoother show some limitations, however, which call for the development of more specific adaptive filters. First, they do not cope well with the strong nonlinearity in the model that is necessary for an adequate description of the relation between cortical electric activities and the metabolic demand responsible for fMRI signals. Second, they fail to estimate a sparse activity (i.e., presenting sharp peaks at specific locations and times). Finally their computational cost remains high. We use schematic examples to explain these limitations and propose further developments of our method to overcome them.