Miniaturized fluorescence microscopes (miniscopes) have been instrumental to monitor neural signals during unrestrained behavior and their open-source versions have made them affordable. Often, the footprint and weight of open-source miniscopes is sacrificed for added functionality. Here, we present NINscope: a light-weight miniscope with a small footprint that integrates a high-sensitivity image sensor, an inertial measurement unit and an LED driver for an external optogenetic probe. We use it to perform the first concurrent cellular resolution recordings from cerebellum and cerebral cortex in unrestrained mice, demonstrate its optogenetic stimulation capabilities to examine cerebello-cerebral or cortico-striatal connectivity, and replicate findings of action encoding in dorsal striatum. In combination with cross-platform acquisition and control software, our miniscope is a versatile addition to the expanding tool chest of open-source miniscopes that will increase access to multi-region circuit investigations during unrestrained behavior.@en

Mathematical models with varying degrees of complexity have been proposed and simulated in an attempt to represent the intricate mechanisms of the human neuron. One of the most biochemically realistic and analytical models, based on the Hodgkin–Huxley (HH) model, has been selected for study in this paper. In order to satisfy the model's computational demands, we present a simulator implemented on Intel Xeon Phi Knights Landing manycore processors. This high-performance platform features an x86-based architecture, allowing our implementation to be portable to other common manycore processing machines. This is reinforced by the fact that Phi adopts the popular OpenMP and MPI programming models. The simulator performance is evaluated when calculating neuronal networks of varying sizes, density and network connectivity maps. The evaluation leads to an analysis of the neuronal synaptic patterns and their impact on performance when tackling this type of workload on a multinode system. It will be shown that the simulator can calculate 100 ms of simulated brain activity for up to 2 millions of biophysically-accurate neurons and 2 billion neuronal synapses within one minute of execution time. This level of performance renders the application an efficient solution for large-scale detailed model simulation.@en