The architecture of neuron connectivity in brain networks is one of the basic mechanisms by which to organize and sustain a particular function of the brain circuitry. There are areas of the brain composed of well-organized layers of neurons connected by unidirectional synaptic connections (e.g., cortex, hippocampus). Re-engineering of the neural circuits with such a heterogeneous network structure in culture may uncover basic mechanisms of emergent information functions of these circuits. In this study, we present such a model designed with two subpopulations of primary hippocampal neurons (E18) with directed connectivity grown in a microfluidic device with asymmetric channels. We analysed and compared neurite growth in the microchannels with various shapes that promoted growth dominantly in one direction. We found an optimal geometric shape features of the microchannels in which the axons coupled two chambers with the neurons. The axons grew in the promoted direction and formed predefined connections during the first 6 days in vitro (DIV). The microfluidic devices were coupled with microelectrode arrays (MEAs) to confirm unidirectional spiking pattern propagation through the microchannels between two compartments. We found that, during culture development, the defined morphological and functional connectivity formed and was maintained for up to 25 DIV.
The structured organization of connectivity in neural networks is associated with highly efficient information propagation and processing in the brain, in contrast with disordered homogeneous network architectures. Using microfluidic methods, we engineered modular networks of cultures using dissociated cells with unidirectional synaptic connections formed by asymmetric microchannels. The complexity of the microchannel geometry defined the strength of the synaptic connectivity and the properties of spiking activity propagation. In this study, we developed an experimental platform to study the effects of synaptic plasticity on a network level with predefined locations of unidirectionally connected cellular assemblies using multisite extracellular electrophysiology.
Predicting the live weight of cattle helps us monitor the health of animals, conduct genetic selection, and determine the optimal timing of slaughter. On large farms, accurate and expensive industrial scales are used to measure live weight. However, a promising alternative is to estimate live weight using morphometric measurements of livestock and then apply regression equations relating such measurements to live weight. Manual measurements on animals using a tape measure are time-consuming and stressful for the animals. Therefore, computer vision technologies are now increasingly used for non-contact morphometric measurements. The paper proposes a new model for predicting live weight based on augmenting three-dimensional clouds in the form of flat projections and image regression with deep learning. It is shown that on real datasets, the accuracy of weight measurement using the proposed model reaches 91.6%. We also discuss the potential applicability of the proposed approach to animal husbandry.
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