Abstract: Provided herein is a system for occupiable space automation using neural networks that delivers scalable and more intelligent occupiable space automation that can continuously learn from user actions and experiences and adapt to specific needs of each individual occupiable space. The occupiable space automation control system is built based on brain inspired multi-layer neural network with plastic connectivity between neurons. The occupiable space automation control system is configured to (a) adaptively predict previously learned activity patterns and (b) alert about potentially harmful or undesired activity patterns of the plurality of periphery devices based on response events of the plurality of artificial neurons and coupling strengths of the plurality of synapses. The occupiable space automation control system is configured to automatically operate the at least one controller based on the predicted activity pattern and/or provide user alerts based on a detected harmful activity pattern.

Abstract: A method is disclosed for predicting the stimulation effect of cortical neurons in response to extracellular electrical stimulation. The method comprising the steps of defining an electrode configuration, defining a reconstructed neuronal cell type, wherein the reconstructed neuronal cell is characteristic of the physical and electrical properties of a neuronal cell, computing an electric field potential at the reconstructed neuronal cell, computing an effective transmembrane current of an arborization of the reconstructed neuronal cell, and determining a probability of activation of the reconstructed neuronal cell. A method for inducing an electrical stimulation effect on a cortical column is also disclosed. In an embodiment, the predicted network response of a cortical column is used to configure one or more electrodes to induce cortical stimulation.

Abstract: Provided herein is a system for occupiable space automation using neural networks that delivers scalable and more intelligent occupiable space automation that can continuously learn from user actions and experiences and adapt to specific needs of each individual occupiable space. The occupiable space automation control system is built based on brain inspired multi-layer neural network with plastic connectivity between neurons. The occupiable space automation control system is configured to (a) adaptively predict previously learned activity patterns and (b) alert about potentially harmful or undesired activity patterns of the plurality of periphery devices based on response events of the plurality of artificial neurons and coupling strengths of the plurality of synapses. The occupiable space automation control system is configured to automatically operate the at least one controller based on the predicted activity pattern and/or provide user alerts based on a detected harmful activity pattern.

Abstract: A method is disclosed for predicting the stimulation effect of cortical neurons in response to extracellular electrical stimulation. The method comprising the steps of defining an electrode configuration, defining a reconstructed neuronal cell type, wherein the reconstructed neuronal cell is characteristic of the physical and electrical properties of a neuronal cell, computing an electric field potential at the reconstructed neuronal cell, computing an effective transmembrane current of an arborization of the reconstructed neuronal cell, and determining a probability of activation of the reconstructed neuronal cell. A method for inducing an electrical stimulation effect on a cortical column is also disclosed. In an embodiment, the predicted network response of a cortical column is used to configure one or more electrodes to induce cortical stimulation.