Abstract: Systems, devices, and methods are disclosed for decision making based on plasticity rules of a neural network. A method may include obtaining a multilayered model. The multilayered model may include an input layer including one or more input units. The multilayered model may include one or more hidden layers including one or more hidden units. Each input unit may have a first connection with at least one hidden unit. The multilayered model may include an output layer including one or more output units. The method may also include receiving an input at a first input unit. The method may include sending a first signal from the first input unit to at least one hidden unit via a first connection comprising a first strength. The method may also include making a decision based on the model receiving the input.

Abstract: Systems and methods for generating Artificial Neural Networks (ANNs) based on the principles of biological sleep are disclosed. Namely, the systems and methods can be configured to apply a sleep-like phase to ANNs which enables training to be generalized, performance to be improved, and catastrophic forgetting for sequential multi-task training to be prevented. Various implementations of these systems and methods can be configured to: (i) train an ANN using backpropagation algorithm, (ii) convert the architecture of the ANN to an equivalent Spiking Neural Network (SNN) and simulate a sleep phase in the SNN while using plasticity rules to modify synaptic weights, and (iii) convert the modified synaptic weights associated with the simulated sleep phase of the SNN back into the ANN. This transformation from ANN to SNN to ANN effectively emulates learning mechanisms actuated during biological sleep and, as such, overcomes limitations commonly associated with machine learning.

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: Systems and methods for neuronal networks for associative learning are described. For example, a method may include obtaining target content, obtaining conditioned feature extraction models, generating multiple extracted features by applying the conditioned feature extraction models to the target content, obtaining a conditioned integration model, generating a representation of the target content by applying the conditioned integration model to the multiple extracted features, and displaying the representation.

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: Systems, devices, and methods are disclosed for decision making based on plasticity rules of a neural network. A method may include obtaining a multilayered model. The multilayered model may include an input layer including one or more input units. The multilayered model may include one or more hidden layers including one or more hidden units. Each input unit may have a first connection with at least one hidden unit. The multilayered model may include an output layer including one or more output units. The method may also include receiving an input at a first input unit. The method may include sending a first signal from the first input unit to at least one hidden unit via a first connection comprising a first strength. The method may also include making a decision based on the model receiving the input.

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.