Neuronal gate devices that function as a biological equivalent of AND, OR, and NOT digital logic gates are described. The neuronal gate devices comprise microstructures that support the patterned growth of two or more populations of neurons that are synaptically coupled. Also described are reconfigurable biological computers that comprise two or more coupled neuronal gates and may be trained to process a set of one or more input signals and generate a corresponding set of one or more output signals.

In one embodiment, the present invention provides a neural network circuit comprising multiple symmetric core circuits. Each symmetric core circuit comprises a first core module and a second core module. Each core module comprises a plurality of electronic neurons, a plurality of electronic axons, and an interconnection network comprising multiple electronic synapses interconnecting the axons to the neurons. Each synapse interconnects an axon to a neuron. The first core module and the second core module are logically overlayed on one another such that neurons in the first core module are proximal to axons in the second core module, and axons in the first core module are proximal to neurons in the second core module. Each neuron in each core module receives axonal firing events via interconnected axons and generates a neuronal firing event according to a neuronal activation function.

Living Machine for the Manufacture of Living Knowledge by Living Individuals through the practice of the Living Knowledge Creation Process in Living Knowledge Creation Process Cycles where Living Knowledge Economics operates.

Apparatus and methods for 3D-Scanless Holographic Optogenetics with Temporal focusing (3D-SHOT), which allows precise, simultaneous photo-activation of arbitrary sets of neurons anywhere within the addressable volume of the microscope. Soma-targeted (ST) optogenetic tools, ST-ChroME and IRES-ST-eGtACR1, optimized for multiphoton activation and suppression are also provided. The methods use point-cloud holography to place multiple copies of a temporally focused disc matching the dimensions of a designated neuron's cell body. Experiments in cultured cells, brain slices, and in living mice demonstrate single-neuron spatial resolution even when optically targeting randomly distributed groups of neurons in 3D.

A computing device includes at least one sensor configured to generate voltage signals indicative of a measured neurological impulse and one or more application specific integrated circuit (ASIC) chips connected to the at least one sensor. The one or more ASIC chips configured to determine at least one command for operating a device based on the measured neurological impulse and to transmit the at least one command to the device.

The present disclosure provides a system and method for interfacing a biological neural network and an artificial neural network. The system and method comprises an interface layer comprising electrically conductive nodes to relay a transmission of neural data from the biological neural network to the artificial neural network, the artificial neural network comprising a communication interface to receive the transmission of neural data from the interface layer, a processor to translate the transmission of neural data into an artificial memory data, and a memory to store the artificial memory data.

A system for biologically determined artificial intelligence selection guidance includes a computing device designed and configured to receive at least a biological extraction and an item descriptor from a user, generate, using a classification algorithm and a plurality of past extractions, a user classifier matching user data to user sets, identify, using the classifier and the element user data, a user set identifier matching the user, produce a selection guidance using the user set identifier and the item category identifier, and provide the selection guidance to the user.

A system includes a housing, sensors, actuators, and a processing device. The housing is formed of material that is soft, flexible, stretchable, deformable, or any combination of thereof. Each sensor is disposed on a surface of the housing, disposed at least partially inside the housing, or positioned spaced apart from the housing, and are configured to generate data associated with physical properties of the housing an environment external to the housing. The processing device is configured to implement a neural network having one or more inputs and one or more outputs. The inputs include the data associated with the physical properties of the housing and the data associated with the environment external to the housing. The outputs are based on the data associated the physical properties of the housing and the data associated with the environment external to the housing.

Robust recurrent artificial neural networks and techniques for improving the robustness of recurrent artificial neural networks. For example, a system can include a plurality of nodes and links arranged in a recurrent neural network, wherein either transmissions of information along the links or decisions at the nodes are non-deterministic, and an output configured to output indications of occurrences of topological patterns of activity in the recurrent artificial neural network.

Embodiments may provide a general-purpose, relatively inexpensive, AI-driven implant that is able to adapt to and modulate any given region in the brain. For example, in an embodiment, an implant device adapted to be implanted within a body of a person for interacting with brain tissue may comprise a plurality of fibers adapted to receive electrical and optical signals from electrophysiological neural signals of the brain tissue and to transmit electrical and optical signals to provide electrophysiological stimulation of the brain tissue, the fibers electrically and optically coupled to at least one readout integrated circuit.