A system used to implement the method of exchanging information through a wireless brain-computer interface includes a specified brain and a quantum supercomputer. The quantum supercomputer is initially used to detect a plurality of compositional particles within the specified brain. A quantum entanglement is then induced in between each compositional particle and the quantum supercomputer. The quantum supercomputer is subsequently used to generate an eigenmatrix of the specified brain with the quantum supercomputer, wherein the eigenmatrix is a representation of each compositional particle. The method concludes by enabling two-way communication between the specified brain and the quantum supercomputer by modifying the eigenmatrix.

The disclosures relates to systems and methods for using a trained alignment neural network along with a trained latent representation model to achieve accurate alignment between complex neural signals arising from co-variation across neuron populations over time and their intended motor control that can be invariant for a much longer period without supervised recalibrations. In one implementation, the method may include receiving neural data for a period of time from one or more sensors. The method may further include transforming the neural data to generate aligned variables using a trained alignment network. The method may also include processing the aligned variables through a trained latent model to determine a latent spatiotemporal representation of one or more brain state variables for the period of time and decoding the latent spatiotemporal representation into a brain state for the period of time.

A computer-assisted method for determining a microfluidic circuit configured to reproduce a neuron circuit, and comprising including the following steps: —obtaining a description of the neuron circuit, the description of the neuron circuit comprising a plurality of neuron populations and at least one neuron connection; —determining at least one first parameter for each node of a plurality of nodes of the microfluidic circuit, each node being associated with and configured to receive one neuron population among the plurality of neuron populations of the neuron circuit; —determining at least one second parameter for at least one connection of the microfluidic circuit, each connection being associated with and configured to receive a neuron connection of the at least one neuron connection of the neuron circuit; —determining the positioning of each node of the plurality of nodes and of each connection of the at least one connection.

A circuit comprises a series of calculating blocks that can each implement a group of neurons; a transformation block that is linked to the calculating blocks by a communication means and that can be linked at the input of the circuit to an external data bus, the transformation block transforming the format of the input data and transmitting the data to said calculating blocks by means of K independent communication channels, an input data word being cut up into sub-words such that the sub-words are transmitted over multiple successive communication cycles, one sub-word being transmitted per communication cycle over a communication channel dedicated to the word such that the N channels can transmit K words in parallel.

A neural network method and device are included, A neural network circuit includes a synaptic memory cell including a resistive memory element, which is disposed along an output line and which can have a first resistance value and a second resistance value as a resistance value, the synaptic memory cell generates a column signal, based on the resistance value of the resistive memory element and an input signal received via an input line, a reference memory cell including a reference memory element, which is disposed along a reference line and which has a resistance value that is a ratio of the first and second resistance values, the reference memory cell generates a reference signal, based on the resistance value of the reference memory element and the input signal, and an output circuit generates an output signal for the output line based on the column signal and the reference signal.

Democratically self-governed System for Knowledge Creation through Living Machine for the Manufacture of Living Knowledge by Living Individuals practicing the Knowledge Creation Process in Cycles.

A method of operating a biological interface is disclosed. The method may include obtaining an input physiological or neural signal from a subject, acquiring an input set of values from the input signal, obtaining a predictive signal from the subject or the environment, acquiring a predictive set of values from the predictive signal, training a decoder function in response to data from the predictive set of values, performing at least one calculation on the input set of values using the decoder function to produce an output set of values, and operating a device with the output set of values. A biological interface system is also disclosed. The biological interface system may contain an input signal sensor, an input signal processor, a predictive signal processor, a memory device storing data, and a system processor coupled to the memory device and configured to execute a decoder function.

A system and method for interfacing a computing device with in vitro biological neurons is described. In one embodiment, a method of interfacing with a plurality of in vitro biological neurons, comprises: generating, by a processing device, a first tensor indicative of a state of a virtual environment; encoding the first tensor into an instruction; generating first signals according to the instruction using a first plurality of electrodes, one or more chemical emitters or one or more light sources; detecting second signals by a second plurality of electrodes, one or more chemical sensors or one or more image sensors, the second signals having been generated by one or more of the plurality of in vitro biological neurons, wherein the second signals represent an action associated with the virtual environment; decoding the second signals into a second tensor; and applying the action to the virtual environment based on the second tensor.

A method for tuning the conductance of a molecular network includes a network of covalently bound molecular units, which are molecular entities assembled so as to form a network that can typically be compared to a finite, imperfect 2D crystal. Each of the molecular entities includes: a branching junction; M branches (M≥3) branching from said branching junction, where each of the M branches comprises an aliphatic group; and M linkers, each terminating a respective one of the M branches. Each of the M linkers is covalently bound to a linker of another molecular entity of the network. The method involves tuning the electrical conductance of molecular entities of a subset of the molecular entities of the network, in one or several (e.g., parallel or successive) steps.

A system and method for interfacing a computing device with in vitro biological neurons is described. In one embodiment, a method of interfacing with a plurality of in vitro biological neurons, comprises: generating, by a processing device, a first tensor indicative of a state of a virtual environment; encoding the first tensor into an instruction; generating first signals according to the instruction using a first plurality of electrodes, one or more chemical emitters or one or more light sources; detecting second signals by a second plurality of electrodes, one or more chemical sensors or one or more image sensors, the second signals having been generated by one or more of the plurality of in vitro biological neurons, wherein the second signals represent an action associated with the virtual environment; decoding the second signals into a second tensor; and applying the action to the virtual environment based on the second tensor.