According to one embodiment, a method, computer system, and computer program product for increasing linearity of a weight update of a phase change memory (PCM) cell is provided. The present invention may include applying a RESET pulse to amorphize the phase change material of the PCM cell; responsive to applying the RESET pulse, applying an incubation pulse to the PCM cell; and applying a plurality of partial SET pulses to incrementally increase the conductance of the PCM cell.

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.

Neural networks can be implemented with DNA strand displacement (DSD) circuits. The neural networks are designed and trained in silico taking into account the behavior of DSD circuits. Oligonucleotides comprising DSD circuits are synthesized and combined to form a neural network. In an implementation, the neural network may be a binary neural network in which the output from each neuron is a binary value and the weight of each neuron either maintains the incoming binary value or flips the binary value. Inputs to the neural network are one more oligonucleotides such as synthetic oligonucleotides containing digital data or natural oligonucleotides such as mRNA. Outputs from the neural networks may be oligonucleotides that are read by directly sequencing or oligonucleotides that generate signals such as by release of fluorescent reporters.

The present invention relates to a neuromimetic network comprising a set of neurons and a set of synapses, at least one neuron comprising a first stack of superimposed layers, the first stack successively comprising: a first electrode, a first barrier layer made of an electrically insulating material, and a second electrode, the first electrode, the first barrier layer and the second electrode forming a first ferroelectric tunnel junction, at least one synapse comprising a second stack of superimposed layers, the second stack successively comprising: a third electrode, a second barrier layer made of an electrically insulating material, and a fourth electrode, the third electrode, the second barrier layer and the fourth electrode forming a second ferroelectric tunnel junction.

Artificial neuromorphic circuit includes synapse and post-neuron circuits. Synapse circuit includes phase change element, first switch having at least three terminals, and second switch. Phase change element includes first and second terminals. First switch includes first, second and control terminals. Second switch includes first, second and control terminals. First switch is configured to receive first pulse signal. Second switch is coupled to phase change element and first switch, and is configured to receive second pulse signal. Post-neuron circuit includes capacitor and input terminal. Input terminal of post-neuron circuit charges capacitor in response to first pulse signal. Post-neuron circuit generates firing signal based on voltage level of capacitor and threshold voltage. Post-neuron circuit generates control signal based on firing signal. Control signal controls turning on of second switch. Second pulse signal flows through second switch to control state of phase change element to determine weight of artificial neuromorphic circuit.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for generating model neurons. In one aspect, a method includes receiving a plurality of descriptions of branches of dendrites of one or more neurons and generating a collection of model neurites. Each of the descriptions characterizes, for an individual branch, i) a distance from a cell body at which the individual branch first bifurcates and ii) a distance from the cell body at which the individual branch actually terminates. Generating the collection of model neurites includes repeatedly selecting a description of a branch from the plurality and probabilistically generating a topology of a model neurite based on the selected description. The probabilistic generation of the model neurite includes deciding whether to bifurcate, terminate, or continue the model neurites at different positions based on the selected description.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining network architectures based on synaptic connectivity. One of the methods includes processing a network input using a neural network to generate a network output, comprising: processing the network input using an encoder subnetwork of the neural network to generate an embedding of the network input; processing the embedding of the network input using a first connectivity layer of the neural network to generate a first connectivity layer output; processing the first connectivity layer output using a brain emulation subnetwork of the neural network to generate a brain emulation subnetwork output; processing the brain emulation subnetwork output using a second connectivity layer of the neural network to generate a second connectivity layer output; and processing the second connectivity layer output using a decoder subnetwork of the neural network to generate the network output.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing connectivity neural network layers. One of the methods includes processing a network input using a neural network to generate a network output, comprising: generating a layer input to a connectivity layer of the neural network based on the network input, wherein the layer input to the connectivity layer comprises a plurality of input values arranged in a plurality of input channels; processing the layer input using the connectivity layer to generate a layer output comprising a plurality of output values arranged in a plurality of output channels; processing the plurality of output channels of the connectivity layer using a brain emulation subnetwork of the neural network to generate a brain emulation subnetwork output; and generating the network output based on the brain emulation subnetwork output.

A bioprocessing device performing an operation based on cultured biological neurons includes: an electrode layer comprising electrodes connected to the biological neurons; circuit layers, stacked with the electrode layer, comprising stacked circuits for the biological neurons; and inter-layer connectors configured to connect the electrode layer and the circuit layers.

In one aspect, there is provided a method performed by one or more data processing apparatus, the method including receiving a training dataset having multiple training examples, where each training example includes: (i) an image, and (ii) a segmentation defining a target region of the image that has been classified as including pixels in a target category. The method further includes determining a respective refined segmentation for each training example, including, for each training example, processing the target region of the image defined by the segmentation for the training example using a de-noising neural network to generate a network output that defines the refined segmentation for the training example. The method further includes training a segmentation machine learning model on the training examples of the training dataset, including, for each training example training the segmentation machine learning model to process the image included in the training example to generate a model output that matches the refined segmentation for the training example.