US 2021/0334657 A1Published application

Cognitive computing methods and systems based on bilogival neurol networks

AssigneeAlpvision S.A.

InventorsFrederic JORDAN, Martin KUTTER, Yves DELACRETAZ

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Abstract

A Biological Neural Network (BNN) core unit comprising a neural cell culture, an input stimulation unit, an output readout unit may be controlled through its various life cycles to provide data processing functionality. An automation system comprising an environmental and chemical controller unit adapted to operate with the BNN stimulation and readout data interfaces facilitates the monitoring and adaptation of the BNN core unit parameters. Pre-processing and post-processing of the BNN interface signals may further facilitate the training and reinforcement learning by the BNN. Multiple BNN core units may also be assembled together as a stack. The proposed system provides a BNN Operating System as a core component for a wetware server to receive, process and transmit data for different client applications without exposing the BNN core unit components to the client user while requiring significantly less energy than conventional silicon-based hardware and software information processing for high-level cognitive computing tasks.

Claims15

Dependent claims

An automated processing system for transforming a spatio-temporal input data signal into a spatio-temporal output data signal, the system comprising:

an in vitro biological neural network (BNN) culture of neural cells in a BNN core unit;
an input stimulation unit adapted to apply an input spatio-temporal stimulation signal into a first set of the neural cells;
an output readout unit adapted to capture an output spatio-temporal readout signal from a second set of the neural cells;
one or more nutrient tanks in connection with one or more nutrient dispensers to inject one or more nutrients into the BNN culture;
one or more additive tanks each in connection with one or more additive dispensers to inject one or more additives into the BNN culture;
one or more nutrient waste collectors, for filtering and expelling nutrient waste from the BNN culture;
one or more additive waste collectors, for filtering and expelling additive waste from the BNN culture;
one or more vascularization networks to connect said nutrient dispensers, additive dispensers, nutrient waste collectors and additive waste collectors to the BNN culture;
one or more sensors to measure at least one environmental parameter of the BNN culture;
and
an automation controller configured to adapt the stimulation signal to the input data signal, to adapt the output data signal to the readout signal and to control at least one of:
a BNN core unit environmental parameter;
a BNN culture nutrient supply;
a BNN culture additive supply;
a BNN culture nutrient waste collection;
a BNN culture additive waste collection;
to maintain the homeostasis of the BNN culture over time so that the spatio-temporal input data signal is continuously transformed into the spatio-temporal output data signal.

14 more elements

A method for transforming a spatio-temporal input data signal into a spatio-temporal output data signal with an automation controller and a Biological Neural Network (BNN) core unit, the BNN core unit comprising at least an in vitro culture of neural cells (the BNN culture) adapted, with an input stimulation unit, to feed a stimulation spatio-temporal signal into a first set of the neural cells and adapted, with an output readout unit, to readout a spatio-temporal signal from a second set of the neural cells, the method comprising:

pre-processing, with the automation controller, the spatio-temporal input data signal into the stimulation spatio-temporal signal;
post-processing, with the automation controller, the readout spatio-temporal signal into the spatio-temporal output data signal;
and
controlling, with the automation controller, at least one of:
a BNN core unit environmental parameter;
a BNN culture nutrient supply;
a BNN culture additive supply;
a BNN culture nutrient waste collection;
a BNN culture additive waste collections;
a pre-processing parameter;
a post-processing parameter;
to maintain the homeostasis of the BNN neural cell culture over time so that the BNN core unit continuously transforms the spatio-temporal input data signal into the spatio-temporal output data signal.

9 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/274,020Filed 8 Sep 2019
Priority: 8 Sep 2018
Published: 28 Oct 2021
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

G06N 3/08
Neural networks: learning methods
G06N 3/002
Computing arrangements based on biological models: biomolecular computers, i.e. using biomolecules, proteins, cells
G06N 3/0464
Architecture, e.g. interconnection topology: convolutional networks [CNN, ConvNet]
G06N 3/061
Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons: using biological neurons, e.g. biological…

Family

Application62/728,765Us provisional application · filed 8 Sep 2018
US 12,210,967 B2Related patent
US 2021/0334657 A1This patent

From the US filing history. Foreign family members aren’t listed.

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