Artificial neural network architectures based on synaptic connectivity graphs

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an artificial neural network architecture based on a synaptic connectivity graph. According to one aspect, there is provided a method comprising: obtaining a synaptic resolution image of at least a portion of a brain of a biological organism; processing the image to identify: (i) a plurality of neurons in the brain, and (ii) a plurality of synaptic connections between pairs of neurons in the brain; generating data defining a graph representing synaptic connectivity between the neurons in the brain; determining an artificial neural network architecture corresponding to the graph representing the synaptic connectivity between the neurons in the brain; and processing a network input using an artificial neural network having the artificial neural network architecture to generate a network output.

1 . (canceled) 
     
     
         2 . A method performed by one or more data processing apparatus, the method comprising:
 obtaining a three-dimensional (3D) synaptic resolution image of at least a portion of a brain of a biological organism;   processing the 3D synaptic resolution image to generate data defining a set of weight values that jointly parameterize an artificial neural network architecture;   generating an artificial neural network that has the artificial neural network architecture and that is parametrized by the set of weight values derived from the 3D synaptic resolution image of the brain; and   processing a network input using the artificial neural network, in accordance with the set of weight values derived from the 3D synaptic resolution image of the brain, to generate a network output.   
     
     
         3 . The method of  claim 2 , wherein processing the 3D synaptic resolution image to generate data defining the set of weight values that joint parametrize the artificial neural network architecture comprises:
 processing the 3D synaptic resolution image of the brain to identify a plurality of synaptic connections depicted in the 3D synaptic resolution image of the brain; and   processing the 3D synaptic resolution image of the brain to generate a respective biological weight value for each of the plurality of synaptic connections depicted in the 3D synaptic resolution image of the brain.   
     
     
         4 . The method of  claim 3 , wherein each of the plurality of synaptic connections is between a respective first neuron and a respective second neuron in the brain of the biological organism, and
 wherein processing the 3D synaptic resolution image of the brain to generate a respective biological weight value for each of the plurality of synaptic connections depicted in the 3D synaptic resolution image of the brain comprises, for each of the plurality of synaptic connections:
 determining: (i) a first tolerance region in the 3D synaptic resolution image around a first neuron corresponding to the synaptic connection, and (ii) a second tolerance region in the 3D synaptic resolution image around a second neuron corresponding to the synaptic connection; and 
 determining the biological weight value for the synaptic connection based on an area of overlap between the first tolerance region and the second tolerance region. 
   
     
     
         5 . The method of  claim 2 , further comprising processing the 3D synaptic resolution image of the brain to generate the artificial neural network architecture. 
     
     
         6 . The method of  claim 5 , wherein processing the 3D synaptic resolution image of the brain to generate the artificial neural network architecture comprises:
 processing the 3D synaptic resolution image of the brain to identify a plurality of biological neurons depicted in the 3D synaptic resolution image; and   instantiating a respective artificial neuron in the artificial neural network architecture corresponding to each of the plurality of biological neurons.   
     
     
         7 . The method of  claim 6 , further comprising:
 processing the 3D synaptic resolution image of the brain to identify a plurality of synaptic connections depicted in the 3D synaptic resolution image; and   determining a wiring between artificial neurons of the artificial neural network architecture based on the synaptic connections depicted in the 3D synaptic resolution image.   
     
     
         8 . The method of  claim 7 , wherein determining the wiring between artificial neurons of the artificial neural network based on the synaptic connections depicted in the 3D synaptic resolution image comprises, for each of the plurality of synaptic connections:
 mapping the synaptic connection to a corresponding connection between a corresponding pair of artificial neurons in the artificial neural network architecture.   
     
     
         9 . The method of  claim 2 , wherein the artificial neural network architecture is further parametrized by a set of weight values that are not derived from the 3D synaptic resolution image of the brain. 
     
     
         10 . The method of  claim 2 , further comprising training the artificial neural network using a machine learning training technique on a set of training data. 
     
     
         11 . The method of  claim 2 , wherein the network input comprises image data. 
     
     
         12 . The method of  claim 2 , wherein the network output comprises classification data that specifies a respective score for each of multiple classes. 
     
     
         13 . The method of  claim 2 , wherein the biological organism is an animal. 
     
     
         14 . The method of  claim 13 , wherein the biological organism is a fly. 
     
     
         15 . The method of  claim 2 , wherein the 3D synaptic resolution image of the brain of the biological organism is an electron microscope image. 
     
     
         16 . A system comprising:
 one or more computers; and   one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
 obtaining a three-dimensional (3D) synaptic resolution image of at least a portion of a brain of a biological organism; 
 processing the 3D synaptic resolution image to generate data defining a set of weight values that jointly parameterize an artificial neural network architecture; 
 generating an artificial neural network that has the artificial neural network architecture and that is parametrized by the set of weight values derived from the 3D synaptic resolution image of the brain; and 
 processing a network input using the artificial neural network, in accordance with the set of weight values derived from the 3D synaptic resolution image of the brain, to generate a network output. 
   
     
     
         17 . One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:
 obtaining a three-dimensional (3D) synaptic resolution image of at least a portion of a brain of a biological organism;   processing the 3D synaptic resolution image to generate data defining a set of weight values that jointly parameterize an artificial neural network architecture;   generating an artificial neural network that has the artificial neural network architecture and that is parametrized by the set of weight values derived from the 3D synaptic resolution image of the brain; and   processing a network input using the artificial neural network, in accordance with the set of weight values derived from the 3D synaptic resolution image of the brain, to generate a network output.   
     
     
         18 . The non-transitory computer storage media of  claim 17 , wherein processing the 3D synaptic resolution image to generate data defining the set of weight values that joint parametrize the artificial neural network architecture comprises:
 processing the 3D synaptic resolution image of the brain to identify a plurality of synaptic connections depicted in the 3D synaptic resolution image of the brain; and   processing the 3D synaptic resolution image of the brain to generate a respective biological weight value for each of the plurality of synaptic connections depicted in the 3D synaptic resolution image of the brain.   
     
     
         19 . The non-transitory computer storage media of  claim 18 , wherein each of the plurality of synaptic connections is between a respective first neuron and a respective second neuron in the brain of the biological organism, and
 wherein processing the 3D synaptic resolution image of the brain to generate a respective biological weight value for each of the plurality of synaptic connections depicted in the 3D synaptic resolution image of the brain comprises, for each of the plurality of synaptic connections:
 determining: (i) a first tolerance region in the 3D synaptic resolution image around a first neuron corresponding to the synaptic connection, and (ii) a second tolerance region in the 3D synaptic resolution image around a second neuron corresponding to the synaptic connection; and 
 determining the biological weight value for the synaptic connection based on an area of overlap between the first tolerance region and the second tolerance region. 
   
     
     
         20 . The non-transitory computer storage media of  claim 17 , wherein the operations further comprise processing the 3D synaptic resolution image of the brain to generate the artificial neural network architecture. 
     
     
         21 . The non-transitory computer storage media of  claim 20 , wherein processing the 3D synaptic resolution image of the brain to generate the artificial neural network architecture comprises:
 processing the 3D synaptic resolution image of the brain to identify a plurality of biological neurons depicted in the 3D synaptic resolution image; and   instantiating a respective artificial neuron in the artificial neural network architecture corresponding to each of the plurality of biological neurons.