Method and system for distributed coding and learning in neuromorphic networks for pattern recognition
AssigneeHRL Laboratories, LLC
InventorsYongqiang Cao, Praveen K. Pilly
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Abstract
Described is a system for pattern recognition designed for neuromorphic hardware. The system generates a spike train of neuron spikes for training patterns with each excitatory neuron in an excitatory layer, where each training pattern belongs to a pattern class. A spiking rate distribution of excitatory neurons is generated for each pattern class. Each spiking rate distribution of excitatory neurons is normalized, and a class template is generated for each pattern class from the normalized spiking rate distributions. An unlabeled input pattern is classified using the class templates. A mechanical component of an autonomous device can be controlled based on classification of the unlabeled input pattern.
Claims21
A system for pattern recognition applied to neuromorphic hardware, the system comprising:
- one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform an operation of:
- training a spiking neural network that is applied to neuromorphic hardware, wherein training the spiking neural network comprises operations of:
- generating, with each excitatory neuron in an excitatory layer of the spiking neural network, a spike train of neuron spikes for a plurality of training patterns, each training pattern belonging to a pattern class;
- generating a spiking rate distribution of excitatory neurons for each pattern class;
- normalizing each spiking rate distribution of excitatory neurons with a L2 norm;
- and
- generating a class template for each pattern class from the normalized spiking rate distributions.
A system for pattern recognition applied to neuromorphic hardware, the system comprising:
- one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform operations of:
- using a trained spiking neural network applied to neuromorphic hardware to classify an unlabeled input pattern, wherein classifying the unlabeled input pattern comprises operations of:
- using L2 normalization, classifying an unlabeled input pattern using class templates generated for a plurality of pattern classes from normalized spike rate distributions of excitatory neurons in the spiking neural network;
- and
- controlling a mechanical component of an autonomous device based on classification of the unlabeled input pattern.
A computer implemented method for pattern recognition applied to neuromorphic hardware, the method comprising an act of:
- causing one or more processors to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
- training a spiking neural network that is applied to neuromorphic hardware, wherein training the spiking neural network comprises operations of:
- generating, with each excitatory neuron in an excitatory layer of the spiking neural network, a spike train of neuron spikes for a plurality of training patterns, each training pattern belonging to a pattern class;
- generating a spiking rate distribution of excitatory neurons for each pattern class;
- normalizing each spiking rate distribution of excitatory neurons with a L2 norm;
- and
- generating a class template for each pattern class from the normalized spiking rate distributions.
A computer implemented method for pattern recognition applied to neuromorphic hardware, the method comprising an act of:
- causing one or more processors to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
- using a trained spiking neural network applied to neuromorphic hardware to classify an unlabeled input pattern, wherein classifying the unlabeled input pattern comprises operations of:
- using L2 normalization, classifying an unlabeled input pattern using class templates generated for a plurality of pattern classes from normalized spike rate distributions of excitatory neurons in the spiking neural network;
- and
- controlling a mechanical component of an autonomous device based on classification of the unlabeled input pattern.
A computer program product for pattern recognition applied to neuromorphic hardware, the computer program product comprising:
- computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of:
- training a spiking neural network that is applied to neuromorphic hardware, wherein training the spiking neural network comprises operations of:
- generating, with each excitatory neuron in an excitatory layer of the spiking neural network, a spike train of neuron spikes for a plurality of training patterns, each training pattern belonging to a pattern class;
- generating a spiking rate distribution of excitatory neurons for each pattern class;
- normalizing each spiking rate distribution of excitatory neurons with a L2 norm;
- and
- generating a class template for each pattern class from the normalized spiking rate distributions.
A computer program product for pattern recognition applied to neuromorphic hardware, the computer program product comprising:
- computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of:
- using a trained spiking neural network applied to neuromorphic hardware to classify an unlabeled input pattern, wherein classifying the unlabeled input pattern comprises operations of:
- using L2 normalization, classifying an unlabeled input pattern using class templates generated for a plurality of pattern classes from normalized spike rate distributions of excitatory neurons in the spiking neural network;
- and
- controlling a mechanical component of an autonomous device based on classification of the unlabeled input pattern.
Dependencies are inferred from the claim text.