Odor discrimination using binary spiking neural network
AssigneeThe United States of America as represented by the Secretary of the Air Force
InventorsJacob Allen, Robert L. Ewing, Hoda S. Abdel-Aty-Zohdy
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Abstract
An odor discrimination method and device for an electronic nose system including olfactory pattern classification based on a binary spiking neural network with the capability to handle many sensor inputs in a noise environment while recognizing a large number of potential odors. The spiking neural networks process a large number of inputs arriving from a chemical sensor array and implemented with efficient use of chip surface area.
Claims13
A method of olfactory pattern classification comprising the steps of:
- sensing odorants using a plurality of odor receptors;
- converting output of said sensing step to binary data;
- inputting binary data from said converting step to a spiking neural network;
- training said spiking neural network to learn most prevalent combination of odor receptors;
- and
- associating said combination of odor receptors from said training step with an output neuron.
A method of olfactory pattern classification comprising the steps of:
- sensing odorants using a plurality of odor receptors;
- first converting output of said sensing step to binary data;
- second converting said binary data into spike trains comprising an adder/comparator combination having an input of zero representing a lack of odorant stimulus and an input of one representing an odorant stimulus;
- summing active inputs to a counter for every clock cycle of said adder/comparator combination;
- adding one to every clock cycle of said adder/comparator for every zero input;
- posting a spike to a spike bus every time said counter reaches a specified threshold;
- resetting said counter to zero after said posting step;
- training said spiking neural network to learn which combination of odor receptors is most prevelant;
- and
- associating a set of most prevelant ordor receptors with an output neuron.
An olfactory pattern classification device comprising:
- a plurality of odor receptors for sensing odorants;
- means for converting output of said odor receptors to binary data;
- a spiking neural network for receiving said binary data comprising:
- a plurality of potentiated synapses, wherein the weight of an off synapse is zero and the weight of an on synapse is one;
- a counter for adding positive weights from said potentiated synapses;
- a threshold comparator for determining when said counter has reached a preselected threshold value;
- a training program for training said spiking neural network to learn which combination of odor receptors is most prevelant;
- and
- a specified output neuron specified by which set of odor receptors are most prevelant.
Dependencies are inferred from the claim text.