Odor discrimination using binary spiking neural network

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.

1. 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.  
 
   
   
     2. The method of olfactory pattern classification of  claim 1  further comprising the step of 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. 
   
   
     3. The method of olfactory pattern classification of  claim 2  wherein said training step further comprises the steps of:
 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; and  
 resetting said counter to zero after said posting step.  
 
   
   
     4. The method of olfactory pattern classification of  claim 3  wherein said summing step further comprises summing active inputs to a counter for every 20 KHz clock cycle of said adder/comparator combination. 
   
   
     5. The method of olfactory pattern classification of  claim 1  wherein said sensing step further comprises sensing odorants using a plurality of CHEMFET odor receptors. 
   
   
     6. The method of olfactory pattern classification of  claim 1  wherein said sensing step further comprises sensing odorants using a plurality of IONFET odor receptors. 
   
   
     7. The method of olfactory pattern classification of  claim 1  wherein said training step further comprises the steps of:
 receiving an input signal from an olfactory receptor;  
 summing said input from said receiving step;  
 adding one to a clock cycle for every input signal from said receiving step;  
 comparing values from said summing step and said adding step and comparing to a preselected threshold value;  
 inputting an above threshold value from said summing step to a spike bus;  
 determining whether value from said inputting step matches data on a synapse listing;  
 adding values from said determining step that do not match data on said synapse listing to a noise counter;  
 adding values from said determining step that do match data on said synapse list to a spike counter; and  
 outputting a signal associated with said spike counter after inputs to said spike counter reach a preselected threshold value.  
 
   
   
     8. The method of olfactory pattern classification of  claim 7  wherein said inputting step further comprises the steps of:
 providing a spike bus including synchronization logic;  
 connecting input signal from said receiving step to said spike bus using a priority encoder;  
 posting address of said input signal on said spike bus using said priority encoder; and  
 connecting neuron modules in parallel to said spike bus by a potentiated synapse list.  
 
   
   
     9. The method of olfactory pattern classification of  claim 7  wherein said determining step further comprises the step of determining whether value from said inputting step matches data on a synapse listing containing odor receptor signatures. 
   
   
     10. 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.  
 
   
   
     11. 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.  
 
   
   
     12. The olfactory pattern classification device of  claim 11  wherein said training program further comprises:
 a spike bus providing synchronization logic;  
 a priority encoder for connecting an input signal from said spike bus and for posting address of said input signal on said spike bus; and  
 a potentiated synapse list for connecting neuron modules in parallel to said spike bus.  
 
   
   
     13. The olfactory pattern classification device of  claim 12  wherein said potentiated synapse list further comprises a potentiated synapse list comprising odor receptor signatures.