Spatial and Temporal Encoding for a PSN Student: Cameron Johnson, Department of Electrical and Computer Engineering Faculty Advisor: Dr. G.K. Venayagamoorthy, Department of Electrical and Computer Engineering RESULTS The gray-code-based spatial encoding method produced distinctly different numbers and combinations of neurons spiking in response for seven different inputs OBJECTIVES Encode real-valued data into a Polychronous Spiking Network (PSN) Explore the advantages and disadvantages of spatial and temporal encoding methods Develop means of decoding real-valued data from a PSN after the PSN has performed calculations to generate it Use a PSN as a function approximator to prove concept Use a PSN on real-world problems Robot movement and vision Power system prediction and control DISCUSSION Spatial encoding has yet to yield any decoding information Shows promise for entering distinct values in and getting distinct responses Decoding demonstrated by Zhang and Feng via their encoding method Only manages function reproduction, not calculation Still promising The difficulty of encoding and decoding is due to a lack of understanding of how living brains actually use the information they process Rate coding methods other than Zhang and Feng’s are slow Spike coding methods typically lose a lot of portential information BACKGROUND Current Neural Networks are used for function approximation Neuroidentification is a form of function approximation Spiking Neurons model biological neuron behavior more faithfully than other modern neural models Information is carried and processed by the pattern of spikes which make up the neural network Translating real data into spikes requires a method of encoding Temporal encoding has been explored many times, including rate coding and spike time coding Spatial encoding relies on physical relation of input spikes to the neurons to which they’re connected RESULTS CONCLUDING REMARKS Encoding into a PSN enables a very brain-like model to operate on data Gain the same sort of intuitive function handling that a living brain can Navigation through the real world Expert handling of control problems Possibly more intuitive handling of instructions Spatial encoding may deal with long-term memory and learned reflexes Temporal encoding may deal with pattern recognition and short-term memory A combination of the two is hoped to exploit the capabilities of a PSN to their fullest APPROACH: SPATIAL ENCODING Gray-code-based spatial encoding has been demonstrated in an Izhikevich neural network Encoding mechanism has two neurons representing one bit Neither neuron receiving a spike means there is no input A “1” is represented by one neuron in the pair receiving a spike and the other nothing. A “0” is represented by the re- verse. APPROACH: TEMPORAL ENCODING Shown here is an encoding method that treats the real value as a poisson rate applied directly to the voltage equation X. Zhang, G. You, T. Chen, J. Feng. “Maximum Likelihood Decoding of Neuronal Inputs from an Interspike Interval Distribution.” Neural Computation 21, 2009, 3079-3105. Other traditional methods Rate coding Spike density Spike count Population activity Spike coding Time to first spike Spike phase FUTURE WORK Test temporal decoding methods to see how well they differentiate values Experiment with combined spatial and temporal encoding Develop a decoding mechanism for PSNs, whether spatial or temporal or a combination Use a PSN for power system identification Acknowledgements This work was supported by the National Science Foundation (NSF) under EFRI award #0836017 and by a GAANN Fellowship. Special Thanks to: Real-Time Power and Intelligent Systems Lab Intelligent Systems Center