Poster Template

1
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 Fengs
  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 theyre
  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