My lab is interested in understanding the design and computing principles of biological sensory systems and translating this knowledge into bio-inspired intelligent systems and machine learning algorithms. Research in my lab involves two main themes:

• Systems Neuroscience: an investigation that will combine computational and electrophysiological approaches to examine fundamental principles of olfactory coding and signal processing, learning, and memory


• Neuromorphic Engineering: development of novel, bio-inspired devices (e.g. ‘electronic nose’) and algorithmic tools for non-invasive medical diagnosis and homeland security applications
  
  

How does the nervous system convert sensory stimuli into neural representations? Odorants are detected by a large population of olfactory receptor neurons (ORNs), which convert chemical stimuli into an electrical signal that is relayed downstream for further processing in the olfactory bulb (OB, vertebrates) or the antennal lobe (AL, insects). In the AL/OB, interactions between ensembles of excitatory principal neurons and inhibitory local neurons reshape the ORN input into complex, slow spatio-temporal patterns that are superimposed on a faster oscillatory field potential activity. The patterned AL/OB responses contain information about odor identity and intensity and form the only odor representation available to the organism. Hence they are considered to be the ‘odor code’. The generated odor code is then transmitted to learning and memory centers.


Related Publications:

• Temporally diverse firing patterns in olfactory receptor neurons underlie spatio-temporal neural codes for odors
  B. Raman, J. Joseph, J. Tang, and M. Stopfer
  The Journal of Neuroscience , Vol. 30, no. 6, pp. 1994-2006, 2010 (Highlighted article)
  
  
• Frequency transitions in odor-evoked neural oscillations
  I. Ito, M. Bazhenov, C. R. Ong, B. Raman, and M. Stopfer
  Neuron , Vol. 64, pp. 692-706, December 2009
  
  
• Sparse odor representation and olfactory learning
  I. Ito, C. R. Ong, B. Raman, and M. Stopfer
  Nature Neuroscience, Vol. 11, no.10, pp. 1177-1184, October 2008 (Highlighted article; F1000 evaluation: ‘Must read’)
  
  

An electronic nose is an instrument that combines an array of cross-selective chemical sensors and a pattern recognition engine to recognize chemical species. We employ statistical and bio-inspired signal approaches to design & operate MEMS-based chemiresistive microsensor arrays and tune them for specific chemical sensing application

Target applications of electronic nose include:

• Medical Diagnostics (Breath Analysis)
• Homeland Security

Related Publications:

• Microsensors in Dynamic Backgrounds: Towards real-time breath monitoring with temperature programmed microsensors
  K. Benkstein, B. Raman and S. Semancik
  IEEE Sensors, Special Issue on Sensors for Breath Analysis, Vol. 10, pp. 137-144, January 2010 (Cover article)
  
  
• Designing and Optimizing Microsensor Arrays for Recognizing Chemical Hazards in Complex Environments
  B. Raman, D. C. Meier, J. K. Evju and S. Semancik
  Sensors and Actuators B, Vol. 137(2), pp. 617-629, April 2009
  
  
• A bio-inspired methodology for artificial olfaction
  B. Raman, J. Hertz, K. Benkstein, and S. Semancik
  Analytical chemistry, Vol. 80, no. 22, pp. 8364-8471, November 2008 (Highlighted article)
  
  
• Processing of chemical sensor array with a biologically-inspired model of olfactory coding
  B. Raman, P. Sun, A. Gutierrez-Galvez, and R. Gutierrez-Osuna
  IEEE Transactions on Neural Networks, Vol. 17, no. 4, pp. 1015-1024, July 2006