In-silico model of NMDA and non-NMDA receptor activities using analog very-large-scale integrated circuits

Advances in Experimental Medicine and Biology

Volumn 551, Issue , 2004, Pages 171-175

  Rachmuth, Guy ^a,b   Poon, Chi Sang ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

METAL OXIDE; N METHYL DEXTRO ASPARTIC ACID;

ANALYTIC METHOD; CELL POPULATION; COMPLEX FORMATION; COMPUTER PROGRAM; CONFERENCE PAPER; INTEGRATED CIRCUIT; MODEL; MOLECULAR DYNAMICS; PRIORITY JOURNAL; RECEPTOR UPREGULATION; RESPIRATION CONTROL; RESPIRATORY NERVE CELL; SIMULATION; SYNAPTIC TRANSMISSION;

EID: 11144326949     PISSN: 00652598     EISSN: None     Source Type: Book Series    
DOI: 10.1007/0-387-27023-x_26     Document Type: Conference Paper

References (12)

1. Rybak, I.A., Shevtsova, N.A., St-John, W.M., Paton, J.F.R., Pierrefiche, O. Endogenous rhythm generation in the pre-Botzinger complex and ionic currents: modeling and in vitro studies, European Journal of Neuroscience 18 (2): 239-257 (2003).
2. Butera, R.J., Johnson, S.M., DelNegro, C.A., Rinzel, J., Smith, J.C. Dynamics of excitatory networks of bursting pacemaking neurons: Modeling and experimental studies of the respiratory central pattern generator. Neurocomputing: 32: 323-330 (2000).
3. Poon, C.-S. and M. Siniaia. Plasticity of cardiorespiratory neural processing: classification and computational functions. Respirat. Physiol. Special Issue on Modeling and Control of Breathing 122: 83-109 (2000).
4. Mead., C. Analog VLSI and Neural Systems. 1989. Addison-Wesley. Reading Massachusetts.
5. Mahowald, M. and R. Douglas, A Silicon neuron. Nature 354: pp. 515-518, (1991).
6. Hebb, D. The Organization of Behavior. 1949, New York Wiely.
7. Young, D., and C.S. Poon. A Hebbian feedback covariance learning paradigm for self-tuning optimal control. IEEE Tran Systems and Cybernetics 31: 173-186 (2001).
8. Rachmuth, G. and C.S. Poon. Design of a neuromorphic hebbian synapse using analog VLSI. First International IEEE EMBS Conference on Neural Engineering, Capri, Italy, 20-22 March 2003 Conference Proceedings., pp. 221-224 (2003).
9. Mussa-Ivalidi, F.A., and Miller, L.E. Brain-Machine interfaces: computational demands and clinical needs meet basic neuroscience. Trends in Neuroscience. 26: 329-334 (2003).
10. Jung, R., Brauer, E.J., Abbas, J.J. Real-time interaction between a neuromorphic electronic circuit and the spinal cord. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 9: 319-326 (2001).
11. Krebs, H. Hogan, N., Aisen, M.L., and Volpe, T. Robot-aided neurorehabilitation. IEEE Trans Rehabil Engineering, 6: 75-87 (1998).
12. Sutton, R.S., and Barto, A.G. Reinforcement Learning. (1998). Cambridge, Massachusetts, MIT Press.