Hidden Markov modelling of simultaneously recorded cells in the associative cortex of behaving monkeys

References

• Abeles M. Corticonics. Cambridge University Press, Cambridge 1991
   Google Scholar
• Abeles M, Bergman H, Gat I, Meilijson I, Seidemann E, Tishby N, Vaadia E. Cortical activity flips among quasi-stationary states. Proc. Natl Acad. Sci. 1995; 8616–20
   PubMed Web of Science ®Google Scholar
• Aertsen A, Gerstein G L, Habib M K, Palm G. Dynamics of neuronal firing correlation: modulation of ‘effective connectivity’. J. Neurophysiol. 1989; 61: 900–17
   PubMed Web of Science ®Google Scholar
• Ahissar E, Vaadia E, Ahissar M, Bergman H, Arieli A, Abeles M. Dependence of cortical plasticity on correlated activity of single neurons and on behavioral context. Science 1992; 257: 1412–5
   PubMed Web of Science ®Google Scholar
• Baum L E. An inequality and associated maxmization technique in statistical estimation of probabilistic functions of Markov processes. Inequalities 1972; 3(1)1–8
   Google Scholar
• Bellman R E, Dreyfus S E. Applied Dynamic Programming. Princeton University Press, Princeton, NJ 1962
   Google Scholar
• Bellman R E. Dynamic Programming. Princeton University Press, Princeton, NJ 1957
   Google Scholar
• Baum L E, Petrie T. Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Stat. 1966; 37: 1554–63
   Google Scholar
• Bruce C J. Single neuron activity in the monkey's prefrontal cortex. Neurobiology of Neocortex, P Rakic, W Singer. Wiley, New York 1988; 297–329
   Google Scholar
• Braitenberg V, Schuz A. Anatomy of the Cortex. Springer, Berlin 1991
   Google Scholar
• Cover T M, Thomas J A. Elements of Information Theory. Wiley, New York 1991
   Google Scholar
• Duda R O, Hart P E. Pattern Classification and Scene Analysis. Wiley, New York 1973
   Google Scholar
• Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc. 1977; 39: 1–38
   Google Scholar
• Fuster J M. Behavioral electrophysiology of the prefrontal cortex. Trends Neurosci. 1984; 408–14
   Web of Science ®Google Scholar
• Gerstein G L, Bedenbaugh P, Aertsen A. Neuronal assemblies. IEEE Trans. Biomed. Eng. 1989; 36: 4–14
   PubMed Web of Science ®Google Scholar
• Ghez C. Voluntary movement. Principles of Neural Science, J H Schwartz, E P Kandel, T M Jessell. Wiley, New York 1988; 609–25
   Google Scholar
• Gat I, Tishby N. Statistical modeling of cell-assemblies activities in associative cortex of behaving monkeys. Neural Information Process. Syst. 1993; 5: 945–53
   Google Scholar
• Gat I, Tishby N. Comparative study of different supervised detection methods of simultaneously recorded spike trains. 1997, in preparation
   Google Scholar
• Hebb D O. The Organization of Behavior: a Neurophysiological Theory. Wiley, New York 1949
   Google Scholar
• Juang B H, Rabiner L R. The segmental K-means algorithm for estimating parameters of hidden Markov models. IEEE Trans. Signal Process. 1990; ASSP-38: 1639–41
   Web of Science ®Google Scholar
• Kruger J. Simultaneous individual recordings from many cerebral neurons: techniques and results. Rev. Phys. Biochem. Pharmacol. 1983; 98: 177–233
   PubMed Web of Science ®Google Scholar
• Kullback S. Information Theory and Statistics. Wiley, New York 1959
   Google Scholar
• Levinson S E, Rabiner L R, Sondhi M M. An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition. Bell Syst. Tech. J. 1983; 62: 1035–74
   Google Scholar
• Merhav N, Ephraim Y. Hidden Markov modeling using the most likely state sequence. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing. 1991; 1: 469–72
   Google Scholar
• Moore G P, Perkel D H, Segundo J P. Statistical analysis and functional interpretation of neuronal spike data. Ann. Rev. Physiol. 1966; 18: 493–522
   Google Scholar
• Palm G, Aertsen A, Gerstein G L. On the significance of correlations among neuronal spike trains. Biol. Cybern. 1988; 59: 1–11
   PubMed Web of Science ®Google Scholar
• Palm G. Neural Assemblies-Studies of Brain Functions. Springer, Berlin 1982
   Google Scholar
• Perkel D H, Gerstein G L, Moore G P. Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. Biophys. J. 1967; 7: 419–40
   PubMed Web of Science ®Google Scholar
• Poritz A B. Hidden Markov models: a guided tour. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing. 1988; 1: 7–13
   Google Scholar
• Prut Y. Firing patterns as a sensitive estimator of cortical information processing. PhD Thesis. Hebrew University of Jerusalem. 1995
   Google Scholar
• Pandya D N, Yeterian E H. Architecture and connections of cortical association areas. Cerebral Cortex, A Peters, E G Jones. Plenum, New York 1985; 609–24
   Google Scholar
• Rabiner L R. Tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 1989; 77: 257–86
   Web of Science ®Google Scholar
• Radons G, Becker J D, Dulfer B, Kruger J. Analysis, classification, and coding of multielectrode spike trains with hidden Markov models. Biol. Cybern. 1994; 359
   PubMed Web of Science ®Google Scholar
• Rabiner L R, Juang B H, Levinson S E, Sondhi M M. Recognition of isolated digits using hidden Markov models with continuous mixture densities. AT&T Technol. 1986; 64: 1211–22
   Web of Science ®Google Scholar
• Rabiner L R, Wilson J P, Juang B H. A segmental k-means training procedure for connected word recognition. AT&T Technol. 1986; 21–40
   Google Scholar
• Seidemann E, Abeles M, Meilijson I, Vaadia E. Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task. J. Neurophysiol. 1996; 752–68
   Google Scholar
• Shaw G L, Harth E, Scheibel A B. Cooperativity in brain function assemblies of approximately 30 neurons. Exp. Neurol. 1982; 77: 324–58
   PubMed Web of Science ®Google Scholar
• Vaadia E, Ahissar E, Bergman H, Lavner Y. Correlated activity of neurons: a neural code for higher brain functions. Neural Cooperativity, J Kruger. Springer, Berlin 1991; 249–79
   Google Scholar
• von der Malsburg C. Nervous structures with dynamical links. Ber. Bunsenges. Phys. Chem. 1985; 89: 703–10
   Web of Science ®Google Scholar