Spike synchronization and rate modulation differentially involved in motor cortical function

Science

Volumn 278, Issue 5345, 1997, Pages 1950-1953

  Riehle, Alexa ^a   Grün, Sonja ^b   Diesmann, Markus ^c   Aertsen, Ad ^c  

^a CNRS   (France)

^b HEBREW UNIVERSITY   (Israel)

^c UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CELL SYNCHRONIZATION; COGNITION; ELECTROPHYSIOLOGY; HUMAN; MOTOR CORTEX; NERVE CELL; NERVE CELL MEMBRANE POTENTIAL; NEUROPHYSIOLOGY; NONHUMAN; PRIORITY JOURNAL; VISUAL STIMULATION;

ACTION POTENTIALS; ANIMALS; CELL COMMUNICATION; COGNITION; MACACA; MENTAL PROCESSES; MOTOR ACTIVITY; MOTOR CORTEX; NERVE NET; NEURONS; PSYCHOMOTOR PERFORMANCE; TIME FACTORS;

EID: 2642691669     PISSN: 00368075     EISSN: None     Source Type: Journal    
DOI: 10.1126/science.278.5345.1950     Document Type: Article

References (51)

1. H. B. Barlow, Perception 1, 371 (1972); in Information Processing in the Cortex: Experiments and Theory, A. Aertsen and V. B. Braitenberg, Eds. (Springer, Berlin, 1992), pp. 169-174.
2. H. B. Barlow, Perception 1, 371 (1972); in Information Processing in the Cortex: Experiments and Theory, A. Aertsen and V. B. Braitenberg, Eds. (Springer, Berlin, 1992), pp. 169-174.
3. D. H. Hubel and T. N. Wiesel, Proc. R. Soc. London 198, 1 (1977); W. T. Newsome, K. H. Britten, J. A. Movshon, Nature 341, 52 (1989); M. N. Shadlen and W. T. Newsome, Curr. Opin. Neurobiol. 4, 569 (1994).
4. D. H. Hubel and T. N. Wiesel, Proc. R. Soc. London 198, 1 (1977); W. T. Newsome, K. H. Britten, J. A. Movshon, Nature 341, 52 (1989); M. N. Shadlen and W. T. Newsome, Curr. Opin. Neurobiol. 4, 569 (1994).
5. D. H. Hubel and T. N. Wiesel, Proc. R. Soc. London 198, 1 (1977); W. T. Newsome, K. H. Britten, J. A. Movshon, Nature 341, 52 (1989); M. N. Shadlen and W. T. Newsome, Curr. Opin. Neurobiol. 4, 569 (1994).
6. A. P. Georgopoulos, J. F. Kalaska, R. Caminiti, J. T. Massey, J. Neurosci. 2, 1527 (1982); A. P. Georgopoulos, M. Taira, A. Lukashin, Science 260, 47 (1993).
7. A. P. Georgopoulos, J. F. Kalaska, R. Caminiti, J. T. Massey, J. Neurosci. 2, 1527 (1982); A. P. Georgopoulos, M. Taira, A. Lukashin, Science 260, 47 (1993).
8. C. von der Malsburg, Internal Report 81-2 (Max-Planck-Institute, Göttingen, Germany, 1981); M. Abeles, Local Cortical Circuits (Springer, Berlin, 1982); Corticonics (Cambridge Univ. Press, Cambridge, 1991); G. L. Gerstein, P. Bedenbaugh, A. M. H. J. Aertsen, IEEE Trans. Biomed. Eng. 36, 4 (1989); G. Palm, Concepts Neurosci. 1, 133 (1990); W. Singer, Annu. Rev. Physiol. 55, 349 (1993).
9. C. von der Malsburg, Internal Report 81-2 (Max-Planck-Institute, Göttingen, Germany, 1981); M. Abeles, Local Cortical Circuits (Springer, Berlin, 1982); Corticonics (Cambridge Univ. Press, Cambridge, 1991); G. L. Gerstein, P. Bedenbaugh, A. M. H. J. Aertsen, IEEE Trans. Biomed. Eng. 36, 4 (1989); G. Palm, Concepts Neurosci. 1, 133 (1990); W. Singer, Annu. Rev. Physiol. 55, 349 (1993).
10. C. von der Malsburg, Internal Report 81-2 (Max-Planck-Institute, Göttingen, Germany, 1981); M. Abeles, Local Cortical Circuits (Springer, Berlin, 1982); Corticonics (Cambridge Univ. Press, Cambridge, 1991); G. L. Gerstein, P. Bedenbaugh, A. M. H. J. Aertsen, IEEE Trans. Biomed. Eng. 36, 4 (1989); G. Palm, Concepts Neurosci. 1, 133 (1990); W. Singer, Annu. Rev. Physiol. 55, 349 (1993).
11. C. von der Malsburg, Internal Report 81-2 (Max-Planck-Institute, Göttingen, Germany, 1981); M. Abeles, Local Cortical Circuits (Springer, Berlin, 1982); Corticonics (Cambridge Univ. Press, Cambridge, 1991); G. L. Gerstein, P. Bedenbaugh, A. M. H. J. Aertsen, IEEE Trans. Biomed. Eng. 36, 4 (1989); G. Palm, Concepts Neurosci. 1, 133 (1990); W. Singer, Annu. Rev. Physiol. 55, 349 (1993).
12. C. von der Malsburg, Internal Report 81-2 (Max-Planck-Institute, Göttingen, Germany, 1981); M. Abeles, Local Cortical Circuits (Springer, Berlin, 1982); Corticonics (Cambridge Univ. Press, Cambridge, 1991); G. L. Gerstein, P. Bedenbaugh, A. M. H. J. Aertsen, IEEE Trans. Biomed. Eng. 36, 4 (1989); G. Palm, Concepts Neurosci. 1, 133 (1990); W. Singer, Annu. Rev. Physiol. 55, 349 (1993).
13. C. von der Malsburg, Internal Report 81-2 (Max-Planck-Institute, Göttingen, Germany, 1981); M. Abeles, Local Cortical Circuits (Springer, Berlin, 1982); Corticonics (Cambridge Univ. Press, Cambridge, 1991); G. L. Gerstein, P. Bedenbaugh, A. M. H. J. Aertsen, IEEE Trans. Biomed. Eng. 36, 4 (1989); G. Palm, Concepts Neurosci. 1, 133 (1990); W. Singer, Annu. Rev. Physiol. 55, 349 (1993).
14. D. O. Hebb, Organization of Behavior (Wiley, New York, 1949).
15. M. Abeles, Isr. J. Med. Sci. 18, 83 (1982); W. R. Softky and C. Koch, J. Neurosci. 13, 334 (1993).
16. M. Abeles, Isr. J. Med. Sci. 18, 83 (1982); W. R. Softky and C. Koch, J. Neurosci. 13, 334 (1993).
17. R. Eckhorn et al., Biol. Cybern. 60, 121 (1988); C. M. Gray, P. König, A. K. Engel, W. Singer, Nature 338, 334 (1989); reviewed in W. Singer and C. M. Gray, Annu. Rev. Neurosci. 18, 555 (1995); P. R. Roelfsema, A. K. Engel, P. König, W. Singer, J. Cogn. Neurosci. 8, 603 (1996).
18. R. Eckhorn et al., Biol. Cybern. 60, 121 (1988); C. M. Gray, P. König, A. K. Engel, W. Singer, Nature 338, 334 (1989); reviewed in W. Singer and C. M. Gray, Annu. Rev. Neurosci. 18, 555 (1995); P. R. Roelfsema, A. K. Engel, P. König, W. Singer, J. Cogn. Neurosci. 8, 603 (1996).
19. R. Eckhorn et al., Biol. Cybern. 60, 121 (1988); C. M. Gray, P. König, A. K. Engel, W. Singer, Nature 338, 334 (1989); reviewed in W. Singer and C. M. Gray, Annu. Rev. Neurosci. 18, 555 (1995); P. R. Roelfsema, A. K. Engel, P. König, W. Singer, J. Cogn. Neurosci. 8, 603 (1996).
20. R. Eckhorn et al., Biol. Cybern. 60, 121 (1988); C. M. Gray, P. König, A. K. Engel, W. Singer, Nature 338, 334 (1989); reviewed in W. Singer and C. M. Gray, Annu. Rev. Neurosci. 18, 555 (1995); P. R. Roelfsema, A. K. Engel, P. König, W. Singer, J. Cogn. Neurosci. 8, 603 (1996).
21. M. Ahissar, E. Ahissar, H. Bergman, E. Vaadia, J. Neurophysiol. 67, 203 (1992); J. J. Eggermont, ibid. 71, 246 (1994); R. C. DeCharms and M. M. Merzenich, Nature 381, 610 (1995); Y. Sakurai, Neurosci. Res. 26, 1 (1996).
22. M. Ahissar, E. Ahissar, H. Bergman, E. Vaadia, J. Neurophysiol. 67, 203 (1992); J. J. Eggermont, ibid. 71, 246 (1994); R. C. DeCharms and M. M. Merzenich, Nature 381, 610 (1995); Y. Sakurai, Neurosci. Res. 26, 1 (1996).
23. M. Ahissar, E. Ahissar, H. Bergman, E. Vaadia, J. Neurophysiol. 67, 203 (1992); J. J. Eggermont, ibid. 71, 246 (1994); R. C. DeCharms and M. M. Merzenich, Nature 381, 610 (1995); Y. Sakurai, Neurosci. Res. 26, 1 (1996).
24. M. Ahissar, E. Ahissar, H. Bergman, E. Vaadia, J. Neurophysiol. 67, 203 (1992); J. J. Eggermont, ibid. 71, 246 (1994); R. C. DeCharms and M. M. Merzenich, Nature 381, 610 (1995); Y. Sakurai, Neurosci. Res. 26, 1 (1996).
25. M. A. L. Nicolelis, L. A. Baccala, R. C. S. Lin, J. K. Chapin, Science 268, 1353 (1995).
26. A. Aertsen et al., J. Hirnforsch. 32, 735 (1991); M. Abeles, H. Bergman, E. Margalit, E. Vaadia, J. Neurophysiol. 70, 1629 (1993); E. Vaadia et al., Nature 373, 515 (1995).
27. A. Aertsen et al., J. Hirnforsch. 32, 735 (1991); M. Abeles, H. Bergman, E. Margalit, E. Vaadia, J. Neurophysiol. 70, 1629 (1993); E. Vaadia et al., Nature 373, 515 (1995).
28. A. Aertsen et al., J. Hirnforsch. 32, 735 (1991); M. Abeles, H. Bergman, E. Margalit, E. Vaadia, J. Neurophysiol. 70, 1629 (1993); E. Vaadia et al., Nature 373, 515 (1995).
29. V. N. Murthy and E. E. Fetz, Proc. Natl. Acad. Sci. U.S.A. 89, 5670 (1992); J. N. Sanes and J. P. Donoghue, ibid. 90, 4470 (1993).
30. V. N. Murthy and E. E. Fetz, Proc. Natl. Acad. Sci. U.S.A. 89, 5670 (1992); J. N. Sanes and J. P. Donoghue, ibid. 90, 4470 (1993).
31. D. R. Humphrey, E. M. Schmidt, W. D. Thompson, Science 170, 758 (1970); A. P. Georgopoulos, A. P. Schwartz, R. E. Kettner, ibid. 233, 1416 (1986); F. A. Mussa-Ivaldi, Neurosci. Lett. 91, 106 (1988); A. V. Lukashin, Biol. Cybern. 63, 377 (1990); A. V. Lukashin and A. P. Georgopoulos, ibid. 69, 517 (1993); J. Wickens, B. Hyland, G. Anson, J. Motor Behav. 26, 66 (1994).
32. D. R. Humphrey, E. M. Schmidt, W. D. Thompson, Science 170, 758 (1970); A. P. Georgopoulos, A. P. Schwartz, R. E. Kettner, ibid. 233, 1416 (1986); F. A. Mussa-Ivaldi, Neurosci. Lett. 91, 106 (1988); A. V. Lukashin, Biol. Cybern. 63, 377 (1990); A. V. Lukashin and A. P. Georgopoulos, ibid. 69, 517 (1993); J. Wickens, B. Hyland, G. Anson, J. Motor Behav. 26, 66 (1994).
33. D. R. Humphrey, E. M. Schmidt, W. D. Thompson, Science 170, 758 (1970); A. P. Georgopoulos, A. P. Schwartz, R. E. Kettner, ibid. 233, 1416 (1986); F. A. Mussa-Ivaldi, Neurosci. Lett. 91, 106 (1988); A. V. Lukashin, Biol. Cybern. 63, 377 (1990); A. V. Lukashin and A. P. Georgopoulos, ibid. 69, 517 (1993); J. Wickens, B. Hyland, G. Anson, J. Motor Behav. 26, 66 (1994).
34. D. R. Humphrey, E. M. Schmidt, W. D. Thompson, Science 170, 758 (1970); A. P. Georgopoulos, A. P. Schwartz, R. E. Kettner, ibid. 233, 1416 (1986); F. A. Mussa-Ivaldi, Neurosci. Lett. 91, 106 (1988); A. V. Lukashin, Biol. Cybern. 63, 377 (1990); A. V. Lukashin and A. P. Georgopoulos, ibid. 69, 517 (1993); J. Wickens, B. Hyland, G. Anson, J. Motor Behav. 26, 66 (1994).
35. D. R. Humphrey, E. M. Schmidt, W. D. Thompson, Science 170, 758 (1970); A. P. Georgopoulos, A. P. Schwartz, R. E. Kettner, ibid. 233, 1416 (1986); F. A. Mussa-Ivaldi, Neurosci. Lett. 91, 106 (1988); A. V. Lukashin, Biol. Cybern. 63, 377 (1990); A. V. Lukashin and A. P. Georgopoulos, ibid. 69, 517 (1993); J. Wickens, B. Hyland, G. Anson, J. Motor Behav. 26, 66 (1994).
36. D. R. Humphrey, E. M. Schmidt, W. D. Thompson, Science 170, 758 (1970); A. P. Georgopoulos, A. P. Schwartz, R. E. Kettner, ibid. 233, 1416 (1986); F. A. Mussa-Ivaldi, Neurosci. Lett. 91, 106 (1988); A. V. Lukashin, Biol. Cybern. 63, 377 (1990); A. V. Lukashin and A. P. Georgopoulos, ibid. 69, 517 (1993); J. Wickens, B. Hyland, G. Anson, J. Motor Behav. 26, 66 (1994).
37. A. Riehle, S. Grün, A. Aertsen, J. Requin, Soc. Neurosci. Abstr. 21, 2077 (1995).
38. J. Requin, J. Brener, C. Ring, in Handbook of Cognitive Psychophysiology: Central and Autonomous Nervous System Approaches, R. R. Jennings and M. G. H. Coles, Eds. (Wiley, New York, 1991) pp. 357-448.
39. Experiments were performed on two macaque monkeys cared for as described [The NIH Guide for the Care and Use of Laboratory Animals (National Institutes of Health, Bethesda, MD) and the French government regulations]. After training, the animals were prepared for multiple single-neuron recordings. A cylindrical stainless steel recording chamber (inner diameter, 15 mm) was implanted over the contralateral primary motor cortex (MI) under aseptic conditions and general anesthesia. A stainless steel T bar was cemented to the skull to fixate the animal's head during recording sessions. To record multiple single-neuron activity, a multielectrode microdrive [V. B. Mountcastle, R. J. Reitböck, G. F. Poggio, M. A. Steinmetz, J. Neurosci. Methods 36, 77 (1991)] was used to transdurally insert seven independently driven microelectrodes (quartz insulated platinum-tungsten electrodes; outer diameter, 80 μm; impedance, 2 to 5 M Ω at 1000 Hz). The electrodes (spaced 330 μm apart) were arranged in most cases in a circle - that is, one electrode in the middle and six electrodes around - and sometimes in a row (distance between first and seventh electrodes, 1980 μm). From each electrode, single-neuron spikes were isolated by using a window discriminator. Classical histological techniques were used to reconstruct the recording sites and to define the cortical areas [A. Riehle and J. Requin, J. Neurophysiol. 61, 534 (1989)].
40. Experiments were performed on two macaque monkeys cared for as described [The NIH Guide for the Care and Use of Laboratory Animals (National Institutes of Health, Bethesda, MD) and the French government regulations]. After training, the animals were prepared for multiple single-neuron recordings. A cylindrical stainless steel recording chamber (inner diameter, 15 mm) was implanted over the contralateral primary motor cortex (MI) under aseptic conditions and general anesthesia. A stainless steel T bar was cemented to the skull to fixate the animal's head during recording sessions. To record multiple single-neuron activity, a multielectrode microdrive [V. B. Mountcastle, R. J. Reitböck, G. F. Poggio, M. A. Steinmetz, J. Neurosci. Methods 36, 77 (1991)] was used to transdurally insert seven independently driven microelectrodes (quartz insulated platinum-tungsten electrodes; outer diameter, 80 μm; impedance, 2 to 5 M Ω at 1000 Hz). The electrodes (spaced 330 μm apart) were arranged in most cases in a circle - that is, one electrode in the middle and six electrodes around - and sometimes in a row (distance between first and seventh electrodes, 1980 μm). From each electrode, single-neuron spikes were isolated by using a window discriminator. Classical histological techniques were used to reconstruct the recording sites and to define the cortical areas [A. Riehle and J. Requin, J. Neurophysiol. 61, 534 (1989)].
41. Experiments were performed on two macaque monkeys cared for as described [The NIH Guide for the Care and Use of Laboratory Animals (National Institutes of Health, Bethesda, MD) and the French government regulations]. After training, the animals were prepared for multiple single-neuron recordings. A cylindrical stainless steel recording chamber (inner diameter, 15 mm) was implanted over the contralateral primary motor cortex (MI) under aseptic conditions and general anesthesia. A stainless steel T bar was cemented to the skull to fixate the animal's head during recording sessions. To record multiple single-neuron activity, a multielectrode microdrive [V. B. Mountcastle, R. J. Reitböck, G. F. Poggio, M. A. Steinmetz, J. Neurosci. Methods 36, 77 (1991)] was used to transdurally insert seven independently driven microelectrodes (quartz insulated platinum-tungsten electrodes; outer diameter, 80 μm; impedance, 2 to 5 M Ω at 1000 Hz). The electrodes (spaced 330 μm apart) were arranged in most cases in a circle -that is, one electrode in the middle and six electrodes around - and sometimes in a row (distance between first and seventh electrodes, 1980 μm). From each electrode, single-neuron spikes were isolated by using a window discriminator. Classical histological techniques were used to reconstruct the recording sites and to define the cortical areas [A. Riehle and J. Requin, J. Neurophysiol. 61, 534 (1989)].
42. The activity of 379 neurons recorded in 145 sessions was selected for analysis by the following criteria: a discharge frequency of more than five impulses per second, which was reproducible across multiple trials. Of those, 21 neurons (5.5%) did not change their activity during the task. Of the remaining 358 task-related neurons, 194 (51.2%) changed their activity during the PP, and 351 (92.6%) changed their activity after occurrence of the RS - that is, in relation to execution of movement. The change in PP activity consisted of a phasic modulation of the firing rate after occurrence of the PS (87 neurons, with increased activity in 78 neurons and decreased activity in 9 neurons), a sustained modulation of the firing rate (127 neurons, with increased activity in 82 neurons and decreased activity in 45 neurons), or a phasic change in activity at 600 ms - that is, the moment when the animal expected the first RS (32 neurons). Note that one neuron could exhibit both phasic and tonic changes in activity during the PP.
43. Dynamic changes in synchronicity between neurons were analyzed off-line by determining epochs of statistically significant, excess synchronized firing. The analysis proceeded in several stages (Fig. 2, A to F), details of which are described and calibrated in (18) (S. Grün and A. Aertsen, in preparatio n ). Briefly, the occurrences of action potentials elicited by N simultaneously recorded neurons were transformed by appropriate binning to N-dimensional joint-activity vectors consisting of ones (action potential) and zeros (no action potential). The composition of these vectors represents the various constellations of coincident spiking activity across the N neurons. Under the null hypothesis that N neurons fire independently, the expected number of occurrences of any joint-spike constellation and the associated probability distribution can be calculated analytically from (and, hence, normalized for) the single neuron firing rates. Using this distribution, we tested the statistical significance of the difference between the observed and the expected numbers of coincident events; those occurrences that exceeded a significance level of 5% are referred to as UEs. Throughout the analyses reported here, the coincidence bin width was fixed to 5 ms; this value was motivated by experimental observations on the temporal precision of spiking in cortical pyramidal neurons (10) [Z. F. Mainen and T. J. Sejnowski, Science 268, 1503 (1995)]. In a number of cases (for example, for the data used in Fig. 2), we varied this parameter systematically and found even significant UEs with similar temporal clustering for coincidence precision down to 1 to 2 ms. To normalize for within-trial nonstationarities in the discharge rates of the neurons, the modulations in spike rates and coincidence rate were determined on the basis of short data segments by sliding a fixed time window of 100 ms along the data in steps of the coincidence bin width. This timing segmentation was applied to each trial, and the data of corresponding segments in all trials were then analyzed as one quasi-stationary data set. Throughout the analyses reported here, the sliding window width was fixed to 100 ms; this choice was motivated by the typically observed rates of change of the activity in the recorded neurons and the need to acquire reliable statistics. In a number of cases, this parameter was varied systematically between 50 and 150 ms, with no substantial effects on the results.
44. Dynamic changes in synchronicity between neurons were analyzed off-line by determining epochs of statistically significant, excess synchronized firing. The analysis proceeded in several stages (Fig. 2, A to F), details of which are described and calibrated in (18) (S. Grün and A. Aertsen, in preparatio n ). Briefly, the occurrences of action potentials elicited by N simultaneously recorded neurons were transformed by appropriate binning to N-dimensional joint-activity vectors consisting of ones (action potential) and zeros (no action potential). The composition of these vectors represents the various constellations of coincident spiking activity across the N neurons. Under the null hypothesis that N neurons fire independently, the expected number of occurrences of any joint-spike constellation and the associated probability distribution can be calculated analytically from (and, hence, normalized for) the single neuron firing rates. Using this distribution, we tested the statistical significance of the difference between the observed and the expected numbers of coincident events; those occurrences that exceeded a significance level of 5% are referred to as UEs. Throughout the analyses reported here, the coincidence bin width was fixed to 5 ms; this value was motivated by experimental observations on the temporal precision of spiking in cortical pyramidal neurons (10) [Z. F. Mainen and T. J. Sejnowski, Science 268, 1503 (1995)]. In a number of cases (for example, for the data used in Fig. 2), we varied this parameter systematically and found even significant UEs with similar temporal clustering for coincidence precision down to 1 to 2 ms. To normalize for within-trial nonstationarities in the discharge rates of the neurons, the modulations in spike rates and coincidence rate were determined on the basis of short data segments by sliding a fixed time window of 100 ms along the data in steps of the coincidence bin width. This timing segmentation was applied to each trial, and the data of corresponding segments in all trials were then analyzed as one quasi-stationary data set. Throughout the analyses reported here, the sliding window width was fixed to 100 ms; this choice was motivated by the typically observed rates of change of the activity in the recorded neurons and the need to acquire reliable statistics. In a number of cases, this parameter was varied systematically between 50 and 150 ms, with no substantial effects on the results.
45. S. Grün, Unitary Joint-Events in Multiple-Neuron Spiking Activity (Harry Deutsch, Thun, Germany, 1996).
46. A. Riehle et al., in Supercomputing in Brain Research: From Tomography to Neural Networks, H. J. Herrmann, D. E. Wolf, E. Pöppel, Eds. (World Scientific, Singapore, 1995), pp. 281-288; A. Riehle, S. Grün, A. Aertsen, J. Requin, in Artificial Neural Networks - ICANN '96, C. von der Malsburg, W. von Seelen, J. C. Vorbrüggen, B. Sendhoff, Eds. (Springer, Berlin, 1996), pp. 673-678.
47. A. Riehle et al., in Supercomputing in Brain Research: From Tomography to Neural Networks, H. J. Herrmann, D. E. Wolf, E. Pöppel, Eds. (World Scientific, Singapore, 1995), pp. 281-288; A. Riehle, S. Grün, A. Aertsen, J. Requin, in Artificial Neural Networks - ICANN '96, C. von der Malsburg, W. von Seelen, J. C. Vorbrüggen, B. Sendhoff, Eds. (Springer, Berlin, 1996), pp. 673-678.
48. G. Palm, A. Aertsen, G. L. Gerstein, Biol. Cybern. 59, 1 (1988); A. Aertsen, G. L. Gerstein, M. K. Habib, G. Palm, J. Neurophysiol. 61, 900 (1989).
49. G. Palm, A. Aertsen, G. L. Gerstein, Biol. Cybern. 59, 1 (1988); A. Aertsen, G. L. Gerstein, M. K. Habib, G. Palm, J. Neurophysiol. 61, 900 (1989).
50. A. M. H. J. Aertsen and G. L. Gerstein, Brain Res. 340, 341 (1985).
51. We thank M. Abeles, S. Rotter, G. Schöner, W. Singer, and E. Vaadia for their constructive comments. We thank W. Coulmance for writing the data acquisition software and N. Vitton for assistance throughout the experiments. Supported in part by grants from the Minerva Foundation (S.G.) and the Human Frontier Science Program (A.A. and M.D.).