Linking spontaneous activity of single cortical neurons and the underlying functional architecture

Science

Volumn 286, Issue 5446, 1999, Pages 1943-1946

  Tsodyks, M ^a   Kenet, T ^a   Grinvald, A ^a   Arieli, A ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL EXPERIMENT; ARTICLE; INFORMATION PROCESSING; NERVE CELL; NERVE CELL NETWORK; NERVE POTENTIAL; NONHUMAN; PRIORITY JOURNAL; SPATIAL ORIENTATION; STRIATE CORTEX;

ACTION POTENTIALS; ANIMALS; BRAIN MAPPING; CATS; EVOKED POTENTIALS, VISUAL; IMAGE PROCESSING, COMPUTER-ASSISTED; NERVE NET; NEURONS; PATCH-CLAMP TECHNIQUES; PHOTIC STIMULATION; VISUAL CORTEX; VISUAL PATHWAYS;

EID: 0033521056     PISSN: 00368075     EISSN: None     Source Type: Journal    
DOI: 10.1126/science.286.5446.1943     Document Type: Article

References (56)

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22. Recent advances, including the design of improved voltage-sensitive dyes providing a 10- to 30-fold improvement in signal-to-noise ratio (D. Shoham et al., Neuron, in press), the development of a high-resolution fast camera [T. Iijima, G. Matosomoto, Y. Kosokoro, Neuroscience 51, 211 (1992)], and the modification we introduced for in vivo experiments, have enabled us to obtain many of the present results. For an updated review see A. Grinvald et al., in Modern Techniques in Neuroscience Research, U. Windhorst and H. Johansson, Eds. (Springer-Verlag, Berlin, Heidelberg, New York, 1999), pp. 893-969.
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26. For this study we selected neurons that fired at least 100 action potentials in a single spontaneous recording session.
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32. This functional map for orientation preference shown in Fig. 1D is defined as a single condition orientation map. Thus far such maps could not be obtained due to the large noise previously associated with voltage-sensitive dye imaging. Only the recent technical advances allowed us to obtain reproducible single condition maps (7). To minimize contributions from the intrinsic signals, the data were filtered above 0.6 Hz and the maps were computed with frames from 100 to 150 ms after stimulus onset, much earlier than the onset of the slow intrinsic signal. Normally, maps are revealed by differential imaging only [T. Bonhoeffer and A. Grinvald, J. Neurosci. 13, 4157 (1993)]. The single condition map was validated by comparing it to the differential orientation maps obtained by using two stimuli of orthogonal orientations (Fig. 1B). In addition, the map was confirmed by imaging based on intrinsic signals. The latter two types of maps can be obtained with a much better signal-so-noise ratio. Therefore, we are confident that the PCS shown here corresponds to the relevant functional architecture.
33. The evident variability in both single-unit and population responses could have various sources, including the impact of ongoing activity on evoked activity (2) and relatively large noise in these single-trial images. The values of the correlation coefficients warrant a discussion. The fact that correlation coefficients never reach high values (above 0.6) could manifest the mixing of several cortical states in one PCS during the averaging, or it could result from the residual noise in the recording of instantaneous activity patterns. The values of the correlation coefficients should be compared with the highest value that can be obtained for two maps derived under the same experimental conditions. The correlation between two maps produced by the same stimuli on two independent sets of trials was 0.81.
34. We converted the correlation coefficient between the population activity and the neuron's PCS into a predicted instantaneous firing, as will be explained later (Fig. 3). We then generated a Poisson spike train using this predicted instantaneous rate.
35. The average correlation coefficient for all five pairs of spontaneous and evoked activity sessions was 0.73 (range 0.65 to 0.81) from five different cats.
36. We expect that spontaneous activity of a cortical neuron can be predicted by this procedure whenever the neuron has response properties that are common to many other neurons. In other cases where the tuning properties are not that robust, or the neuron is a member of neuronal assemblies possessing different spatial structures, other types of analysis, such as clustering techniques, may be required.
37. In Fig. 2 we used the neuron's PCS to predict the spike trains. In Fig. 1, C and D, we showed that the PCS and the map of the functional architecture are very similar. Therefore, here we performed the same analysis using both the functional map and the PCS. The results were very similar. In Fig. 3 we show the results based on the functional map.
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56. We thank K. Pawelzik for illuminating discussions and M. Abeles, A. Aertsen, D. Sagi, and H. Sompolinsky for useful comments on the manuscript. Supported by grants from the German Israel Foundation, the Joint German Israeli Research Program, the Wolfson Foundation, Minerva, and Ms. Enoch.