72-Hz refresh we used an antialiasing algorithm in which each 13.8-ms frame of a movie was construct- ed by averaging 14 images representing the position of the CRF at about 1-ms resolution. 11. All animal procedures were approved by the Univer- sity of California, Berkeley, Animal Care and Use Committee and conformed to or exceeded all rele- vant National Institutes of Health and U.S. Depart- ment of Agriculture standards. Single neuron record- ings were made from two awake, behaving macaque monkeys (Macaca mulatta) with extracellular elec- trodes. Additional details about recording and surgi- cal procedures are given in [C. E. Connor et al., J. Neurosci. 17, 3201 (1997)]. All data reported here were taken under conditions of excellent single-unit isolation. Eye position was monitored with a scleral search coil and trials were aborted if the eye deviated from fixation by more than 0.35°. Movie duration varied from 5 to 10 s. During recording sessions each movie was divided into 5-s segments; segments were then shown in and around the CRF on successive trials while the animal performed a fixation task for a juice reward. Each trial consisted of a stimulus of a single size with differently sized stimulus conditions randomly interleaved across trials. 12. A well-established and useful description of how sparsely a neuron responds across stimuli is given by its activity fraction, A = ( r i /n) 2 / (r i 2 /n). For further discussion see [E. T. Rolls and M. J. Tovee, J. Neuro- physiol. 73, 713 (1995)]. Our sparseness statistic is a convenient rescaling of A that ranges from 0% to 100%: S = (1 - A)/(1 - A MIN ) = (1 - A)/(1 - 1/n). 13. Throughout this report we measured significance with randomization tests using 1000 random permu- tations of the relevant data. For further discussion see [B. F. J. Manly, Randomization and Monte-Carlo Methods in Biology, (Chapman & Hall, New York, 1991)]. 14. If responses are averaged within a fixation, sparse- ness declines from 41 to 23%, 52 to 34%, 61 to 42%, and 62 to 45% for stimuli one, two, three, and four times the size of the CRF, respectively. 15. The boundaries of the CRF were estimated with bar and grating stimuli whose characteristics were con- trolled interactively. For 38 of 61 neurons we con- firmed these manual estimates by reverse correlation on responses evoked by a dynamic sequence of small white squares distributed in and around the CRF (square positions were chosen randomly for each frame). Reliable CRF estimates were obtained with 150 to 300 s of data (30 to 60 behavioral fixation trials). Generally there is excellent agreement be- tween the CRF profile estimates obtained with the two methods. Our CRF estimates ranged from about 20 to 50 min of arc, which is entirely consistent with the range of receptive field diameters obtained in awake behaving macaques by other researchers; for example, see [D. M. Snodderly and M. Gur, J. Neuro- physiol. 74, 2100 (1995)]. 16. S. J. Judge, R. H. Wurtz, B. J. Richmond, J. Neuro- physiol. 43, 1133 (1980); B. C. Motter, J. Neuro- physiol. 70, 909 (1993). 17. Animals viewed high-resolution natural images digi- tized on commercial photo-CDs (Corel Corp.) and shown at a resolution of 1280  1024 pixels. Images were shown for 10 s each. Neural responses and eye position were recorded continuously during this free viewing (8). Natural vision movies that simulated these specific free-viewing episodes were construct- ed by using the eye position records to determine the position of the recorded CRF during free viewing. In six cells the diameter of the reconstructed movies was four times the CRF, and in 11 cells it was three times the CRF. These data have been combined in this report. 18. Each free-viewing episode produced a single-spike train evoked by a unique pattern of exploratory eye movements. In contrast, natural vision movies were repeated many times. To obtain comparable sparse- ness estimates for these data, we separately analyzed the spike train evoked by each repetition of the natural vision movie. The average of this distribution of sparseness values was then compared with the single sparseness value obtained from the free-view- ing data. To ensure matched stimulus conditions, we made all comparisons on a movie-by-movie basis. Note that sparseness values based on single-spike trains are biased upward because of the discrete nature of spike generation. 19. The random sinusoidal grating sequence was similar to that used by D. L. Ringach, M. J. Hawken, R. Shapley [Nature 387, 281 (1997)]. The orientation, spatial frequency, and phase of the grating were chosen randomly on each video frame (at 72 Hz). Gratings were shown at a Michelson contrast of 0.5. Before analysis, stimuli were binned into 10° orien- tation steps and 6 to 12 spatial frequency steps. Responses were analyzed by parametric reverse cor- relation on orientation and spatial frequency, aver- aging over phase. The mean responses across stimu- lus bins (at the peak response latency) were used to estimate the sparseness statistic. 20. A. J. Bell and T. J. Sejnowski, Vision Res. 37, 3327 (1997). 21. Several theoretical studies of sparse population cod- ing have reported the kurtosis of the distribution of responses observed across a set of linear filters, with respect to a particular stimulus ensemble (2, 20). This measure is not directly applicable to our data be- cause the responses of area V1 neurons are asym- metric: cells typically exhibit low spontaneous rates and appropriate stimuli elevate these rates. To esti- mate kurtosis we converted each response distribu- tion to a symmetric distribution by reflecting the data about the origin. The resulting symmetric dis- tributions are unimodal with zero mean and decrease smoothly to zero. Our kurtosis statistic is well be- haved and directly comparable to the results of the- oretical studies. 22. Let P 1 and P 2 be the PSTH response vectors for a pair of neurons. Then cos() = P 1 P 2 /P 1   P 2 , where P n  is the norm of the appropriate vector. This measure is sensitive to changes across the basis dimensions of the movie time stream and is insensi- tive to differences in absolute rate. 23. It is difficult to choose a scalar measure of response similarity appropriate for all situations; see [P. Di Lorenzo, J. Neurophysiol. 62, 823 (1989)]. To validate our results we performed two alternative versions of the population decorrelation analysis. For each neu- ron pair we also computed both the linear correlation coefficient and the neural discrimination index of Di Lorenzo. In both cases, nCRF stimulation leads to significant decorrelation (P  0.001). To ensure that the slightly different stimulus sizes do not influence our results, we also performed all similarity analyses on a data set restricted to neuron pairs with identical CRF sizes (and thus identical stimulation). Under these conditions the decorrelating effect of the nCRF remains significant (P  0.001). 24. The compound grating stimulus consisted of a CRF conditioning grating and a probe grating. We set the conditioning grating’s orientation and spatial fre- quency to the neuron’s preferred values [as deter- mined by reverse correlation on responses to a dy- namic grating sequence (19) presented in the CRF]. The phase of the conditioning grating varied random- ly with each video frame. Both gratings were present- ed at a Michelson contrast of 0.5 and their edges were blended into one another and into the back- ground. We performed reverse correlation on the position of the probe grating within the nCRF annulus (collapsing over all other parameters). To measure baseline responses we presented interleaved trials containing only the conditioning grating. 25. G. A. Walker et al., J. Neurosci. 19, 10536 (1999). 26. Y. Dan, J. J. Atick, R. C. Reid, J. Neurosci. 16, 3351 (1996). 27. M. S. Lewicki and B. A. Olshausen, in Advances in Neural Information Processing Systems 10, M. I. Jor- dan, M. J. Kearns, S. A. Solla, Eds. (MIT Press, Cam- bridge, MA, 1997) pp. 815–821. 28. D. J. Heeger, Visual Neurosci. 9, 181 (1992); C. D. Gilbert, Neuron 9, 1 (1992); A. M. Sillito et al., Nature 378, 492 (1995); C. D. Gilbert et al., Proc. Natl. Acad. Sci. U.S.A. 93, 615 (1996); M. Carandini et al., J. Neu- rosci. 17, 8621 (1997); J. J. Knierim and D. C. Van Essen, J. Neurophysiol. 67, 961 (1992). 20 August 1999; accepted 27 December 1999 Mitochondrial FtsZ in a Chromophyte Alga Peter L. Beech, 1,3 * Thao Nheu, 3 † Thomas Schultz, 3 ‡ Shane Herbert, 1 Trevor Lithgow, 4 Paul R. Gilson, 2,3 Geoffrey I. McFadden 2,3 A homolog of the bacterial cell division gene ftsZ was isolated from the alga Mallomonas splendens. The nuclear-encoded protein (MsFtsZ-mt) was closely related to FtsZs of the -proteobacteria, possessed a mitochondrial targeting signal, and localized in a pattern consistent with a role in mitochondrial division. Although FtsZs are known to act in the division of chloroplasts, MsFtsZ-mt appears to be a mitochondrial FtsZ and may represent a mitochondrial division protein. Mitochondria are ubiquitous organelles that form networks, reticulae, or punctate struc- tures in eukaryotic cells. Mitochondria in many cells appear to constitutively fuse with one another and divide (1), but we know little about the proteins involved in these process- es, particularly mitochondrial division. Eu- karyotes depend on mitochondria for respira- tion and adenosine triphosphate synthesis and rely on them to divide before daughter mito- chondria can be apportioned to each new cell generation. In chloroplasts, homologs of the bacterial cell division protein FtsZ are essen- tial components of the organellar division machinery (2). FtsZ is found in nearly all prokaryotes, is structurally related to tubulin, and accumulates at the furrow between divid- ing cells, playing a critical role in cell divi- sion (3). No potential mitochondrial FtsZ has been identified in the complete genomes of Caenorhabditis elegans or Saccharomyces cerevisiae. However, because both mitochon- dria and chloroplasts arose from endosymbi- otic bacteria, we anticipated that early in evolution, mitochondrial division might also have been regulated by FtsZ. Here we de- R EPORTS 18 FEBRUARY 2000 VOL 287 SCIENCE www.sciencemag.org 1276