@article {69723, title = {The first complete 3D reconstruction and morphofunctional mapping of an insect eye}, year = {2025}, month = {01/2025}, doi = {10.7554/elife.103247.1}, url = {http://dx.doi.org/10.7554/eLife.103247.1}, author = {Makarova, Anastasia A and Chua, Nicholas J and Diakova, Anna V and Desyatirkina, Inna A and Gunn, Pat and Pang, Song and Xu, C Shan and Hess, Harald and Chklovskii, Dmitri B and Polilov, Alexey A} } @article {69022, title = {A complete reconstruction of the early visual system of an adult insect.}, journal = {Current Biology}, volume = {33}, year = {2023}, month = {2023 Nov 06}, pages = {4611-4623}, abstract = {

For most model organisms in neuroscience, research into visual processing in the brain is difficult because of a lack of high-resolution maps that capture complex neuronal circuitry. The microinsect Megaphragma viggianii, because of its small size and non-trivial behavior, provides a unique opportunity for tractable whole-organism connectomics. We image its whole head using serial electron microscopy. We reconstruct its compound eye and analyze the optical properties of the ommatidia as well as the connectome of the first visual neuropil-the lamina. Compared with the fruit fly and the honeybee, Megaphragma visual system is highly simplified: it has 29 ommatidia per eye and 6 lamina neuron types. We report features that are both stereotypical among most ommatidia and specialized to some. By identifying the "barebones" circuits critical for flying insects, our results will facilitate constructing computational models of visual processing in insects.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2023.09.021}, author = {Chua, Nicholas J and Makarova, Anastasia A and Gunn, Pat and Villani, Sonia and Cohen, Ben and Thasin, Myisha and Wu, Jingpeng and Shefter, Deena and Pang, Song and Xu, C Shan and Hess, Harald F and Polilov, Alexey A and Chklovskii, Dmitri B} } @article {67280, title = {CaImAn an open source tool for scalable calcium imaging data analysis}, journal = {eLife}, volume = {8}, year = {2019}, month = {01/2019}, abstract = {

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn\ provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn\ is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn\ we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn\ achieves near-human performance in detecting locations of active neurons

}, doi = {10.7554/eLife.38173}, url = {https://elifesciences.org/articles/38173}, author = {Giovannucci, Andrea and Friedrich, Johannes and Gunn, Pat and Kalfon, J{\'e}r{\'e}mie and Brown, Brandon L and Koay, Sue Ann and Taxidis, Jiannis and Najafi, Farzaneh and Gauthier, Jeffrey L and Zhou, Pengcheng and Khakh, Baljit S and Tank, David W and Chklovskii, Dmitri B and Pnevmatikakis, Eftychios A} } @article {48436, title = {The comprehensive connectome of a neural substrate for {\textquoteright}ON{\textquoteright} motion detection in Drosophila.}, journal = {eLife}, volume = {6}, year = {2017}, month = {2017 Apr 22}, abstract = {

Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. We reconstructed a comprehensive connectome of the circuits of Drosophila{\textquoteright}s motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4{\textquoteright}s dendrite shafts, while inhibitory inputs localize to the bases. Consistent with our previous study, we reveal that Mi1 and Tm3 cells provide most synaptic contacts onto T4. We are, however, unable to reproduce the spatial offset between these cells reported previously. Our comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors.

}, issn = {2050-084X}, doi = {10.7554/eLife.24394}, author = {Takemura, Shin-ya and Nern, Aljoscha and Chklovskii, Dmitri B and Scheffer, Louis K and Rubin, Gerald M and Meinertzhagen, Ian A} } @article {46974, title = {Automatic adaptation to fast input changes in a time-invariant neural circuit.}, journal = {PLoS Computational Biology}, volume = {11}, year = {2015}, month = {2015 Aug 6}, pages = {e1004315}, doi = {10.1371/journal.pcbi.1004315}, author = {Bharioke, Arjun and Chklovskii, Dmitri B} } @article {45854, title = {A consistent muscle activation strategy underlies crawling and swimming in Caenorhabditis elegans.}, journal = {Journal of the Royal Society Interface}, volume = {12}, year = {2015}, month = {2015 Jan 6}, pages = {20140963}, abstract = {

Although undulatory swimming is observed in many organisms, the neuromuscular basis for undulatory movement patterns is not well understood. To better understand the basis for the generation of these movement patterns, we studied muscle activity in the nematode Caenorhabditis elegans. Caenorhabditis elegans exhibits a range of locomotion patterns: in low viscosity fluids the undulation has a wavelength longer than the body and propagates rapidly, while in high viscosity fluids or on agar media the undulatory waves are shorter and slower. Theoretical treatment of observed behaviour has suggested a large change in force-posture relationships at different viscosities, but analysis of bend propagation suggests that short-range proprioceptive feedback is used to control and generate body bends. How muscles could be activated in a way consistent with both these results is unclear. We therefore combined automated worm tracking with calcium imaging to determine muscle activation strategy in a variety of external substrates. Remarkably, we observed that across locomotion patterns spanning a threefold change in wavelength, peak muscle activation occurs approximately 45{\textdegree} (1/8th of a cycle) ahead of peak midline curvature. Although the location of peak force is predicted to vary widely, the activation pattern is consistent with required force in a model incorporating putative length- and velocity-dependence of muscle strength. Furthermore, a linear combination of local curvature and velocity can match the pattern of activation. This suggests that proprioception can enable the worm to swim effectively while working within the limitations of muscle biomechanics and neural control.

}, issn = {1742-5662}, author = {Butler, Victoria J and Branicky, Robyn and Yemini, Eviatar and Liewald, Jana F and Gottschalk, Alexander and Kerr, Rex A and Chklovskii, Dmitri B and Schafer, William R} } @article {47152, title = {Synaptic circuits and their variations within different columns in the visual system of Drosophila.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {112}, year = {2015}, month = {2015 Nov 3}, pages = {13711-6}, abstract = {

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly{\textquoteright}s compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla{\textquoteright}s neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, \<1\% of contacts are not part of a consensus circuit, and we classify those contacts that supplement (E+) or are missing from it (E-). Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form >=20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics.

}, issn = {1091-6490}, doi = {10.1073/pnas.1509820112}, author = {Takemura, Shin-ya and Xu, C Shan and Lu, Zhiyuan and Rivlin, Patricia K and Parag, Toufiq and Olbris, Donald J and Plaza, Stephen and Zhao, Ting and Katz, William T and Umayam, Lowell and Weaver, Charlotte and Hess, Harald F and Horne, Jane Anne and Nunez-Iglesias, Juan and Aniceto, Roxanne and Chang, Lei-Ann and Lauchie, Shirley and Nasca, Ashley and Ogundeyi, Omotara and Sigmund, Christopher and Takemura, Satoko and Tran, Julie and Langille, Carlie and Le Lacheur, Kelsey and McLin, Sari and Shinomiya, Aya and Chklovskii, Dmitri B and Meinertzhagen, Ian A and Scheffer, Louis K} } @article {6905, title = {Toward large-scale connectome reconstructions.}, journal = {Current Opinion in Neurobiology}, volume = {25C}, year = {2014}, month = {2014 Mar 2}, pages = {201-10}, abstract = {

Recent results have shown the possibility of both reconstructing connectomes of small but biologically interesting circuits and extracting from these connectomes insights into their function. However, these reconstructions were heroic proof-of-concept experiments, requiring person-months of effort per neuron reconstructed, and will not scale to larger circuits, much less the brains of entire animals. In this paper we examine what will be required to generate and use substantially larger connectomes, finding five areas that need increased attention: firstly, imaging better suited to automatic reconstruction, with excellent z-resolution; secondly, automatic detection, validation, and measurement of synapses; thirdly, reconstruction methods that keep and use uncertainty metrics for every object, from initial images, through segmentation, reconstruction, and connectome queries; fourthly, processes that are fully incremental, so that the connectome may be used before it is fully complete; and finally, better tools for analysis of connectomes, once they are obtained.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2014.01.019}, author = {Plaza, Stephen M and Scheffer, Louis K and Chklovskii, Dmitri B} } @article {6881, title = {Wiring economy can account for cell body placement across species and brain areas.}, journal = {Current biology : CB}, volume = {24}, year = {2014}, month = {2014 Feb 3}, pages = {R109-10}, abstract = {The placement of neuronal cell bodies relative to the neuropile differs among species and brain areas. Cell bodies can be either embedded as in mammalian cortex or segregated as in invertebrates and some other vertebrate brain areas. Why are there such different arrangements? Here we suggest that the observed arrangements may simply be a reflection of wiring economy, a general principle that tends to reduce the total volume of the neuropile and hence the volume of the inclusions in it. Specifically, we suggest that the choice of embedded versus segregated arrangement is determined by which neuronal component - the cell body or the neurite connecting the cell body to the arbor - has a smaller volume. Our quantitative predictions are in agreement with existing and new measurements.}, issn = {1879-0445}, doi = {10.1016/j.cub.2013.12.012}, author = {Rivera-Alba, Marta and Peng, Hanchuan and de Polavieja, Gonzalo G and Chklovskii, Dmitri B} } @article {6263, title = {Machine learning of hierarchical clustering to segment 2D and 3D images.}, journal = {PLoS One}, volume = {8}, year = {2013}, month = {2013}, pages = {e71715}, abstract = {

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0071715}, author = {Nunez-Iglesias, Juan and Kennedy, Ryan and Toufiq Parag and Shi, Jianbo and Chklovskii, Dmitri B} } @article {6674, title = {Thalamocortical input onto layer 5 pyramidal neurons measured using quantitative large-scale array tomography.}, journal = {Frontiers in Neural Circuits}, volume = {7}, year = {2013}, month = {2013}, pages = {177}, abstract = {

The subcellular locations of synapses on pyramidal neurons strongly influences dendritic integration and synaptic plasticity. Despite this, there is little quantitative data on spatial distributions of specific types of synaptic input. Here we use array tomography (AT), a high-resolution optical microscopy method, to examine thalamocortical (TC) input onto layer 5 pyramidal neurons. We first verified the ability of AT to identify synapses using parallel electron microscopic analysis of TC synapses in layer 4. We then use large-scale array tomography (LSAT) to measure TC synapse distribution on L5 pyramidal neurons in a 1.00 {\texttimes} 0.83 {\texttimes} 0.21 mm(3) volume of mouse somatosensory cortex. We found that TC synapses primarily target basal dendrites in layer 5, but also make a considerable input to proximal apical dendrites in L4, consistent with previous work. Our analysis further suggests that TC inputs are biased toward certain branches and, within branches, synapses show significant clustering with an excess of TC synapse nearest neighbors within 5-15 μm compared to a random distribution. Thus, we show that AT is a sensitive and quantitative method to map specific types of synaptic input on the dendrites of entire neurons. We anticipate that this technique will be of wide utility for mapping functionally-relevant anatomical connectivity in neural circuits.

}, issn = {1662-5110}, doi = {10.3389/fncir.2013.00177}, author = {Rah, Jong-Cheol and Bas, Erhan and Colonell, Jennifer and Mishchenko, Yuriy and Karsh, Bill and Fetter, Richard D and Myers, Eugene W and Chklovskii, Dmitri B and Svoboda, Karel and Harris, Timothy D and Isaac, John T R} } @article {6178, title = {A visual motion detection circuit suggested by Drosophila connectomics.}, journal = {Nature}, volume = {500}, year = {2013}, month = {2013 Aug 7}, pages = {175{\textendash}81}, abstract = {

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.

}, issn = {1476-4687}, doi = {doi:10.1038/nature12450}, url = {http://www.nature.com/nature/journal/v500/n7461/full/nature12450.html}, author = {Takemura, Shin-ya and Bharioke, Arjun and Lu, Zhiyuan and Nern, Aljoscha and Vitaladevuni, Shiv and Rivlin, Patricia K. and Katz, William T. and Olbris, Donald J. and Plaza, Stephen M and Winston, Philip and Zhao, Ting and Horne, Jane Anne and Fetter, Richard D and Takemura, Satoko and Blazek, Katerina and Chang, Lei-Ann and Ogundeyi, Omotara and Saunders, Mathew A. and Shapiro, Victor and Sigmund, Christopher and Rubin, Gerald M and Scheffer, Louis K and Meinertzhagen, Ian A. and Chklovskii, Dmitri B} } @article {5438, title = {A lattice filter model of the visual pathway.}, journal = {Advances in Neural Information Processing Systems}, volume = {24}, year = {2012}, month = {2012}, pages = {1709-17}, abstract = {

Early stages of visual processing are thought to decorrelate, or whiten, the incoming temporally varying signals. Because the typical correlation time of natural stimuli, as well as the extent of temporal receptive fields of lateral geniculate nucleus (LGN) neurons, is much greater than neuronal time constants, such decorrelation must be done in stages combining contributions of multiple neurons. We propose to model temporal decorrelation in the visual pathway with the lattice filter, a signal processing device for stage-wise decorrelation of temporal signals. The stage-wise architecture of the lattice filter maps naturally onto the visual pathway (photoreceptors -\> bipolar cells -\> retinal ganglion cells -\> LGN) and its filter weights can be learned using Hebbian rules in a stage-wise sequential manner. Moreover, predictions of neural activity from the lattice filter model are consistent with physiological measurements in LGN neurons and fruit fly second-order visual neurons. Therefore, the lattice filter model is a useful abstraction that may help unravel visual system function.

}, author = {Gregor, Karol and Chklovskii, Dmitri B} } @article {6220, title = {A network of spiking neurons for computing sparse representations in an energy-efficient way.}, journal = {Neural computation}, volume = {24}, year = {2012}, month = {2012 Nov}, pages = {2852-72}, abstract = {Computing sparse redundant representations is an important problem in both applied mathematics and neuroscience. In many applications, this problem must be solved in an energy-efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating by low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, the operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We show that the numerical performance of HDA is on par with existing algorithms. In the asymptotic regime, the representation error of HDA decays with time, t, as 1/t. HDA is stable against time-varying noise; specifically, the representation error decays as 1/√t for gaussian white noise.}, keywords = {Action Potentials, Algorithms, Models, Neural Networks (Computer), Neurological, Neurons}, issn = {1530-888X}, doi = {10.1162/NECO_a_00353}, author = {Hu, Tao and Genkin, Alexander and Chklovskii, Dmitri B} } @article {5658, title = {Neuronal circuits underlying persistent representations despite time varying activity.}, journal = {Current Biology}, volume = {22}, year = {2012}, month = {2012 Nov 20}, pages = {2095-103}, abstract = {

Our brains are capable of remarkably stable stimulus representations despite time-varying neural activity. For instance, during delay periods in working memory tasks, while stimuli are represented in working memory, neurons in the prefrontal cortex, thought to support the memory representation, exhibit time-varying neuronal activity. Since neuronal activity encodes the stimulus, its time-varying dynamics appears to be paradoxical and incompatible with stable network stimulus representations. Indeed, this finding raises a fundamental question: can stable representations only be encoded with stable neural activity, or, its corollary, is every change in activity a sign of change in stimulus representation?

}, issn = {1879-0445}, doi = {10.1016/j.cub.2012.08.058}, author = {Druckmann, S and Chklovskii, Dmitri B} } @article {5435, title = {Neuronal spike generation mechanism as an oversampling, noise-shaping A-to-D converter.}, journal = {Advances in Neural Information Processing Systems}, volume = {24}, year = {2012}, month = {2012}, pages = {503-11}, abstract = {

We explore the hypothesis that the neuronal spike generation mechanism is an analog-to-digital converter, which rectifies low-pass filtered summed synaptic currents and encodes them into spike trains linearly decodable in post-synaptic neurons. To digitally encode an analog current waveform, the sampling rate of the spike generation mechanism must exceed its Nyquist rate. Such oversampling is consistent with the experimental observation that the precision of the spike-generation mechanism is an order of magnitude greater than the cut-off frequency of dendritic low-pass filtering. To achieve additional reduction in the error of analog-to-digital conversion, electrical engineers rely on noise-shaping. If noise-shaping were used in neurons, it would introduce correlations in spike timing to reduce low-frequency (up to Nyquist) transmission error at the cost of high-frequency one (from Nyquist to sampling rate). Using experimental data from three different classes of neurons, we demonstrate that biological neurons utilize noise-shaping. We also argue that rectification by the spike-generation mechanism may improve energy efficiency and carry out de-noising. Finally, the zoo of ion channels in neurons may be viewed as a set of predictors, various subsets of which are activated depending on the statistics of the input current.

}, author = {Chklovskii, Dmitri B and Soudry, Daniel} } @article {5205, title = {Neuroscience. The mind of a male?}, journal = {Science}, volume = {337}, year = {2012}, month = {2012 Jul 27}, pages = {416-7}, keywords = {Animal, Animals, Caenorhabditis elegans, Male, Nerve Net, Neurons, Sexual Behavior, Synapses}, issn = {1095-9203}, doi = {10.1126/science.1225853}, author = {Chklovskii, Dmitri B and Bargmann, Cornelia I} } @article {5460, title = {Proprioceptive coupling within motor neurons drives C. elegans forward locomotion.}, journal = {Neuron}, volume = {76}, year = {2012}, month = {2012 Nov 21}, pages = {750-61}, abstract = {

Locomotion requires coordinated motor activity throughout an animal{\textquoteright}s body. In both vertebrates and invertebrates, chains of coupled central pattern generators (CPGs) are commonly evoked to explain local rhythmic behaviors. In C.\ elegans, we report that proprioception within the motor circuit is responsible for propagating and coordinating rhythmic undulatory waves from head to tail during forward movement. Proprioceptive coupling between adjacent body regions transduces rhythmic movement initiated near the head into bending waves driven along the body by a chain of reflexes. Using optogenetics and calcium imaging to manipulate and monitor motor circuit activity of moving C.\ elegans held in microfluidic devices, we found that the B-type cholinergic motor neurons transduce the proprioceptive signal. In C.\ elegans, a sensorimotor feedback loop operating within a specific type of motor neuron both drives and organizes body movement.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2012.08.039}, author = {Wen, Quan and Po, Michelle D. and Hulme, Elizabeth and Chen, Sway and Liu, Xinyu and Kwok, Sen Wai and Gershow, Marc and Leifer, Andrew M. and Butler, Victoria and Fang-Yen, Christopher and Kawano, Taizo and Schafer, William R and Whitesides, George and Wyart, Matthieu and Chklovskii, Dmitri B and Samuel, Aravinthan D. T.} } @article {4326, title = {Large-scale automated histology in the pursuit of connectomes.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {31}, year = {2011}, month = {2011 Nov 9}, pages = {16125-38}, abstract = {

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain{\textquoteright}s computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4077-11.2011}, author = {Kleinfeld, David and Bharioke, Arjun and Blinder, Pablo and Bock, Davi D and Briggman, Kevin L and Chklovskii, Dmitri B and Denk, Winfried and Helmstaedter, Moritz and Kaufhold, John P and Lee, Wei-Chung Allen and Meyer, Hanno S and Micheva, Kristina D and Oberlaender, Marcel and Prohaska, Steffen and Reid, R Clay and Smith, Stephen J and Takemura, Shinya and Tsai, Philbert S and Sakmann, Bert} } @article {1233, title = {Structural properties of the Caenorhabditis elegans neuronal network.}, journal = {PLoS Computational Biology}, volume = {7}, year = {2011}, month = {2011 Feb 3}, pages = {e1001066}, abstract = {

Despite recent interest in reconstructing neuronal networks, complete wiring diagrams on the level of individual synapses remain scarce and the insights into function they can provide remain unclear. Even for Caenorhabditis elegans, whose neuronal network is relatively small and stereotypical from animal to animal, published wiring diagrams are neither accurate nor complete and self-consistent. Using materials from White et al. and new electron micrographs we assemble whole, self-consistent gap junction and chemical synapse networks of hermaphrodite C. elegans. We propose a method to visualize the wiring diagram, which reflects network signal flow. We calculate statistical and topological properties of the network, such as degree distributions, synaptic multiplicities, and small-world properties, that help in understanding network signal propagation. We identify neurons that may play central roles in information processing, and network motifs that could serve as functional modules of the network. We explore propagation of neuronal activity in response to sensory or artificial stimulation using linear systems theory and find several activity patterns that could serve as substrates of previously described behaviors. Finally, we analyze the interaction between the gap junction and the chemical synapse networks. Since several statistical properties of the C. elegans network, such as multiplicity and motif distributions are similar to those found in mammalian neocortex, they likely point to general principles of neuronal networks. The wiring diagram reported here can help in understanding the mechanistic basis of behavior by generating predictions about future experiments involving genetic perturbations, laser ablations, or monitoring propagation of neuronal activity in response to stimulation.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1001066}, author = {Varshney, Lav R and Chen, Beth L and Paniagua, Eric and Hall, David H and Chklovskii, Dmitri B} } @article {1245, title = {Increasing depth resolution of electron microscopy of neural circuits using sparse tomographic reconstruction.}, journal = {Computer Vision and Pattern Recognition (CVPR)}, year = {2010}, month = {2010}, pages = {1767-74}, issn = {1063-6919}, doi = {10.1109/CVPR.2010.5539846}, author = {Veeraraghavan, Ashok and Genkin, Alex V and Vitaladevuni, Shiv and Scheffer, Lou and Xu, CShan and Hess, Harald and Fetter, Richard and Cantoni, Marco and Knott, Graham and Chklovskii, Dmitri B} } @article {1234, title = {Semi-automated reconstruction of neural circuits using electron microscopy.}, journal = {Current Opinion in Neurobiology}, volume = {20}, year = {2010}, month = {2010 Oct}, pages = {667-75}, abstract = {

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience, and the focus of the nascent field of connectomics. Previously used to reconstruct the C. elegans wiring diagram, serial-section transmission electron microscopy (ssTEM) is a proven technique for the task. However, to reconstruct more complex circuits, ssTEM will require the automation of image processing. We review progress in the processing of electron microscopy images and, in particular, a semi-automated reconstruction pipeline deployed at Janelia. Drosophila circuits underlying identified behaviors are being reconstructed in the pipeline with the goal of generating a complete Drosophila connectome.

}, issn = {1873-6882}, doi = {10.1371/journal.pcbi.1001066}, author = {Chklovskii, Dmitri B and Vitaladevuni, Shiv and Scheffer, Louis K} } @article {1235, title = {Ultrastructural analysis of hippocampal neuropil from the connectomics perspective.}, journal = {Neuron}, volume = {67}, year = {2010}, month = {2010 Sep 23}, pages = {1009-20}, abstract = {

Complete reconstructions of vertebrate neuronal circuits on the synaptic level require new approaches. Here, serial section transmission electron microscopy was automated to densely reconstruct four volumes, totaling 670 μm(3), from the rat hippocampus as proving grounds to determine when axo-dendritic proximities predict synapses. First, in contrast with Peters{\textquoteright} rule, the density of axons within reach of dendritic spines did not predict synaptic density along dendrites because the fraction of axons making synapses was variable. Second, an axo-dendritic touch did not predict a synapse; nevertheless, the density of synapses along a hippocampal dendrite appeared to be a universal fraction, 0.2, of the density of touches. Finally, the largest touch between an axonal bouton and spine indicated the site of actual synapses with about 80\% precision but would miss about half of all synapses. Thus, it will be difficult to predict synaptic connectivity using data sets missing ultrastructural details that distinguish between axo-dendritic touches and bona fide synapses.

}, keywords = {Animals, Automatic Data Processing, Axons, Dendrites, Electron, Hippocampus, Long-Evans, Male, Microscopy, Neuropil, Newborn, Rats, Synapses, Transmission}, issn = {1097-4199}, doi = {10.1371/journal.pcbi.1001066}, author = {Mishchenko, Yuriy and Hu, Tao and Spacek, Josef and Mendenhall, John and Harris, Kristen M and Chklovskii, Dmitri B} } @article {1236, title = {Maximization of the connectivity repertoire as a statistical principle governing the shapes of dendritic arbors.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {106}, year = {2009}, month = {2009 Jul 28}, pages = {12536-41}, abstract = {

The shapes of dendritic arbors are fascinating and important, yet the principles underlying these complex and diverse structures remain unclear. Here, we analyzed basal dendritic arbors of 2,171 pyramidal neurons sampled from mammalian brains and discovered 3 statistical properties: the dendritic arbor size scales with the total dendritic length, the spatial correlation of dendritic branches within an arbor has a universal functional form, and small parts of an arbor are self-similar. We proposed that these properties result from maximizing the repertoire of possible connectivity patterns between dendrites and surrounding axons while keeping the cost of dendrites low. We solved this optimization problem by drawing an analogy with maximization of the entropy for a given energy in statistical physics. The solution is consistent with the above observations and predicts scaling relations that can be tested experimentally. In addition, our theory explains why dendritic branches of pyramidal cells are distributed more sparsely than those of Purkinje cells. Our results represent a step toward a unifying view of the relationship between neuronal morphology and function.

}, keywords = {Action Potentials, Algorithms, Animals, Axons, Dendrites, Haplorhini, Models, Neural Pathways, Neurological, Purkinje Cells, Pyramidal Cells, Rats, Synapses}, issn = {1091-6490}, doi = {10.1371/journal.pcbi.1001066}, author = {Wen, Quan and Stepanyants, Armen and Elston, Guy N and Grosberg, Alexander Y and Chklovskii, Dmitri B} } @article {1246, title = {Reconstruction of sparse circuits using multi-neuronal excitation (RESCUME).}, journal = {Neural Information Processing Systems}, volume = {22}, year = {2009}, month = {2009}, pages = {790-8}, abstract = {

One of the central problems in neuroscience is reconstructing synaptic connectivity in neural circuits. Synapses onto a neuron can be probed by sequentially stimulating potentially pre-synaptic neurons while monitoring the membrane voltage of the post-synaptic neuron. Reconstructing a large neural circuit using such a "brute force" approach is rather time-consuming and inefficient because the connectivity in neural circuits is sparse. Instead, we propose to measure a post-synaptic neuron{\textquoteright}s voltage while stimulating sequentially random subsets of multiple potentially pre-synaptic neurons. To reconstruct these synaptic connections from the recorded voltage we apply a decoding algorithm recently developed for compressive sensing. Compared to the brute force approach, our method promises significant time savings that grow with the size of the circuit. We use computer simulations to find optimal stimulation parameters and explore the feasibility of our reconstruction method under realistic experimental conditions including noise and non-linear synaptic integration. Multineuronal stimulation allows reconstructing synaptic connectivity just from the spiking activity of post-synaptic neurons, even when sub-threshold voltage is unavailable. By using calcium indicators, voltage-sensitive dyes, or multi-electrode arrays one could monitor activity of multiple postsynaptic neurons simultaneously, thus mapping their synaptic inputs in parallel, potentially reconstructing a complete neural circuit.

}, author = {Hu, Tao and Chklovskii, Dmitri B} } @article {1237, title = {A cost-benefit analysis of neuronal morphology.}, journal = {Journal of Neurophysiology}, volume = {99}, year = {2008}, month = {2008 May}, pages = {2320-8}, abstract = {

Over hundreds of millions of years, evolution has optimized brain design to maximize its functionality while minimizing costs associated with building and maintenance. This observation suggests that one can use optimization theory to rationalize various features of brain design. Here, we attempt to explain the dimensions and branching structure of dendritic arbors by minimizing dendritic cost for given potential synaptic connectivity. Assuming only that dendritic cost increases with total dendritic length and path length from synapses to soma, we find that branching, planar, and compact dendritic arbors, such as those belonging to Purkinje cells in the cerebellum, are optimal. The theory predicts that adjacent Purkinje dendritic arbors should spatially segregate. In addition, we propose two explicit cost function expressions, falsifiable by measuring dendritic caliber near bifurcations.

}, keywords = {Algorithms, Axons, Cerebellum, Cost-Benefit Analysis, Dendrites, Models, Neurological, Neurons, Purkinje Cells, Synapses}, issn = {0022-3077}, doi = {10.1371/journal.pcbi.1001066}, author = {Wen, Quan and Chklovskii, Dmitri B} } @article {1248, title = {Local potential connectivity in cat primary visual cortex.}, journal = {Cerebral Cortex}, volume = {18}, year = {2008}, month = {2008 Jan}, pages = {13-28}, abstract = {

Time invariant description of synaptic connectivity in cortical circuits may be precluded by the ongoing growth and retraction of dendritic spines accompanied by the formation and elimination of synapses. On the other hand, the spatial arrangement of axonal and dendritic branches appears stable. This suggests that an invariant description of connectivity can be cast in terms of potential synapses, which are locations in the neuropil where an axon branch of one neuron is proximal to a dendritic branch of another neuron. In this paper, we attempt to reconstruct the potential connectivity in local cortical circuits of the cat primary visual cortex (V1). Based on multiple single-neuron reconstructions of axonal and dendritic arbors in 3 dimensions, we evaluate the expected number of potential synapses and the probability of potential connectivity among excitatory (pyramidal and spiny stellate) neurons and inhibitory basket cells. The results provide a quantitative description of structural organization of local cortical circuits. For excitatory neurons from different cortical layers, we compute local domains, which contain their potentially pre- and postsynaptic excitatory partners. These domains have columnar shapes with laminar specific radii and are roughly of the size of the ocular dominance column. Therefore, connections between most excitatory neurons in the ocular dominance column can be implemented by local synaptogenesis. Structural connectivity involving inhibitory basket cells is generally weaker than excitatory connectivity. Here, only nearby neurons are capable of establishing more than one potential synapse, implying that within the ocular dominance column these connections have more limited potential for circuit remodeling.

}, keywords = {Animals, Cats, Cells, Cultured, Nerve Net, Neural Pathways, Synapses, Visual Cortex}, issn = {1460-2199}, doi = {10.1093/cercor/bhm027}, author = {Stepanyants, Armen and Hirsch, Judith A and Martinez, Luis M and Kisv{\'a}rday, Zolt{\'a}n F and Ferecsk{\'o}, Alex S and Chklovskii, Dmitri B} } @article {1238, title = {Optimal information storage in noisy synapses under resource constraints.}, journal = {Neuron}, volume = {52}, year = {2006}, month = {2006 Nov 9}, pages = {409-23}, abstract = {

Experimental investigations have revealed that synapses possess interesting and, in some cases, unexpected properties. We propose a theoretical framework that accounts for three of these properties: typical central synapses are noisy, the distribution of synaptic weights among central synapses is wide, and synaptic connectivity between neurons is sparse. We also comment on the possibility that synaptic weights may vary in discrete steps. Our approach is based on maximizing information storage capacity of neural tissue under resource constraints. Based on previous experimental and theoretical work, we use volume as a limited resource and utilize the empirical relationship between volume and synaptic weight. Solutions of our constrained optimization problems are not only consistent with existing experimental measurements but also make nontrivial predictions.

}, keywords = {Animals, Excitatory Postsynaptic Potentials, Information Storage and Retrieval, Memory, Models, Neurological, Synapses, Synaptic Transmission}, issn = {0896-6273}, doi = {10.1371/journal.pcbi.1001066}, author = {Varshney, Lav R and Sj{\"o}str{\"o}m, Per Jesper and Chklovskii, Dmitri B} } @article {1239, title = {Wiring optimization can relate neuronal structure and function.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {103}, year = {2006}, month = {2006 Mar 21}, pages = {4723-8}, abstract = {

We pursue the hypothesis that neuronal placement in animals minimizes wiring costs for given functional constraints, as specified by synaptic connectivity. Using a newly compiled version of the Caenorhabditis elegans wiring diagram, we solve for the optimal layout of 279 nonpharyngeal neurons. In the optimal layout, most neurons are located close to their actual positions, suggesting that wiring minimization is an important factor. Yet some neurons exhibit strong deviations from "optimal" position. We propose that biological factors relating to axonal guidance and command neuron functions contribute to these deviations. We capture these factors by proposing a modified wiring cost function.

}, keywords = {Animals, Caenorhabditis elegans, Models, Neurological, Neurons, Synapses}, issn = {0027-8424}, doi = {10.1371/journal.pcbi.1001066}, author = {Chen, Beth L and Hall, David H and Chklovskii, Dmitri B} } @article {1251, title = {Highly nonrandom features of synaptic connectivity in local cortical circuits.}, journal = {PLoS Biology}, volume = {3}, year = {2005}, month = {2005 Mar}, pages = {e68}, abstract = {

How different is local cortical circuitry from a random network? To answer this question, we probed synaptic connections with several hundred simultaneous quadruple whole-cell recordings from layer 5 pyramidal neurons in the rat visual cortex. Analysis of this dataset revealed several nonrandom features in synaptic connectivity. We confirmed previous reports that bidirectional connections are more common than expected in a random network. We found that several highly clustered three-neuron connectivity patterns are overrepresented, suggesting that connections tend to cluster together. We also analyzed synaptic connection strength as defined by the peak excitatory postsynaptic potential amplitude. We found that the distribution of synaptic connection strength differs significantly from the Poisson distribution and can be fitted by a lognormal distribution. Such a distribution has a heavier tail and implies that synaptic weight is concentrated among few synaptic connections. In addition, the strengths of synaptic connections sharing pre- or postsynaptic neurons are correlated, implying that strong connections are even more clustered than the weak ones. Therefore, the local cortical network structure can be viewed as a skeleton of stronger connections in a sea of weaker ones. Such a skeleton is likely to play an important role in network dynamics and should be investigated further.

}, keywords = {Animals, Cell Communication, Neural Pathways, Neuronal Plasticity, Neurons, Rats, Signal Transduction, Synapses, Visual Cortex}, issn = {1545-7885}, doi = {10.1016/j.tins.2005.05.006}, author = {Song, Sen and Sj{\"o}str{\"o}m, Per Jesper and Reigl, Markus and Nelson, Sacha and Chklovskii, Dmitri B} } @article {1249, title = {Neurogeometry and potential synaptic connectivity.}, journal = {Trends in Neurosciences}, volume = {28}, year = {2005}, month = {2005 Jul}, pages = {387-94}, abstract = {

The advent of high-quality 3D reconstructions of neuronal arbors has revived the hope of inferring synaptic connectivity from the geometric shapes of axons and dendrites, or {\textquoteright}neurogeometry{\textquoteright}. A quantitative description of connectivity must be built on a sound theoretical framework. Here, we review recent developments in neurogeometry that can provide such a framework. We base the geometric description of connectivity on the concept of a {\textquoteright}potential synapse{\textquoteright}{\textendash}the close apposition between axons and dendrites necessary to form an actual synapse. In addition to describing potential synaptic connectivity in neuronal circuits, neurogeometry provides insight into basic features of functional connectivity, such as specificity and plasticity.

}, keywords = {Animals, Axons, Dendrites, Imaging, Models, Neural Pathways, Neurological, Neuronal Plasticity, Neurons, Synapses, Synaptic Transmission, Three-Dimensional}, issn = {0166-2236}, doi = {10.1016/j.tins.2005.05.006}, author = {Stepanyants, Armen and Chklovskii, Dmitri B} } @article {1240, title = {Segregation of the brain into gray and white matter: a design minimizing conduction delays.}, journal = {PLoS Computational Biology}, volume = {1}, year = {2005}, month = {2005 Dec}, pages = {e78}, abstract = {

A ubiquitous feature of the vertebrate anatomy is the segregation of the brain into white and gray matter. Assuming that evolution maximized brain functionality, what is the reason for such segregation? To answer this question, we posit that brain functionality requires high interconnectivity and short conduction delays. Based on this assumption we searched for the optimal brain architecture by comparing different candidate designs. We found that the optimal design depends on the number of neurons, interneuronal connectivity, and axon diameter. In particular, the requirement to connect neurons with many fast axons drives the segregation of the brain into white and gray matter. These results provide a possible explanation for the structure of various regions of the vertebrate brain, such as the mammalian neocortex and neostriatum, the avian telencephalon, and the spinal cord.

}, keywords = {Animals, Brain, Color, Models, Neural Pathways, Neurological}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1001066}, author = {Wen, Quan and Chklovskii, Dmitri B} } @article {1262, title = {Biophysical constraints on neuronal branching.}, journal = {Neurocomputing}, volume = {58-60}, year = {2004}, month = {2004 Jun}, pages = {487-95}, abstract = {

We investigate rules that govern neuronal arborization, speci\%cally the local geometry of the

bifurcation of a neurite into its sub-branches. In the present study we set out to determine

the relationship between branch diameter and angle. Existing theories are based on minimizing a

neuronal volume cost function, or, alternatively, on the equilibrium of mechanical tension forces,

whichdepend on branchdiameters. Our experimental results utilizing two-dimensional cultured

neural networks partly corroborate both the volume optimization principles and the tension theory.

Deviation from pure tension forces equilibrium is explained by an additional force exerted by

the anchoring of the junction to the substrate.

}, author = {Shefi, Orit and Harel, Amir and Chklovskii, Dmitri B and Ben-Jacob, Eshel and Ayali, Amir} } @article {1255, title = {Class-specific features of neuronal wiring.}, journal = {Neuron}, volume = {43}, year = {2004}, month = {2004 Jul 22}, pages = {251-9}, abstract = {

Brain function relies on specificity of synaptic connectivity patterns among different classes of neurons. Yet, the substrates of specificity in complex neuropil remain largely unknown. We search for imprints of specificity in the layout of axonal and dendritic arbors from the rat neocortex. An analysis of 3D reconstructions of pairs consisting of pyramidal cells (PCs) and GABAergic interneurons (GIs) revealed that the layout of GI axons is specific. This specificity is manifested in a relatively high tortuosity, small branch length of these axons, and correlations of their trajectories with the positions of postsynaptic neuron dendrites. Axons of PCs show no such specificity, usually taking a relatively straight course through neuropil. However, wiring patterns among PCs hold a large potential for circuit remodeling and specificity through growth and retraction of dendritic spines. Our results define distinct class-specific rules in establishing synaptic connectivity, which could be crucial in formulating a canonical cortical circuit.

}, keywords = {Animals, Axons, Computer-Assisted, Dendrites, gamma-Aminobutyric Acid, Image Processing, Imaging, Interneurons, Neocortex, Neural Pathways, Neurons, Pyramidal Cells, Rats, Synapses, Three-Dimensional, Wistar}, issn = {0896-6273}, doi = {10.1016/j.tins.2005.05.006}, author = {Stepanyants, Armen and Tam{\'a}s, G{\'a}bor and Chklovskii, Dmitri B} } @article {1264, title = {Cortical rewiring and information storage.}, journal = {Nature}, volume = {431}, year = {2004}, month = {2004 Oct 14}, pages = {782-8}, abstract = {

Current thinking about long-term memory in the cortex is focused on changes in the strengths of connections between neurons. But ongoing structural plasticity in the adult brain, including synapse formation/elimination and remodelling of axons and dendrites, suggests that memory could also depend on learning-induced changes in the cortical {\textquoteright}wiring diagram{\textquoteright}. Given that the cortex is sparsely connected, wiring plasticity could provide a substantial boost in storage capacity, although at a cost of more elaborate biological machinery and slower learning.

}, issn = {1476-4687}, author = {Chklovskii, Dmitri B and Mel, B.W. and Svoboda, Karel} } @article {1253, title = {Exact solution for the optimal neuronal layout problem.}, journal = {Neural Computation}, volume = {16}, year = {2004}, month = {2004 Oct}, pages = {2067-78}, abstract = {

Evolution perfected brain design by maximizing its functionality while minimizing costs associated with building and maintaining it. Assumption that brain functionality is specified by neuronal connectivity, implemented by costly biological wiring, leads to the following optimal design problem. For a given neuronal connectivity, find a spatial layout of neurons that minimizes the wiring cost. Unfortunately, this problem is difficult to solve because the number of possible layouts is often astronomically large. We argue that the wiring cost may scale as wire length squared, reducing the optimal layout problem to a constrained minimization of a quadratic form. For biologically plausible constraints, this problem has exact analytical solutions, which give reasonable approximations to actual layouts in the brain. These solutions make the inverse problem of inferring neuronal connectivity from neuronal layout more tractable.

}, keywords = {Animals, Brain, Computer Simulation, Humans, Models, Nerve Net, Neurological, Neurons}, issn = {0899-7667}, doi = {10.1016/j.tins.2005.05.006}, author = {Chklovskii, Dmitri B} } @article {1256, title = {Maps in the brain: what can we learn from them?}, journal = {Annual Review of Neuroscience}, volume = {27}, year = {2004}, month = {2004}, pages = {369-92}, abstract = {

In mammalian visual cortex, neurons are organized according to their functional properties into multiple maps such as retinotopic, ocular dominance, orientation preference, direction of motion, and others. What determines the organization of cortical maps? We argue that cortical maps reflect neuronal connectivity in intracortical circuits. Because connecting distant neurons requires costly wiring (i.e., axons and dendrites), there is an evolutionary pressure to place connected neurons as close to each other as possible. Then, cortical maps may be viewed as solutions that minimize wiring cost for given intracortical connectivity. These solutions can help us in inferring intracortical connectivity and, ultimately, in understanding the function of the visual system.

}, keywords = {Animals, Axons, Brain Mapping, Dendrites, Humans, Nerve Net, Neural Pathways, Visual Cortex, Visual Fields, Visual Pathways}, issn = {0147-006X}, doi = {10.1016/j.tins.2005.05.006}, author = {Chklovskii, Dmitri B and Koulakov, Alexei A} } @article {1252, title = {Search for computational modules in the C. elegans brain.}, journal = {BMC Biology}, volume = {2}, year = {2004}, month = {2004 Dec 2}, pages = {25}, abstract = {

Does the C. elegans nervous system contain multi-neuron computational modules that perform stereotypical functions? We attempt to answer this question by searching for recurring multi-neuron inter-connectivity patterns in the C. elegans nervous system{\textquoteright}s wiring diagram.

}, keywords = {Animals, Brain, Caenorhabditis elegans, Nerve Net, Neural Networks (Computer), Neurons, Synapses, Systems Biology}, issn = {1741-7007}, doi = {10.1016/j.tins.2005.05.006}, author = {Reigl, Markus and Alon, Uri and Chklovskii, Dmitri B} } @article {1254, title = {Synaptic connectivity and neuronal morphology: two sides of the same coin.}, journal = {Neuron}, volume = {43}, year = {2004}, month = {2004 Sep 2}, pages = {609-17}, abstract = {

Neurons often possess elaborate axonal and dendritic arbors. Why do these arbors exist and what determines their form and dimensions? To answer these questions, I consider the wiring up of a large highly interconnected neuronal network, such as the cortical column. Implementation of such a network in the allotted volume requires all the salient features of neuronal morphology: the existence of branching dendrites and axons and the presence of dendritic spines. Therefore, the requirement of high interconnectivity is, in itself, sufficient to account for the existence of these features. Moreover, the actual lengths of axons and dendrites are close to the smallest possible length for a given interconnectivity, arguing that high interconnectivity is essential for cortical function.

}, keywords = {Animals, Cell Communication, Cell Differentiation, Cell Size, Cerebral Cortex, Dendrites, Humans, Models, Nerve Net, Neural Pathways, Neurological, Synapses}, issn = {0896-6273}, doi = {10.1016/j.tins.2005.05.006}, author = {Chklovskii, Dmitri B} } @article {1257, title = {Power-law for axon diameters at branch point.}, journal = {BMC Neuroscience}, volume = {4}, year = {2003}, month = {2003 Aug 28}, pages = {18}, abstract = {

Axon calibers vary widely among different animals, neuron classes, and even within the same neuron. What determines the diameter of axon branches?

}, keywords = {Animals, Axons, Biological Evolution, Humans, Models, Neural Conduction, Neurological, Predictive Value of Tests, Reaction Time}, issn = {1471-2202}, doi = {10.1016/j.tins.2005.05.006}, author = {Chklovskii, Dmitri B and Stepanyants, Armen} } @article {1259, title = {Geometry and structural plasticity of synaptic connectivity.}, journal = {Neuron}, volume = {34}, year = {2002}, month = {2002 Apr 11}, pages = {275-88}, abstract = {

Changes in synaptic connectivity patterns through the formation and elimination of dendritic spines may contribute to structural plasticity in the brain. We characterize this contribution quantitatively by estimating the number of different synaptic connectivity patterns attainable without major arbor remodeling. This number depends on the ratio of the synapses on a dendrite to the axons that pass within a spine length of that dendrite. We call this ratio the filling fraction and calculate it from geometrical analysis and anatomical data. The filling fraction is 0.26 in mouse neocortex, 0.22-0.34 in rat hippocampus. In the macaque visual cortex, the filling fraction increases by a factor of 1.6-1.8 from area V1 to areas V2, V4, and 7a. Since the filling fraction is much smaller than 1, spine remodeling can make a large contribution to structural plasticity.

}, keywords = {Animals, Macaca fascicularis, Male, Models, Neural Pathways, Neurological, Neuronal Plasticity, Synapses}, issn = {0896-6273}, doi = {10.1016/j.tins.2005.05.006}, author = {Stepanyants, Armen and Hof, Patrick R and Chklovskii, Dmitri B} } @article {1266, title = {Theoretical neuroscience: state of the art.}, journal = {Neuron}, volume = {34}, year = {2002}, month = {2002 Jun 13}, pages = {863-4}, author = {Chklovskii, Dmitri B} } @article {1258, title = {Wiring optimization in cortical circuits.}, journal = {Neuron}, volume = {34}, year = {2002}, month = {2002 Apr 25}, pages = {341-7}, abstract = {

Wiring a brain presents a formidable problem because neural circuits require an enormous number of fast and durable connections. We propose that evolution was likely to have optimized neural circuits to minimize conduction delays in axons, passive cable attenuation in dendrites, and the length of "wire" used to construct circuits, and to have maximized the density of synapses. Here we ask the question: "What fraction of the volume should be taken up by axons and dendrites (i.e., wire) when these variables are at their optimal values?" The biophysical properties of axons and dendrites dictate that wire should occupy 3/5 of the volume in an optimally wired gray matter. We have measured the fraction of the volume occupied by each cellular component and find that the volume of wire is close to the predicted optimal value.

}, keywords = {Animals, Axons, Cerebral Cortex, Hippocampus, Mathematics, Mice, Nerve Net, Neurons}, issn = {0896-6273}, doi = {10.1016/j.tins.2005.05.006}, author = {Chklovskii, Dmitri B and Schikorski, Thomas and Stevens, Charles F} } @article {1267, title = {Orientation preference maps in mammalian visual cortex: A wire length minimization approach.}, journal = {Neuron}, volume = {29}, year = {2001}, month = {2001 Feb}, pages = {519-27}, abstract = {

In the visual cortex of many mammals, orientation preference changes smoothly along the cortical surface, with the exception of singularities such as pinwheels and fractures. The reason for the existence of these singularities has remained elusive, suggesting that they are developmental artifacts. We show that singularities reduce the length of intracortical neuronal connections for some connection rules. Therefore, pinwheels and fractures could be evolutionary adaptations keeping cortical volume to a minimum. Wire length minimization approach suggests that interspecies differences in orientation preference maps reflect differences in intracortical neuronal circuits, thus leading to experimentally testable predictions. We discuss application of our model to direction preference maps.

}, issn = {0896-6273}, author = {Koulakov, Alexei A and Chklovskii, Dmitri B} } @article {1269, title = {Binocular disparity can explain the orientation of ocular dominance stripes in primate primary visual area (V1).}, journal = {Vision Research}, volume = {40}, year = {2000}, month = {2000 Jun 16}, pages = {1765-73}, abstract = {

In the primate primary visual area (V1), the ocular dominance pattern consists of alternating monocular stripes. Stripe orientation follows systematic trends preserved across several species. I propose that these trends result from minimizing the length of intra-cortical wiring needed to recombine information from the two eyes in order to achieve the perception of depth. I argue that the stripe orientation at any point of V1 should follow the direction of binocular disparity in the corresponding point of the visual field. The optimal pattern of stripes determined from this argument agrees with the ocular dominance pattern of macaque and Cebus monkeys. This theory predicts that for any point in the visual field the limits of depth perception are greatest in the direction along the ocular dominance stripes at that point.

}, issn = {0042-6989}, author = {Chklovskii, Dmitri B} } @article {1268, title = {Optimal sizes of dendritic and axonal arbors in a topographic projection.}, journal = {Journal of Neurophysiology}, volume = {83}, year = {2000}, month = {2000}, pages = {2113-19}, chapter = {2113}, abstract = {

I consider a topographic projection between two neuronal layers with different densities of neurons. Given the number of output neurons connected to each input neuron (divergence) and the number of input neurons synapsing on each output neuron (convergence), I determine the widths of axonal and dendritic arbors which minimize the total volume of axons and dendrites. Analytical results for one-dimensional and two-dimensional projections can be summarized qualitatively in the following rule: neurons of the sparser layer should have arbors wider than those of the denser layer. This agrees with the anatomic data for retinal, cerebellar, olfactory bulb, and neocortical neurons the morphology and connectivity of which are known. The rule may be used to infer connectivity of neurons from their morphology.

}, issn = {0022-3077}, author = {Chklovskii, Dmitri B} } @article {1270, title = {A wire length minimization approach to ocular dominance patterns in mammalian visual cortex.}, journal = {Physica A}, volume = {284}, year = {2000}, month = {2000}, pages = {318-34}, author = {Chklovskii, Dmitri B and Koulakov, Alexei A} }