@article {65738, title = {The mind of a mouse.}, journal = {Cell}, volume = {182}, year = {2020}, month = {2020 Sep 17}, pages = {1372-1376}, abstract = {

Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain{\textquoteright}s synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.08.010}, author = {Abbott, Larry F and Bock, Davi D and Callaway, Edward M and Denk, Winfried and Dulac, Catherine and Fairhall, Adrienne L and Fiete, Ila and Harris, Kristen M and Helmstaedter, Moritz and Jain, Viren and Kasthuri, Narayanan and LeCun, Yann and Lichtman, Jeff W and Littlewood, Peter B and Luo, Liqun and Maunsell, John H R and Reid, R Clay and Rosen, Bruce R and Rubin, Gerald M and Sejnowski, Terrence J and Seung, H Sebastian and Svoboda, Karel and Tank, David W and Tsao, Doris and Van Essen, David C} } @article {46852, title = {Epigenomic signatures of neuronal diversity in the mammalian brain.}, journal = {Neuron}, volume = {86}, year = {2015}, month = {2015 Jun 17}, pages = {1369-1384}, abstract = {

Neuronal diversity is essential for mammalian brain function but poses a challenge to molecular profiling. To address the need for tools that facilitate cell-type-specific epigenomic studies, we developed the first\ affinity purification approach to isolate nuclei from genetically defined cell types in a mammal. We\ combine this technique with next-generation sequencing to show that three subtypes of neocortical neurons have highly distinctive epigenomic landscapes. Over 200,000 regions differ in chromatin accessibility and DNA methylation signatures characteristic of gene regulatory regions. By footprinting and motif analyses, these regions are predicted to bind distinct cohorts of neuron subtype-specific transcription factors. Neuronal epigenomes reflect both past and present gene expression, with DNA hyper-methylation at developmentally critical genes appearing as a novel epigenomic signature in mature neurons. Taken together, our findings link the functional and transcriptional complexity of neurons to their underlying epigenomic diversity.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2015.05.018}, author = {Mo, Alisa and Mukamel, Eran A and Davis, Fred P and Luo, Chongyuan and Henry, Gilbert L and Picard, Serge and Urich, Mark A and Nery, Joseph R and Sejnowski, Terrence J and Lister, Ryan and Eddy, Sean R and Ecker, Joseph R and Nathans, Jeremy} } @article {6130, title = {Nanotools for neuroscience and brain activity mapping.}, journal = {ACS Nano}, volume = {7}, year = {2013}, month = {2013 Mar 26}, pages = {1850-66}, abstract = {

Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural interactions and circuits. At the same time, there exist few general theories or principles that explain brain function. We attribute this disparity, in part, to limitations in current methodologies. Traditional neurophysiological approaches record the activities of one neuron or a few neurons at a time. Neurochemical approaches focus on single neurotransmitters. Yet, there is an increasing realization that neural circuits operate at emergent levels, where the interactions between hundreds or thousands of neurons, utilizing multiple chemical transmitters, generate functional states. Brains function at the nanoscale, so tools to study brains must ultimately operate at this scale, as well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to explore brain function by enabling simultaneous measurement and manipulation of activity of thousands or even millions of neurons. We and others refer to this goal as the Brain Activity Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis tools and in the design and synthesis of nanomaterials have generated optical, electrical, and chemical methods that can readily be adapted for use in neuroscience. These approaches represent exciting areas of technical development and research. Moreover, unique opportunities exist for nanoscientists, nanotechnologists, and other physical scientists and engineers to contribute to tackling the challenging problems involved in understanding the fundamentals of brain function.

}, issn = {1936-086X}, doi = {10.1021/nn4012847}, author = {Alivisatos, A Paul and Andrews, Anne M and Boyden, Edward S and Chun, Miyoung and Church, George M and Deisseroth, Karl and Donoghue, John P and Fraser, Scott E and Lippincott-Schwartz, Jennifer and Looger, Loren L and Masmanidis, Sotiris and McEuen, Paul L and Nurmikko, Arto V and Park, Hongkun and Peterka, Darcy S and Reid, Clay and Roukes, Michael L and Scherer, Axel and Schnitzer, Mark and Sejnowski, Terrence J and Shepard, Kenneth L and Tsao, Doris and Turrigiano, Gina and Weiss, Paul S and Xu, Chris and Yuste, Rafael and Zhuang, Xiaowei} } @article {47641, title = {Nanotools for neuroscience and brain activity mapping.}, journal = {ACS nano}, volume = {7}, year = {2013}, month = {2013 Mar 26}, pages = {1850-66}, abstract = {

Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural interactions and circuits. At the same time, there exist few general theories or principles that explain brain function. We attribute this disparity, in part, to limitations in current methodologies. Traditional neurophysiological approaches record the activities of one neuron or a few neurons at a time. Neurochemical approaches focus on single neurotransmitters. Yet, there is an increasing realization that neural circuits operate at emergent levels, where the interactions between hundreds or thousands of neurons, utilizing multiple chemical transmitters, generate functional states. Brains function at the nanoscale, so tools to study brains must ultimately operate at this scale, as well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to explore brain function by enabling simultaneous measurement and manipulation of activity of thousands or even millions of neurons. We and others refer to this goal as the Brain Activity Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis tools and in the design and synthesis of nanomaterials have generated optical, electrical, and chemical methods that can readily be adapted for use in neuroscience. These approaches represent exciting areas of technical development and research. Moreover, unique opportunities exist for nanoscientists, nanotechnologists, and other physical scientists and engineers to contribute to tackling the challenging problems involved in understanding the fundamentals of brain function.

}, keywords = {Animals, Brain Mapping, Humans, Models, Neurological, Nanomedicine, Nanoparticles, Nanotechnology, Nervous System Physiological Phenomena}, issn = {1936-086X}, doi = {10.1021/nn4012847}, author = {Alivisatos, A Paul and Andrews, Anne M and Boyden, Edward S and Chun, Miyoung and Church, George M and Deisseroth, Karl and Donoghue, John P and Fraser, Scott E and Lippincott-Schwartz, Jennifer and Looger, Loren L and Masmanidis, Sotiris and McEuen, Paul L and Nurmikko, Arto V and Park, Hongkun and Peterka, Darcy S and Reid, Clay and Roukes, Michael L and Scherer, Axel and Schnitzer, Mark and Sejnowski, Terrence J and Shepard, Kenneth L and Tsao, Doris and Turrigiano, Gina and Weiss, Paul S and Xu, Chris and Yuste, Rafael and Zhuang, Xiaowei} } @article {46300, title = {Intracellular dynamics of virtual place cells.}, journal = {Neural Computation}, volume = {23}, year = {2011}, month = {2011 Mar}, pages = {651-5}, abstract = {

The pattern of spikes recorded from place cells in the rodent hippocampus is strongly modulated by both the spatial location in the environment and the theta rhythm. The phases of the spikes in the theta cycle advance during movement through the place field. Recently intracellular recordings from hippocampal neurons (Harvey, Collman, Dombeck, \& Tank, 2009 ) showed an increase in the amplitude of membrane potential oscillations inside the place field, which was interpreted as evidence that an intracellular mechanism caused phase precession. Here we show that an existing network model of the hippocampus (Tsodyks, Skaggs, Sejnowski, \& McNaughton, 1996 ) can equally reproduce this and other aspects of the intracellular recordings, which suggests that new experiments are needed to distinguish the contributions of intracellular and network mechanisms to phase precession.

}, issn = {1530-888X}, doi = {10.1162/NECO_a_00087}, author = {Romani, Sandro and Sejnowski, Terrence J and Tsodyks, Misha} } @article {2012, title = {Fast monte carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces.}, journal = {SIAM Journal on Scientific Computing: A Publication of the Society for Industrial and Applied Mathematics}, volume = {30}, year = {2008}, month = {2008 Oct 13}, pages = {3126}, abstract = {

Many important physiological processes operate at time and space scales far beyond those accessible to atom-realistic simulations, and yet discrete stochastic rather than continuum methods may best represent finite numbers of molecules interacting in complex cellular spaces. We describe and validate new tools and algorithms developed for a new version of the MCell simulation program (MCell3), which supports generalized Monte Carlo modeling of diffusion and chemical reaction in solution, on surfaces representing membranes, and combinations thereof. A new syntax for describing the spatial directionality of surface reactions is introduced, along with optimizations and algorithms that can substantially reduce computational costs (e.g., event scheduling, variable time and space steps). Examples for simple reactions in simple spaces are validated by comparison to analytic solutions. Thus we show how spatially realistic Monte Carlo simulations of biological systems can be far more cost-effective than often is assumed, and provide a level of accuracy and insight beyond that of continuum methods.

}, issn = {1095-7197}, doi = {10.1137/070692017}, author = {Kerr, Rex A and Bartol, Thomas M and Kaminsky, Boris and Dittrich, Markus and Chang, Jen-Chien Jack and Baden, Scott B and Sejnowski, Terrence J and Stiles, Joel R} } @article {2013, title = {Division accuracy in a stochastic model of Min oscillations in Escherichia coli.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {103}, year = {2006}, month = {2006 Jan 10}, pages = {347-52}, abstract = {

Accurate cell division in Escherichia coli requires the Min proteins MinC, MinD, and MinE as well as the presence of nucleoids. MinD and MinE exhibit spatial oscillations, moving from pole to pole of the bacterium, resulting in an average MinD concentration that is low at the center of the cell and high at the poles. This concentration minimum is thought to signal the site of cell division. Deterministic models of the Min oscillations reproduce many observed features of the system, including the concentration minimum of MinD. However, there are only a few thousand Min proteins in a bacterium, so stochastic effects are likely to play an important role. Here, we show that Monte Carlo simulations with a large number of proteins agree well with the results from a deterministic treatment of the equations. The location of minimum local MinD concentration is too variable to account for cell division accuracy in wild type but is consistent with the accuracy of cell division in cells without nucleoids. This finding confirms the need to include additional mechanisms, such as reciprocal interactions with the cell division ring or positioning of the nucleoids, to explain wild-type accuracy.

}, keywords = {Adenosine Triphosphatases, Biological, Biophysical Phenomena, Biophysics, Cell Cycle Proteins, Cell Division, Escherichia coli, Escherichia coli Proteins, Models, Monte Carlo Method, Stochastic Processes}, issn = {0027-8424}, doi = {10.1073/pnas.0505825102}, author = {Kerr, Rex A and Levine, Herbert and Sejnowski, Terrence J and Rappel, Wouter-Jan} }