@article {69774, title = {Neuropixels Opto: Combining high-resolution electrophysiology and optogenetics}, journal = {bioRxiv}, year = {2025}, month = {2025 Feb 06}, abstract = {

High-resolution extracellular electrophysiology is the gold standard for recording spikes from distributed neural populations, and is especially powerful when combined with optogenetics for manipulation of specific cell types with high temporal resolution. We integrated these approaches into prototype Neuropixels Opto probes, which combine electronic and photonic circuits. These devices pack 960 electrical recording sites and two sets of 14 light emitters onto a 1 cm shank, allowing spatially addressable optogenetic stimulation with blue and red light. In mouse cortex, Neuropixels Opto probes delivered high-quality recordings together with spatially addressable optogenetics, differentially activating or silencing neurons at distinct cortical depths. In mouse striatum and other deep structures, Neuropixels Opto probes delivered efficient optotagging, facilitating the identification of two cell types in parallel. Neuropixels Opto probes represent an unprecedented tool for recording, identifying, and manipulating neuronal populations.

}, doi = {10.1101/2025.02.04.636286}, url = {https://www.biorxiv.org/content/early/2025/02/06/2025.02.04.636286}, author = {Lakunina, Anna and Socha, Karolina Z and Ladd, Alexander and Bowen, Anna J and Chen, Susu and Colonell, Jennifer and Doshi, Anjal and Karsh, Bill and Krumin, Michael and Kulik, Pavel and Li, Anna and Neutens, Pieter and O{\textquoteright}Callaghan, John and Olsen, Meghan and Putzeys, Jan and Tilmans, Harrie AC and Ye, Zhiwen and Welkenhuysen, Marleen and H{\"a}usser, Michael and Koch, Christof and Ting, Jonathan T. and Neuropixels Opto Consortium and Dutta, Barun and Harris, Timothy D and Steinmetz, Nicholas A and Svoboda, Karel and Siegle, Joshua H and Carandini, Matteo} } @article {70133, title = {Ultra-high-density Neuropixels probes improve detection and identification in neuronal recordings.}, journal = {Neuron}, year = {2025}, month = {2025 Sep 30}, abstract = {

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single-neuron and single-spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). Using NP Ultra, neuronal yield in mouse visual cortex recordings increased by more than 2-fold. With ultra-high spatial resolution, we discovered that a feature of extracellular waveforms, the spatial extent or "footprint," distinguished axonal from somatic recordings. In addition, three genetically identified cortical cell types could be discriminated from one another with \~{}80\% accuracy and from other neurons with \~{}85\% accuracy. NP Ultra improves yield, detection of subcellular compartments, and cell type identification to enable a more powerful dissection of neural circuit activity during behavior.

bioRxiv preprint: doi.org/10.1101/2023.08.23.554527

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2025.08.030}, url = {https://doi.org/10.1016/j.neuron.2025.08.030}, author = {Ye, Zhiwen and Shelton, Andrew M and Shaker, Jordan R and Boussard, Julien and Colonell, Jennifer and Birman, Daniel and Manavi, Sahar and Chen, Susu and Windolf, Charlie and Hurwitz, Cole and Yu, Han and Namima, Tomoyuki and Pedraja, Federico and Weiss, Shahaf and Raducanu, Bogdan C and Ness, Torbj{\o}rn V and Jia, Xiaoxuan and Mastroberardino, Giulia and Rossi, L Federico and Carandini, Matteo and H{\"a}usser, Michael and Einevoll, Gaute T and Laurent, Gilles and Sawtell, Nathaniel B and Bair, Wyeth and Pasupathy, Anitha and Mora Lopez, Carolina and Dutta, Barundeb and Paninski, Liam and Siegle, Joshua H and Koch, Christof and Olsen, Shawn R and Harris, Timothy D and Steinmetz, Nicholas A} } @article {68709, title = {BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Jun 01}, abstract = {

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

}, issn = {1548-7105}, doi = {10.1038/s41592-023-01848-5}, author = {Manubens-Gil, Linus and Zhou, Zhi and Chen, Hanbo and Ramanathan, Arvind and Liu, Xiaoxiao and Liu, Yufeng and Bria, Alessandro and Gillette, Todd and Ruan, Zongcai and Yang, Jian and Radojevi{\'c}, Miroslav and Zhao, Ting and Cheng, Li and Qu, Lei and Liu, Siqi and Bouchard, Kristofer E and Gu, Lin and Cai, Weidong and Ji, Shuiwang and Roysam, Badrinath and Wang, Ching-Wei and Yu, Hongchuan and Sironi, Amos and Iascone, Daniel Maxim and Zhou, Jie and Bas, Erhan and Conde-Sousa, Eduardo and Aguiar, Paulo and Li, Xiang and Li, Yujie and Nanda, Sumit and Wang, Yuan and Muresan, Leila and Fua, Pascal and Ye, Bing and He, Hai-Yan and Staiger, Jochen F and Peter, Manuel and Cox, Daniel N and Simonneau, Michel and Oberlaender, Marcel and Jefferis, Gregory and Ito, Kei and Gonzalez-Bellido, Paloma and Kim, Jinhyun and Rubel, Edwin and Cline, Hollis T and Zeng, Hongkui and Nern, Aljoscha and Chiang, Ann-Shyn and Yao, Jianhua and Roskams, Jane and Livesey, Rick and Stevens, Janine and Liu, Tianming and Dang, Chinh and Guo, Yike and Zhong, Ning and Tourassi, Georgia and Hill, Sean and Hawrylycz, Michael and Koch, Christof and Meijering, Erik and Ascoli, Giorgio A and Peng, Hanchuan} } @article {68968, title = {Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings.}, journal = {bioRxiv}, year = {2023}, month = {2023 Aug 25}, abstract = {

To study the neural basis of behavior, we require methods to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology is a principal method for achieving this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To overcome these limitations, we developed a silicon probe with significantly smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device measures neuronal activity at ultra-high densities (\>1300 sites per mm, 10 times higher than previous probes), with 6 {\textmu}m center-to-center spacing and low noise. This device effectively comprises an implantable voltage-sensing camera that captures a planar image of a neuron{\textquoteright}s electrical field. We introduce a new spike sorting algorithm optimized for these probes and use it to find that the yield of visually-responsive neurons in recordings from mouse visual cortex improves \~{}3-fold. Recordings across multiple brain regions and four species revealed a subset of unexpectedly small extracellular action potentials not previously reported. Further experiments determined that, in visual cortex, these do not correspond to major subclasses of interneurons and instead likely reflect recordings from axons. Finally, using ground-truth identification of cortical inhibitory cell types with optotagging, we found that cell type was discriminable with approximately 75\% success among three types, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, sampling bias, and cell type classification.

}, doi = {10.1101/2023.08.23.554527}, author = {Ye, Zhiwen and Shelton, Andrew M and Shaker, Jordan R and Boussard, Julien and Colonell, Jennifer and Manavi, Sahar and Chen, Susu and Windolf, Charlie and Hurwitz, Cole and Namima, Tomoyuki and Pedraja, Federico and Weiss, Shahaf and Raducanu, Bogdan and Ness, Torbj{\o}rn V and Einevoll, Gaute T and Laurent, Gilles and Sawtell, Nathaniel B and Bair, Wyeth and Pasupathy, Anitha and Mora Lopez, Carolina and Dutta, Barun and Paninski, Liam and Siegle, Joshua H and Koch, Christof and Olsen, Shawn R and Harris, Timothy D and Steinmetz, Nicholas A} } @article {66008, title = {Survey of spiking in the mouse visual system reveals functional hierarchy.}, journal = {Nature}, volume = {592(7852):86-92}, year = {2021}, month = {2021 Jan 20}, pages = {86-92}, abstract = {

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.

}, issn = {1476-4687}, doi = {10.1038/s41586-020-03171-x}, author = {Siegle, Joshua H and Jia, Xiaoxuan and Durand, Severine and Gale, Sam and Bennett, Corbett and Graddis, Nile and Heller, Greggory and Ramirez, Tamina K and Choi, Hannah and Luviano, Jennifer A and Groblewski, Peter A and Ahmed, Ruweida and Arkhipov, Anton and Bernard, Amy and Billeh, Yazan N and Brown, Dillan and Buice, Michael A and Cain, Nicolas and Caldejon, Shiella and Casal, Linzy and Cho, Andrew and Chvilicek, Maggie and Cox, Timothy C and Dai, Kael and Denman, Daniel J and de Vries, Saskia E J and Dietzman, Roald and Esposito, Luke and Farrell, Colin and Feng, David and Galbraith, John and Garrett, Marina and Gelfand, Emily C and Hancock, Nicole and Harris, Julie A and Howard, Robert and Hu, Brian and Hytnen, Ross and Iyer, Ramakrishnan and Jessett, Erika and Johnson, Katelyn and Kato, India and Kiggins, Justin and Lambert, Sophie and Lecoq, Jerome and Ledochowitsch, Peter and Lee, Jung Hoon and Leon, Arielle and Li, Yang and Liang, Elizabeth and Long, Fuhui and Mace, Kyla and Melchior, Jose and Millman, Daniel and Mollenkopf, Tyler and Nayan, Chelsea and Ng, Lydia and Ngo, Kiet and Nguyen, Thuyahn and Nicovich, Philip R and North, Kat and Ocker, Gabriel Koch and Ollerenshaw, Doug and Oliver, Michael and Pachitariu, Marius and Perkins, Jed and Reding, Melissa and Reid, David and Robertson, Miranda and Ronellenfitch, Kara and Seid, Sam and Slaughterbeck, Cliff and Stoecklin, Michelle and Sullivan, David and Sutton, Ben and Swapp, Jackie and Thompson, Carol and Turner, Kristen and Wakeman, Wayne and Whitesell, Jennifer D and Williams, Derric and Williford, Ali and Young, Rob and Zeng, Hongkui and Naylor, Sarah and Phillips, John W and Reid, R Clay and Mihalas, Stefan and Olsen, Shawn R and Koch, Christof} } @article {49495, title = {Shared and distinct transcriptomic cell types across neocortical areas.}, journal = {Nature}, volume = {563}, year = {2018}, month = {2018 Nov}, pages = {72-78}, abstract = {

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

}, issn = {1476-4687}, doi = {10.1038/s41586-018-0654-5}, author = {Tasic, Bosiljka and Yao, Zizhen and Graybuck, Lucas T and Smith, Kimberly A and Nguyen, Thuc Nghi and Bertagnolli, Darren and Goldy, Jeff and Garren, Emma and Economo, Michael N and Viswanathan, Sarada and Penn, Osnat and Bakken, Trygve and Menon, Vilas and Miller, Jeremy and Fong, Olivia and Hirokawa, Karla E and Lathia, Kanan and Rimorin, Christine and Tieu, Michael and Larsen, Rachael and Casper, Tamara and Barkan, Eliza and Kroll, Matthew and Parry, Sheana and Shapovalova, Nadiya V and Hirschstein, Daniel and Pendergraft, Julie and Sullivan, Heather A and Kim, Tae Kyung and Szafer, Aaron and Dee, Nick and Groblewski, Peter and Wickersham, Ian and Cetin, Ali and Harris, Julie A and Levi, Boaz P and Sunkin, Susan M and Madisen, Linda and Daigle, Tanya L and Looger, Loren and Bernard, Amy and Phillips, John and Lein, Ed and Hawrylycz, Michael and Svoboda, Karel and Jones, Allan R and Koch, Christof and Zeng, Hongkui} } @article {49518, title = {Visual physiology of the layer 4 cortical circuit in silico.}, journal = {PLoS Computational Biology}, volume = {14}, year = {2018}, month = {2018 Nov 12}, pages = {e1006535}, abstract = {

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1006535}, author = {Arkhipov, Anton and Gouwens, Nathan W and Billeh, Yazan N and Gratiy, Sergey and Iyer, Ramakrishnan and Wei, Ziqiang and Xu, Zihao and Abbasi-Asl, Reza and Berg, Jim and Buice, Michael and Cain, Nicholas and da Costa, Nuno and de Vries, Saskia and Denman, Daniel and Durand, Severine and Feng, David and Jarsky, Tim and Lecoq, Jerome and Lee, Brian and Li, Lu and Mihalas, Stefan and Ocker, Gabriel K and Olsen, Shawn R and Reid, R Clay and Soler-Llavina, Gilberto and Sorensen, Staci A and Wang, Quanxin and Waters, Jack and Scanziani, Massimo and Koch, Christof} } @article {48935, title = {Fully integrated silicon probes for high-density recording of neural activity.}, journal = {Nature}, volume = {551}, year = {2017}, month = {2017 Nov 08}, pages = {232-236}, abstract = {

Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca(2+) imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal-oxide-semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 {\texttimes} 20-μm cross-section shank. The 6 {\texttimes} 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.

}, issn = {1476-4687}, doi = {10.1038/nature24636}, author = {Jun, James J and Steinmetz, Nicholas A and Siegle, Joshua H and Denman, Daniel J and Bauza, Marius and Barbarits, Brian and Lee, Albert K and Anastassiou, Costas A and Andrei, Alexandru and Ayd{\i}n, {\c C}a{\u g}atay and Barbic, Mladen and Blanche, Timothy J and Bonin, Vincent and Couto, Jo{\~a}o and Dutta, Barundeb and Gratiy, Sergey L and Gutnisky, Diego A and H{\"a}usser, Michael and Karsh, Bill and Ledochowitsch, Peter and Lopez, Carolina Mora and Mitelut, Catalin and Musa, Silke and Okun, Michael and Pachitariu, Marius and Putzeys, Jan and Rich, P Dylan and Rossant, Cyrille and Sun, Wei-Lung and Svoboda, Karel and Carandini, Matteo and Harris, Kenneth D and Koch, Christof and O{\textquoteright}Keefe, John and Harris, Timothy D} } @article {47894, title = {Adult mouse cortical cell taxonomy revealed by single cell transcriptomics.}, journal = {Nature neuroscience}, volume = {19}, year = {2016}, month = {2016 Feb}, pages = {335-46}, abstract = {

Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type-specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.

}, keywords = {Animals, Cell Line, Cerebral Cortex, Classification, gamma-Aminobutyric Acid, Gene Library, Genetic Markers, Glutamic Acid, Interneurons, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Neurons, RNA, Sequence Analysis, RNA, Transcriptome, Visual Cortex}, issn = {1546-1726}, doi = {10.1038/nn.4216}, author = {Tasic, Bosiljka and Menon, Vilas and Nguyen, Thuc Nghi and Kim, Tae Kyung and Jarsky, Tim and Yao, Zizhen and Levi, Boaz and Gray, Lucas T and Sorensen, Staci A and Dolbeare, Tim and Bertagnolli, Darren and Goldy, Jeff and Shapovalova, Nadiya and Parry, Sheana and Lee, Changkyu and Smith, Kimberly and Bernard, Amy and Madisen, Linda and Sunkin, Susan M and Hawrylycz, Michael and Koch, Christof and Zeng, Hongkui} } @article {47895, title = {Canonical genetic signatures of the adult human brain.}, journal = {Nature neuroscience}, volume = {18}, year = {2015}, month = {2015 Dec}, pages = {1832-44}, abstract = {

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.

}, keywords = {Adult, Animals, Brain, Gene Regulatory Networks, Humans, Mice, Nerve Net, Transcriptome}, issn = {1546-1726}, doi = {10.1038/nn.4171}, author = {Hawrylycz, Michael and Miller, Jeremy A and Menon, Vilas and Feng, David and Dolbeare, Tim and Guillozet-Bongaarts, Angela L and Jegga, Anil G and Aronow, Bruce J and Lee, Chang-Kyu and Bernard, Amy and Glasser, Matthew F and Dierker, Donna L and Menche, J{\"o}rg and Szafer, Aaron and Collman, Forrest and Grange, Pascal and Berman, Kenneth A and Mihalas, Stefan and Yao, Zizhen and Stewart, Lance and Barab{\'a}si, Albert-L{\'a}szl{\'o} and Schulkin, Jay and Phillips, John and Ng, Lydia and Dang, Chinh and Haynor, David R and Jones, Allan and Van Essen, David C and Koch, Christof and Lein, Ed} } @article {47017, title = {Optogenetics: 10 years after ChR2 in neurons-views from the community.}, journal = {Nature Neuroscience}, volume = {18}, year = {2015}, month = {2015 Aug 26}, pages = {1202-12}, issn = {1546-1726}, doi = {10.1038/nn.4106}, author = {Adamantidis, Antoine and Arber, Silvia and Bains, Jaideep S and Bamberg, Ernst and Bonci, Antonello and Buzs{\'a}ki, Gy{\"o}rgy and Cardin, Jessica A and Costa, Rui M and Dan, Yang and Goda, Yukiko and Graybiel, Ann M and H{\"a}usser, Michael and Hegemann, Peter and Huguenard, John R and Insel, Thomas R and Janak, Patricia H and Johnston, Daniel and Josselyn, Sheena A and Koch, Christof and Kreitzer, Anatol C and L{\"u}scher, Christian and Malenka, Robert C and Miesenb{\"o}ck, Gero and Nagel, Georg and Roska, Botond and Schnitzer, Mark J and Shenoy, Krishna V and Soltesz, Ivan and Sternson, Scott M and Tsien, Richard W and Tsien, Roger Y and Turrigiano, Gina G and Tye, Kay M and Wilson, Rachel I} } @article {47899, title = {The influence of synaptic weight distribution on neuronal population dynamics.}, journal = {PLoS computational biology}, volume = {9}, year = {2013}, month = {2013 Oct}, pages = {e1003248}, abstract = {

The manner in which different distributions of synaptic weights onto cortical neurons shape their spiking activity remains open. To characterize a homogeneous neuronal population, we use the master equation for generalized leaky integrate-and-fire neurons with shot-noise synapses. We develop fast semi-analytic numerical methods to solve this equation for either current or conductance synapses, with and without synaptic depression. We show that its solutions match simulations of equivalent neuronal networks better than those of the Fokker-Planck equation and we compute bounds on the network response to non-instantaneous synapses. We apply these methods to study different synaptic weight distributions in feed-forward networks. We characterize the synaptic amplitude distributions using a set of measures, called tail weight numbers, designed to quantify the preponderance of very strong synapses. Even if synaptic amplitude distributions are equated for both the total current and average synaptic weight, distributions with sparse but strong synapses produce higher responses for small inputs, leading to a larger operating range. Furthermore, despite their small number, such synapses enable the network to respond faster and with more stability in the face of external fluctuations.

}, keywords = {Action Potentials, Computer Simulation, Models, Neurological, Neurons, Synapses}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1003248}, author = {Iyer, Ramakrishnan and Menon, Vilas and Buice, Michael and Koch, Christof and Mihalas, Stefan} }