Discovering structure in multi-neuron recordings through network modelling

Our brains contain billions of neurons, which are continually producing electrical signals to relay information around the brain. Yet most of our knowledge of how the brain works comes from studying the activity of one neuron at a time. Recently, studies of multiple neurons have shown that they tend to be active together. These coordinated dynamics vary across brain states and impact the way that external sensory information is processed. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. We found that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the reliability of sensory representations. We next recorded from awake mice using calcium imaging techniques, and acquired activity from 10,000 neurons simultaneously in visual cortex while presenting 2,800 different natural images. In awake mice, these intrinsic population-wide fluctuations were suppressed and responses to visual stimuli were reliable. The stimulus-related information was stored in a high-dimensional neural space: 1,000 dimensions of neural activity accounted for 90\% of the variance. Although awake mice lacked large population-wide fluctuations in activity, we observed several dozen dimensions of spontaneous activity. These dimensions of spontaneous activity were not spatially organized in cortex. Instead they were related to the orofacial behaviors of the mouse: over 50\% of the shared variability of the network could be predicted from the facial movements of the mouse. In simulations of high-dimensional network activity, flexible patterns of activity were reproduced only if the network contained multiple dimensions of inhibitory activity. We tested this hypothesis in our recordings and found that inhibitory neuron activity did track excitatory neuron activity across multiple dimensions

Simultaneous computation of dynamical and equilibrium information using a weighted ensemble of trajectories

Equilibrium formally can be represented as an ensemble of uncoupled systems undergoing unbiased dynamics in which detailed balance is maintained. Many non-equilibrium processes can be described by suitable subsets of the equilibrium ensemble. Here, we employ the "weighted ensemble" (WE) simulation protocol [Huber and Kim, Biophys. J., 1996] to generate equilibrium trajectory ensembles and extract non-equilibrium subsets for computing kinetic quantities. States do not need to be chosen in advance. The procedure formally allows estimation of kinetic rates between arbitrary states chosen after the simulation, along with their equilibrium populations. We also describe a related history-dependent matrix procedure for estimating equilibrium and non-equilibrium observables when phase space has been divided into arbitrary non-Markovian regions, whether in WE or ordinary simulation. In this proof-of-principle study, these methods are successfully applied and validated on two molecular systems: explicitly solvated methane association and the implicitly solvated Ala4 peptide. We comment on challenges remaining in WE calculations

Neuromatch Academy: Teaching Computational Neuroscience with global accessibility

Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility.Comment: 10 pages, 3 figures. Equal contribution by the executive committee members of Neuromatch Academy: Tara van Viegen, Athena Akrami, Kate Bonnen, Eric DeWitt, Alexandre Hyafil, Helena Ledmyr, Grace W. Lindsay, Patrick Mineault, John D. Murray, Xaq Pitkow, Aina Puce, Madineh Sedigh-Sarvestani, Carsen Stringer. and equal contribution by the board of directors of Neuromatch Academy: Gunnar Blohm, Konrad Kording, Paul Schrater, Brad Wyble, Sean Escola, Megan A. K. Peter

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Recordings of 10k neurons in V1 during drifting gratings

This data accompanies the following paper:<div><br></div><div><div>Robustness of spike deconvolution for calcium imaging of neural spiking</div><div>Marius Pachitariu, Carsen Stringer, Kenneth D. Harris</div><div>bioRxiv 156786; doi: https://doi.org/10.1101/156786</div></div><div><br></div><div>where it is used to assess the performance of spike deconvolution algorithms. The code repository to reproduce the analyses in the paper is uploaded at </div><div><br></div><div>https://github.com/MouseLand/pachitariu-et-al-2018a<br></div><div><br></div><div>We recorded ~10,000 neurons simultaneously at 2.5Hz using calcium imaging. Visual stimuli were shown at approximately 1 Hz, with randomized inter-stimulus intervals. The stimuli were drifting gratings with 8 directions, 4 spatial frequencies and 3 temporal frequencies. Blank stimuli (gray screen) were also interleaved. </div

Recordings of 10,000 neurons in visual cortex in response to 2,800 natural images

This data release contains simultaneous recordings of ten thousand neurons in response to 2,800 natural images and other stimulus sets. These data were used in <div><br></div><div>Stringer, Pachitariu et al 2018b, High-dimensional geometry of population responses in visual cortex, <i>bioRxiv</i>. </div><div><br></div><div>The code to make the figures in the paper is available at</div><div><br><div>https://github.com/MouseLand/stringer-pachitariu-et-al-2018b<div><br></div><div>We encourage data users to fork this repository, or create their own repository inside MouseLand, where we will also be adding our future data and analyses. "Watching" the repository might be a good idea, since any new information about the data, analyses of the data, or publications using it, will appear there. <br><br></div><div>If you use these data in a paper, please cite the original research paper, as well as this dataset using the figshare doi.</div></div></div