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

Sarvestani et al. DataFiles

Sarvestani et al. DataFile

Data from: Thalamocortical synapses in the cat visual system in vivo are weak and unreliable

The thalamocortical synapse of the visual system has been central to our understanding of sensory computations in the cortex. Although we have a fair understanding of the functional properties of the pre and post-synaptic populations, little is known about their synaptic properties, particularly in vivo. We used simultaneous recordings in LGN and V1 in cat in vivo to characterize the dynamic properties of thalamocortical synaptic transmission in monosynaptically connected LGN-V1 neurons. We found that thalamocortical synapses in vivo are unreliable, highly variable and exhibit short-term plasticity. Using biologically constrained models, we found that variable and unreliable synapses serve to increase cortical firing by means of increasing membrane fluctuations, similar to high conductance states. Thus, synaptic variability and unreliability, rather than acting as system noise, do serve a computational function. Our characterization of LGN-V1 synaptic properties constrains existing mathematical models, and mechanistic hypotheses, of a fundamental circuit in computational neuroscience

Reconstructing mammalian sleep dynamics with data assimilation.

Data assimilation is a valuable tool in the study of any complex system, where measurements are incomplete, uncertain, or both. It enables the user to take advantage of all available information including experimental measurements and short-term model forecasts of a system. Although data assimilation has been used to study other biological systems, the study of the sleep-wake regulatory network has yet to benefit from this toolset. We present a data assimilation framework based on the unscented Kalman filter (UKF) for combining sparse measurements together with a relatively high-dimensional nonlinear computational model to estimate the state of a model of the sleep-wake regulatory system. We demonstrate with simulation studies that a few noisy variables can be used to accurately reconstruct the remaining hidden variables. We introduce a metric for ranking relative partial observability of computational models, within the UKF framework, that allows us to choose the optimal variables for measurement and also provides a methodology for optimizing framework parameters such as UKF covariance inflation. In addition, we demonstrate a parameter estimation method that allows us to track non-stationary model parameters and accommodate slow dynamics not included in the UKF filter model. Finally, we show that we can even use observed discretized sleep-state, which is not one of the model variables, to reconstruct model state and estimate unknown parameters. Sleep is implicated in many neurological disorders from epilepsy to schizophrenia, but simultaneous observation of the many brain components that regulate this behavior is difficult. We anticipate that this data assimilation framework will enable better understanding of the detailed interactions governing sleep and wake behavior and provide for better, more targeted, therapies

Reconstruction of DB Model Dynamics (A) with default values for covariance inflater , and (B) after optimization of and .

<p>Noisy measurements of (blue) were passed to the unscented kalman filter (UKF) framework to track and reconstruct all other variables. Shown are the firing rates for the Wake-active (LC), NREM-active (VLPO), and REM-active (LDT/PPT) cell groups, along with thalamic noise . The framework was given the same parameters used to generate the original data. In both A and B the same data was tracked with model initial conditions chosen randomly. After a transient period, reconstructed (red) Wake and NREM dynamics are close to true (black) dynamics. Without optimization the dynamics of are essentially ignored. After optimization at least some of the stochastic dynamics - those that measurably affect the dynamics of - are reconstructed and reconstruction of REM dynamics is improved.</p

Parameter estimation from observed hypnogram for reconstruction of DB model from inferred measurement of , , and with unknown value for parameter .

<p>A) Convergence of the estimated parameter(magenta) to the true value (black). B) Trajectories for the short model-generated (magenta), reconstructed (red), and true (black) dynamics for different periods of the convergence of . C) Reconstruction metric computed for each data assimilation window for three of the variables. Horizontal dashed lines correspond to computed from the state-conditioned discrete map used to translate the SOV to model space. Note that once the parameter is optimized, the UKF reconstruction far outperforms the observation map.</p

Computational models of the sleep-wake regulatory system and their outputs.

<p>A) Diniz Behn and Booth (DB) Model circuit diagram, illustrating the cell groups, their output neurotransmitters, and connections. Inhibitory connections are represented by minus and excitatory connections are represented by plus signs. Locus coeruleus (LC), dorsal raphe (DR), ventrolateral preoptic nucleus (VLPO), laterodorsal/pendunculopontine tegmentum (LDT/PPT), gamma-aminobutyric acid (GABA), seretonin (5-HT), norepinephrine (NE), acetylcholine (ACh), homeostatic sleep-drive (h), non rapid-eye-movement sleep (NREM), rapid-eye-movement sleep (REM). B) Typical output of the DB model for three of the cell group firing rates, plus the scored sleep-state plotted as a hypnogram. C) Fleshner, Booth, Forger and Diniz Behn (FBFD) Model circuit diagram, which expands on the DB model to include circadian modulation by and feedback to the suprachiasmatic nucleus (SCN), and allows for diurnal variations in behavior with light periods dominated by sleep activities and dark periods by periods of extended awake activity. Dashed lines indicate SCN additions to DB model. D) 36 hour hypnogram from the FBFD model, with 24-hour periodic CIRC input input to the SCN superimposed.</p