Multiscale Object Oriented Simulation Environment

Publications

• 01
  Somashekar, B. P., & Bhalla, U. S. (2025). Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks. eLife, 13, RP100664.
• 02
  Bhalla, U. S. (2021). HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks. PLoS Computational Biology, 17(11), e1009621.
• 03
  Bhatia, A., Moza, S., & Bhalla, U. S. (2019). Precise excitation-inhibition balance controls gain and timing in the hippocampus. Elife, 8, e43415.
• 04
  Viswan, N. A., HarshaRani, G. V., Stefan, M. I., & Bhalla, U. S. (2018). FindSim: a framework for integrating neuronal data and signaling models. Frontiers in Neuroinformatics, 12, 38.
• 05
  Bhalla, U. S. (2017). Synaptic input sequence discrimination on behavioral timescales mediated by reaction-diffusion chemistry in dendrites. Elife, 6, e25827.
• 06
  Brocke, E., Bhalla, U. S., Djurfeldt, M., Hellgren Kotaleski, J., & Hanke, M. (2016). Efficient integration of coupled electrical-chemical systems in multiscale neuronal simulations. Frontiers in Computational Neuroscience, 10, 97.
• 07
  Gilra, A., & Bhalla, U. S. (2015). Bulbar microcircuit model predicts connectivity and roles of interneurons in odor coding. PLoS One, 10(5), e0098045.
• 08
  Jain, P., & Bhalla, U. S. (2014). Transcription control pathways decode patterned synaptic inputs into diverse mRNA expression profiles. PloS One, 9(5), e95154.
• 09
  Dudani, N., Bhalla, U. S., & Ray, S. (2014). MOOSE, the Multiscale Object-Oriented Simulation Environment. In D. Jaeger & R. Jung (Eds.), Encyclopedia of Computational Neuroscience. Springer New York. https://doi.org/10.1007/978-1-4614-7320-6_257-1
• 10
  Bhalla, U. S. (2011). Multiscale interactions between chemical and electric signaling in LTP induction, LTP reversal and dendritic excitability. Neural Networks, 24(9), 943–949.
• 11
  Bhalla, U. S. (2011). Trafficking motifs as the basis for two-compartment signaling systems to form multiple stable states. Biophysical Journal, 101(1), 21–32.
• 12
  Djurfeldt, M., Hjorth, J., Eppler, J. M., Dudani, N., Helias, M., Potjans, T. C., Bhalla, U. S., Diesmann, M., Hellgren Kotaleski, J., & Ekeberg, Ö. (2010). Run-time interoperability between neuronal network simulators based on the MUSIC framework. Neuroinformatics, 8, 43–60.
• 13
  Gleeson, P., Crook, S., Cannon, R. C., Hines, M. L., Billings, G. O., Farinella, M., Morse, T. M., Davison, A. P., Ray, S., Bhalla, U. S., & others. (2010). NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Computational Biology, 6(6), e1000815.
• 14
  Jain, P., & Bhalla, U. S. (2009). Signaling logic of activity-triggered dendritic protein synthesis: an mTOR gate but not a feedback switch. PLoS Computational Biology, 5(2), e1000287.
• 15
  Ray, S., & Bhalla, U. S. (2008). PyMOOSE: Interoperable scripting in Python for MOOSE. Frontiers in Neuroinformatics, 2. https://doi.org/10.3389/neuro.11.006.2008