| || Models ||Description|
Discrimination on behavioral time-scales mediated by reaction-diffusion in dendrites (Bhalla 2017)
||Sequences of events are ubiquitous in sensory, motor, and cognitive function. Key computational
operations, including pattern recognition, event prediction, and plasticity, involve neural
discrimination of spatio-temporal sequences. Here we show that synaptically-driven reaction
diffusion pathways on dendrites can perform sequence discrimination on behaviorally relevant
time-scales. We used abstract signaling models to show that selectivity arises when inputs at
successive locations are aligned with, and amplified by, propagating chemical waves triggered by
previous inputs. We incorporated biological detail using sequential synaptic input onto spines in
morphologically, electrically, and chemically detailed pyramidal neuronal models based on rat data.
Moose/PyMOOSE: interoperable scripting in Python for MOOSE (Ray and Bhalla 2008)
||" ... We report the integration of Python scripting with the Multi-scale Object Oriented Simulation Environment (MOOSE). MOOSE is a general-purpose simulation system for compartmental neuronal models and for models of signaling pathways based on chemical kinetics. We show how the Python-scripting version of MOOSE, PyMOOSE, combines the power of a compiled simulator with the versatility and ease of use of Python. ... "
Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)
||A detailed network model of the dual-layer dendro-dendritic inhibitory microcircuits in the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra, Upinder S. Bhalla (2015).
All cell morphologies and network connections are in NeuroML v1.8.0. PG and granule cell channels and synapses are also in NeuroML v1.8.0. Mitral cell channels and synapses are in native python.
Parameter optimization using CMA-ES (Jedrzejewski-Szmek et al 2018)
||"Computational models in neuroscience can be used to predict causal
relationships between biological mechanisms in neurons and networks,
such as the effect of blocking an ion channel or synaptic connection
on neuron activity. Since developing a biophysically realistic, single
neuron model is exceedingly difficult, software has been developed for
automatically adjusting parameters of computational neuronal
models. The ideal optimization software should work with commonly used
neural simulation software; thus, we present software which works with
models specified in declarative format for the MOOSE
simulator. Experimental data can be specified using one of two
different file formats. The fitness function is customizable as a
weighted combination of feature differences. The optimization itself
uses the covariance matrix adaptation-evolutionary strategy, because
it is robust in the face of local fluctuations of the fitness
function, and deals well with a high-dimensional and discontinuous
fitness landscape. We demonstrate the versatility of the software by
creating several model examples of each of four types of neurons (two
subtypes of spiny projection neurons and two subtypes of globus
pallidus neurons) by tuning to current clamp data.