www.neuron.yale.edu

Two Cardiac Simulation/Computational Physiology postdocs (or contractor) computer modeling positions are available for development of a novel ICN (intrinsic cardiac nervous system) neuronal network simulation. New NIH SPARC Initiative ICN project is being launched at the SUNY Downstate Brooklyn Neu...

I didn't specify runge-kutta or anything else for DERIVATIVE block and get this warning: "adrunge not thread safe" Error goes away if I explicitly choose SOLVE states METHOD derivimplicit or cnexp Seems funny that the default METHOD is one that is not threadable? -- I had generally tried t...

July 13, 2018 Multiscale Modeling Tutorial using NEURON CNS*18 Meeting Seattle Washington, Allen Institute Auditorium The tutorial will present recent advances in methodologies for simulating the electrophysiology of cells and networks in the context of lower and higher scales or organization. At th...

I'm guessing that Anaconda gives you both 2 and 3 and that when NEURON detects that you have 3 installed it assumes that you want to use the more modern version?? I have no personal experience on this; A student reports: having trouble getting my Python 2.7 Anaconda env working ... py2.7 problem tur...

I'm just now re-stumbling over these messages which I haven't seen in a long time. Not sure if that reflects a change in the code or more likely just a change in the way I'm running. Anyway it would still be desirable to have a non-compile toggle for this (perhaps there is one but I didn't find).

I am having trouble getting used to NetPyNE and realize that part of this is my unfamiliarity with proper programming practices in a declarative language such as this. "Declarative programming is where you say what you want without having to say how to do it" When working in an imperative ...

There is great variability in spine resistance estimates in different studies and models. Eg Carnevale in 2013 said "Notably, our experimental data was closely modeled using a neck resistance of 520 M , similar to the value reported for hippocampal pyramidal neurons (26). " ("Compartm...

Two Computational Neuroscience postdoctoral modeling positions are immediately available for development of a novel NIBIB-BRAIN-funded brain theory. Our new NIH BRAIN Initiative brain theory project aims to build and establish a new theory - Embedded Ensemble Encoding (EEE) theory - out of observati...

in python 2.7+ can also do dict comprehensions so

hdict = {name:h.__getattribute__(name) for name in dir(h) if hasattr(h,name)}

I wanted a dictionary of hoc stuff for reasons that in retrospect was unnecessary, but having done this I thought I would share it hdict = dict([(name,h.__getattribute__(name)) for name in dir(h) if hasattr(h,name)]) the hasattr() turned out to be the trick since for reasons obscure some things turn...

With MH's assistance I have produced a functioning test for packing and unpacking # mpiexec -n 4 nrniv -python -mpi test.py from neuron import h pc = h.ParallelContext() myid = int(pc.id()) nhost = int(pc.nhost()) def storevecs (): pc.pack([myid],h.Vector().from_python(range(myid,100,nhost)).as_nump...

the following works for pack and unpack in python tvec = h.Vector().from_python([1.2,3.3,4.7]) # ersatz vec of spike times myid=8 pc.pack([myid],tvec.as_numpy()) # turn myid and vec into python objects when packing pc.post('test') pc.take('test') upmyid=pc.upkpyobj()[0] # grab the myid out of the 1 ...

if i understand correctly, there's a single thread here and, as in the case of multiple threads, the individual thread values have to be explicitly set to correspond to the time given by the interpreters h.t It appears that in order to see these threads that one needs to set up an explicit ParallelC...

Here's full runnable code demonstrating the points made above run python and then execfile('file.py') # file.py is where you saved it from neuron import h,gui import sys,os,time h.load_file("stdrun.hoc") from matplotlib import pyplot as mp import numpy as np mp.ion() mss = h.SaveState(); m...

After a SaveState.restore() , modifications are difficult and seem to depend on doing things at the correct time in the finitialize() sequence. I don't yet fully understand the reasons at each step but will post further as I better understand it. Here is the fairly simple sim I was working with: mss...