"An endurantist holds that for an object to persist through time is for it to exist completely at different times (each instance of existence we can regard as somehow separate from previous and future instances, though still numerically identical with them). A perdurantist on the other hand holds that for a thing to exist through time is for it to exist as a continuous reality, and that when we consider the thing as a whole we must consider an aggregate of all its "temporal parts" or instances of existing. "
Cited from http://en.wikipedia.org/wiki/Philosophy_of_space_and_time
- http://arxiv.org/abs/1103.3300
- http://www.stat.cmu.edu/~gmg/home/index.php/publications.html
- http://www.cscs.umich.edu/~crshalizi/weblog/
- http://cscs.umich.edu/~crshalizi/notebooks/neural-coding.html
- van Rossum, M. C. W. (2001). A novel spike distance. Neural Comput. 13, 751–763
spike distance metric to evaluate each neuron’s spike discharge pattern. The metric quantifies the distance between every possible pair of spike trains in the data set for each neuron.
- Python Neo: http://packages.python.org/neo/classes.html (see RecordingPoint for link to spatial)
- OpenElectrophy http://neuralensemble.org/trac/OpenElectrophy/wiki
- Discrete Event System Specification http://en.wikipedia.org/wiki/DEVS
- Hierarchical Time Series http://robjhyndman.com/papers/hierarchical/
- Characterizing the fine structure of a neural sensory code through information distortion
http://www.springerlink.com/content/at610w0480663m00/fulltext.pdf
http://www.frontiersin.org/computational_neuroscience/10.3389/fncom.2011.00029/full
- DynamicRegion with t dimension. How to transform event and interval?
Use NetworkX to store coupling parameter for a system of coupled, damped oscillators http://www.uncg.edu/phy/hellen/Python_Instructions.html To make a network of such oscillators coupled over the network you could replace those lines with the code: def damped_osc(u,t,b,G): #defines the system of odes
n=len(G) x=u[:n] v=u[n:] dx=v dv=-x-b*v for n in G: # coupling
dx[n] += sum( G[n][nbr].get('weight',1)*(x[nbr]-x[n]) for nbr in G[n] )return r_[dx,dv] # this is one of many ways to concatenate numpy arrays
t = arange(0,20,0.1) u0 = array([1,1.1,0,0]) # initial x for each node and v for each node G=networkx.complete_graph(2) b=0.4 u=odeint(damped_osc,u0,t,args=(b,G)) #b is in tuple, needs comma
So, the network is stored in G, and the odeint vector field function can use it to hold the coupling coefficients between nodes.
- Construction of Symbolic Dynamicsfrom Experimental Time Series
- Carlsson, T. Ishkhanov, F. Mémoli, D. Ringach, G. Sapiro, ‘Topological analysis of the responses of neurons in V1,’
- Search for " Symbolic Dynamics Time Series"