Normal ripples, abnormal ripples, and fast ripples in a hippocampal model (Fink et al. 2015)

 Download zip file 
Help downloading and running models
"...We use a computational model of hippocampus to investigate possible network mechanisms underpinning normal ripples, pathological ripples, and fast ripples. Our results unify several prior findings regarding HFO mechanisms, and also make several new predictions regarding abnormal HFOs. We show that HFOs are generic, emergent phenomena whose characteristics reflect a wide range of connectivity and network input. Although produced by different mechanisms, both normal and abnormal HFOs generate similar ripple frequencies, underscoring that peak frequency is unable to distinguish the two. Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing. In addition, fast ripples transiently and sporadically arise from the precise conditions that produce abnormal ripples. Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner. These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples."
1 . Fink CG, Gliske S, Catoni N, Stacey WC (2015) Network Mechanisms Generating Abnormal and Normal Hippocampal High-Frequency Oscillations: A Computational Analysis. eNeuro [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA1 basket cell;
Channel(s): I Na,t; I A; I K; I h;
Gap Junctions: Gap junctions;
Receptor(s): GabaA; NMDA; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Epilepsy;
Implementer(s): Fink, Christian G. [cgfink at]; Gliske, Stephen [sgliske at]; Stacey, William [wstacey at];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; NMDA; Glutamate; I Na,t; I A; I K; I h;
// $Id: interpxyz.hoc,v 1.2 2005/09/10 23:02:15 ted Exp $
/* Computes xyz coords of nodes in a model cell 
   whose topology & geometry are defined by pt3d data.
   Expects sections to already exist, and that the xtra mechanism has been inserted

Called in setpointers.hoc

// original data, irregularly spaced
objref xx, yy, zz, length
// interpolated data, spaced at regular intervals
objref xint, yint, zint, range

proc grindaway() { local ii, nn, kk, xr
	forall {
	  if (ismembrane("xtra")) {
		// get the data for the section
		nn = n3d()
		xx = new Vector(nn)
		yy = new Vector(nn)
		zz = new Vector(nn)
		length = new Vector(nn)

		for ii = 0,nn-1 {
			xx.x[ii] = x3d(ii)
			yy.x[ii] = y3d(ii)
			zz.x[ii] = z3d(ii)
			length.x[ii] = arc3d(ii)

		// to use Vector class's .interpolate() 
		// must first scale the independent variable
		// i.e. normalize length along centroid

		// initialize the destination "independent" vector
		range = new Vector(nseg+2)

		// length contains the normalized distances of the pt3d points 
		// along the centroid of the section.  These are spaced at 
		// irregular intervals.
		// range contains the normalized distances of the nodes along the 
		// centroid of the section.  These are spaced at regular intervals.
		// Ready to interpolate.

		xint = new Vector(nseg+2)
		yint = new Vector(nseg+2)
		zint = new Vector(nseg+2)
		xint.interpolate(range, length, xx)
		yint.interpolate(range, length, yy)
		zint.interpolate(range, length, zz)

		// for each node, assign the xyz values to x_xtra, y_xtra, z_xtra
//		for ii = 0, nseg+1 {
// don't bother computing coords of the 0 and 1 ends
// also avoid writing coords of the 1 end into the last internal node's coords
		for ii = 1, nseg {
			xr = range.x[ii]
			x_xtra(xr) = xint.x[ii]
			y_xtra(xr) = yint.x[ii]
			z_xtra(xr) = zint.x[ii]