The virtual slice setup (Lytton et al. 2008)

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Accession:116838
"In an effort to design a simulation environment that is more similar to that of neurophysiology, we introduce a virtual slice setup in the NEURON simulator. The virtual slice setup runs continuously and permits parameter changes, including changes to synaptic weights and time course and to intrinsic cell properties. The virtual slice setup permits shocks to be applied at chosen locations and activity to be sampled intra- or extracellularly from chosen locations. ..."
Reference:
1 . Lytton WW, Neymotin SA, Hines ML (2008) The virtual slice setup. J Neurosci Methods 171:309-15 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Extracellular;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s):
Implementer(s): Lytton, William [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu];
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b09jan08
README.html
intf_.mod
misc.mod *
nstim.mod *
stats.mod
vecst.mod
misc.h
mosinit.hoc
screenshot.jpg
                            
// $Id: misc.h,v 1.11 2009/01/07 21:25:34 billl Exp $

#include <stdlib.h>
#include <math.h>
#include <limits.h> /* contains LONG_MAX */
#include <time.h>
#include <sys/time.h> 
#include <values.h>

typedef struct LISTVEC {
  int isz;
  double** pv;
  unsigned int* plen;
  unsigned int* pbuflen;
} ListVec;

typedef struct BVEC {
 int size;
 int bufsize;
 short *x;
 Object* o;
} bvec;

#define BYTEHEADER int _II__;  char *_IN__; char _OUT__[16]; int BYTESWAP_FLAG=0;
#define BYTESWAP(_X__,_TYPE__) \
    if (BYTESWAP_FLAG == 1) { \
	_IN__ = (char *) &(_X__); \
	for (_II__=0;_II__<sizeof(_TYPE__);_II__++) { \
		_OUT__[_II__] = _IN__[sizeof(_TYPE__)-_II__-1]; } \
	(_X__) = *((_TYPE__ *) &_OUT__); \
    }

#define UNCODE(_X_,_J_,_Y_) {(_Y_)=floor((_X_)/sc[(_J_)])/sc[4]; \
                             (_Y_)=floor(sc[4]*((_Y_)-floor(_Y_))+0.5);}

#define	SQRT2PI		2.5066282746
#define VRRY 200
#define ISVEC(_OB__) (strncmp(hoc_object_name(_OB__),"Vector",6)==0)

// Andre Fentons cast designations
typedef	unsigned char	ui1;	/* one byte unsigned integer */
typedef	char		si1;	/* one byte signed integer */
typedef unsigned short	ui2;	/* two byte unsigned integer */
typedef short		si2;	/* two byte signed integer */
typedef unsigned int	ui4;	/* four byte unsigned integer */ 
typedef int		si4;	/* four byte signed integer */ 
typedef float		sf4;	/* four byte signed floating point number */ 
typedef double		sf8;	/* eight byte signed floating point number */ 

extern double *vector_newsize();
extern unsigned int scrsz;
extern unsigned int *scr;
extern unsigned int *scrset(int);
extern double BVBASE;
extern double* hoc_pgetarg();
extern void hoc_notify_iv();
extern double hoc_call_func(Symbol*, int narg);
extern FILE* hoc_obj_file_arg(int narg);
extern Object** hoc_objgetarg();
char** hoc_pgargstr();
extern void vector_resize();
extern int vector_instance_px();
extern void* vector_arg();
extern double* vector_vec();
extern double hoc_epsilon;
extern int stoprun;
extern void set_seed();
extern double mcell_ran4();
extern int nrn_mlh_gsort();
extern int ivoc_list_count(Object*);
extern Object* ivoc_list_item(Object*, int);
extern int list_vector_px2();
extern int hoc_is_double_arg(int narg);
extern Symbol *hoc_get_symbol(char *);
extern Symbol *hoc_lookup(const char*);
extern Point_process* ob2pntproc(Object*);

extern char* hoc_object_name(Object*);
extern int cmpdfn();
extern int openvec(int, double **);
int list_vector_px();
double *list_vector_resize();
static void hxe() { hoc_execerror("",0); }
extern void FreeListVec(ListVec** pp);
extern ListVec* AllocListVec(Object* p);
extern ListVec* AllocILV(Object*, int, double *);
void FillListVec(ListVec* p,double dval);
extern short *nrn_artcell_qindex_;
extern double nrn_event_queue_stats(double*);
extern void clear_event_queue();

static double sc[6];

Lytton WW, Neymotin SA, Hines ML (2008) The virtual slice setup. J Neurosci Methods 171:309-15[PubMed]

References and models cited by this paper

References and models that cite this paper

Carnevale NT, Hines ML (2006) The NEURON Book

Efroni S, Harel D, Cohen IR (2005) Reactive animation: Realistic modeling of complex dynamic systems Computer 38:38-47

Efroni S, Harel D, Cohen IR (2007) Emergent dynamics of thymocyte development and lineage determination. PLoS Comput Biol 3:e13 [PubMed]

Hereld M, Stevens RL, Lee HC, van Drongelen W (2007) Framework for interactive million-neuron simulation. J Clin Neurophysiol 24:189-96 [PubMed]

Hines ML, Carnevale NT (2004) Discrete event simulation in the NEURON environment. Neurocomputing 58-60:1117-1122 [Journal]

   Discrete event simulation in the NEURON environment (Hines and Carnevale 2004) [Model]

Lytton W, Hines M (2004) Hybrid neural networks - combining abstract and realistic neural units. Conf Proc IEEE Eng Med Biol Soc 6:3996-8 [PubMed]

Lytton WW (2006) Neural Query System: Data-mining from within the NEURON simulator. Neuroinformatics 4:163-76 [Journal] [PubMed]

   Neural Query System NQS Data-Mining From Within the NEURON Simulator (Lytton 2006) [Model]

Lytton WW, Omurtag A (2007) Tonic-clonic transitions in computer simulation. J Clin Neurophysiol 24:175-81 [PubMed]

   Tonic-clonic transitions in a seizure simulation (Lytton and Omurtag 2007) [Model]

Lytton WW, Omurtag A, Neymotin SA, Hines ML (2008) Just in time connectivity for large spiking networks Neural Comput 20(11):2745-56 [Journal] [PubMed]

   JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008) [Model]

Lytton WW, Stewart M (2005) A rule-based firing model for neural networks Int J Bioelectromagn 7:47-50

Lytton WW, Stewart M (2006) Rule-based firing for network simulations. Neurocomputing 69:1160-1164

Lytton WW, Stewart M (2007) Data mining through simulation. Methods Mol Biol 401:155-66 [PubMed]

Lytton WW, Stewart M, Hines ML (2008) Simulation of large networks: technique and progress Computational Neuroscience in Epilepsy, Soltesz I:Staley K, ed. pp.3

Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153-60 [Journal] [PubMed]

   [241 reconstructed morphologies on NeuroMorpho.Org]

Migliore M, Cannia C, Lytton WW, Markram H, Hines ML (2006) Parallel Network Simulations with NEURON. J Comp Neurosci 21:110-119 [Journal] [PubMed]

   Parallel network simulations with NEURON (Migliore et al 2006) [Model]

ModelDB (2007) http:--senselab.med.yale.edu-senselab-ModelDB

Rowat PF, Selverston AI (1993) Modeling the gastric mill central pattern generator of the lobster with a relaxation-oscillator network. J Neurophysiol 70:1030-53 [Journal] [PubMed]

Web_site_neuron (2007) http:--www.neuron.yale.edu

Chadderdon GL, Mohan A, Suter BA, Neymotin SA, Kerr CC, Francis JT, Shepherd GM, Lytton WW (2014) Motor cortex microcircuit simulation based on brain activity mapping. Neural Comput 26:1239-62 [Journal] [PubMed]

   Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014) [Model]

Chadderdon GL, Neymotin SA, Kerr CC, Lytton WW (2012) Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex PLoS ONE 2012 7(10):e47251 [Journal]

   Reinforcement learning of targeted movement (Chadderdon et al. 2012) [Model]

Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW (2016) Restoring behavior via inverse neurocontroller in a lesioned cortical spiking model driving a virtual arm. Front. Neurosci. Neuroprosthetics 10:28 [Journal]

   Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015) [Model]

Dura-Bernal S, Zhou X, Neymotin SA, Przekwas A, Francis JT, Lytton WW (2015) Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm. Front Neurorobot 9:13 [Journal] [PubMed]

   Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015) [Model]

Kerr CC, Neymotin SA, Chadderdon GL, Fietkiewicz CT, Francis JT, Lytton WW (2012) Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex IEEE Transactions on Neural Systems & Rehabilitation Engineering 20(2):153-60 [Journal] [PubMed]

   Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012) [Model]

Kerr CC, Van Albada SJ, Neymotin SA, Chadderdon GL, Robinson PA, Lytton WW (2013) Cortical information flow in Parkinson's disease: a composite network-field model. Front Comput Neurosci 7:39:1-14 [Journal] [PubMed]

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Lytton WW, Neymotin SA, Wester JC, Contreras D (2011) Neocortical simulation for epilepsy surgery guidance: Localization and intervention Computational Surgery and Dual Training

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Lytton WW, Seidenstein AH, Dura-Bernal S, McDougal RA, Schurmann F, Hines ML (2016) Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON. Neural Comput :1-28 [Journal] [PubMed]

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Neymotin SA, Chadderdon GL, Kerr CC, Francis JT, Lytton WW (2013) Reinforcement learning of 2-joint virtual arm reaching in a computer model of sensorimotor cortex Neural Computation 25(12):3263-93 [Journal] [PubMed]

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Neymotin SA, Jacobs KM, Fenton AA, Lytton WW (2011) Synaptic information transfer in computer models of neocortical columns. J Comput Neurosci. 30(1):69-84 [Journal] [PubMed]

   Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010) [Model]

Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW (2011) Emergence of physiological oscillation frequencies in a computer model of neocortex. Front Comput Neurosci 5:19-75 [Journal] [PubMed]

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(29 refs)