Learning intrinsic excitability in Medium Spiny Neurons (Scheler 2014)

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Accession:155131
"We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. ... "
Reference:
1 . Scheler G (2014) Learning intrinsic excitability in medium spiny neurons F1000Research 2:88
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Striatum;
Cell Type(s): Neostriatum spiny direct pathway neuron; Neostriatum spiny indirect pathway neuron;
Channel(s): I A; I K; I h; I K,Ca; I Calcium; I A, slow; I Cl, leak; I Ca,p;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s): Kv4.2 KCND2; Kv1.1 KCNA1; Kv1.2 KCNA2; Kv4.3 KCND3; Kv1.4 KCNA4; Kv1.3 KCNA3; Kv1.5 KCNA5; Kv3.3 KCNC3; Cav3.2 CACNA1H; Cav3.1 CACNA1G; Cav3.3 CACNA1I; Cav1.3 CACNA1D; Cav1.1 CACNA1S; Cav1.2 CACNA1C; KCa2.1 KCNN1; Kv2.1 KCNB1; Kv3.1 KCNC1; HCN Cnga1; Cav2.1 CACNA1A; Cav2.2 CACNA1B; KCa2.2 KCNN2; Kv1.9 Kv7.1 KCNQ1; IRK; NR2A GRIN2A; NR2B GRIN2B; Kv3.4 KCNC4; Kv4.1 KCND1;
Transmitter(s): Gaba; Glutamate; Ions;
Simulation Environment: MATLAB;
Model Concept(s): Intrinsic plasticity;
Implementer(s): Schumann, Johann [johann.schumann at gmail.com];
Search NeuronDB for information about:  Neostriatum spiny direct pathway neuron; Neostriatum spiny indirect pathway neuron; GabaA; AMPA; NMDA; I A; I K; I h; I K,Ca; I Calcium; I A, slow; I Cl, leak; I Ca,p; Gaba; Glutamate; Ions;
%=========================================================
startup.m:
must be called at start-up time to set the path

README.txt: this file

Directories with top-level execution scripts and examples.
ch_analysis:
	scripts for plots of V_m over uA/cm^2 and mS/cm^2
		gen_i_vm_001a.m
		gen_i_vm_001e.m
gain_filter:
	scripts to produce a gain plot (firing frequency over strength 
	of input) for various neuron types and parameters
		gf_001.m
		gf_002.m
		gf_002_inh.m
		gf_002_sk.m
		gf_002a_sk.m
		gf_002b_sk.m
		gf_002c_sk.m
		gf_002d_sk.m
		gf_002e_ie.m
		gf_002e_sk.m
		gf_003.m

input_analysis:
	scripts to plot various types of inputs
	for demonstration

interactive:
	This directory contains a number of scripts to initialize the sim,
	to generate inputs, produce plots and save the results.
	These scripts can be used in an interactive mode.
	Two examples of simple scripts are given:
		ex_001.m:   runs 5 variable neurons on generated input 
				and produces plots
		ex_izh_001.n:  runs 4 2D neurons

Directories with implementation code and utility functions
analysis:
	various routines to generate plots of simulation results

gui:
	draft of a gui for parameter exploration (not finished; used
	simulated synaptic input only and only 4 \mu parameters can be changed

input:
	utilities to generate different kinds of input signals
	(poisson-distributed EPSPs, IPSPs, single spikes, sin, DC, etc.)

neuron:
	contains definitions of the ion channels execution (simulation)
	mechanisms for one neuron or a vector of neurons

syn_response:
	contains main simulator functionality; run_sr4 called from various
	other directories


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