Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010)

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Accession:128812
To examine whether an oscillatory interference model of grid cell activity could work if the oscillators were noisy neurons, we implemented these simulations. Here the oscillators are networks (either synaptically- or gap-junction--coupled) of one or more noisy neurons (either Izhikevich's simple model or a Hodgkin-Huxley--type biophysical model) which drive a postsynaptic cell (which may be integrate-and-fire, resonate-and-fire, or the simple model) which should fire spatially as a grid cell if the simulation is successful.
Reference:
1 . Zilli EA, Hasselmo ME (2010) Coupled Noisy Spiking Neurons as Velocity-Controlled Oscillators in a Model of Grid Cell Spatial Firing J. Neurosci. 30(41):13850-13860
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex spiny stellate cell; Abstract integrate-and-fire leaky neuron;
Channel(s): I Na,p; I Na,t; I K; I K,leak; I h;
Gap Junctions: Gap junctions;
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Oscillations; Synchronization; Simplified Models; Spike Frequency Adaptation; Grid cell;
Implementer(s): Zilli, Eric [zilli at bu.edu];
Search NeuronDB for information about:  I Na,p; I Na,t; I K; I K,leak; I h;
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ZilliHasselmo2010
README.txt
Acker_sn_FI_n250.mat
fig_RS2sn_filthaftingtraj_n5000_p01_1noise_02res_July13_2D_spikes_0a.txt
fig_RS2sn_filthaftingtraj_n5000_p01_1noise_02res_July13_C_0a.mat
FigS4ABC_izh_simple0b_RS4sn_LIF_oct30_vary_gandncells_1noise_newpost_draft1.txt
FigS4DEF_izh_simple0b_RS4gn_LIF_oct30_vary_gandncells_1noise_newpost_draft1.txt
FigS8_izh_simple0b_RS2gn_LIF_nov08_vary_gandncells_0.125noise_newpost_draft1.txt
FigS8_izh_simple0b_RS2gn_LIF_nov08_vary_gandncells_0.25noise_newpost_draft1.txt
FigS8_izh_simple0b_RS2gn_LIF_nov08_vary_gandncells_0.5noise_newpost_draft1.txt
FigS8_izh_simple0b_RS2gn_LIF_nov08_vary_gandncells_1noise_newpost_draft1.txt
FigS8_izh_simple0b_RS2gn_LIF_nov08_vary_gandncells_2noise_newpost_draft1.txt
FigS8_izh_simple0b_RS2gn_LIF_nov08_vary_gandncells_4noise_newpost_draft1.txt
hafting_trajectory.m
rat_10925.mat
SI_acker_model_2d_grid.m
SI_acker_model_FI_history_dependence.m
SI_acker_model_FI_relations.m
SI_acker_model_stability_vs_params.m
SI_simple_model_2d_grid.m
SI_simple_model_FI_history_dependence.m
SI_simple_model_FI_relation.m
SI_simple_model_stability_vs_params.m
simple_model_RS1_FI_Jan09_n1.mat
simple_model_RS1gn_FI_Jan09_n250.mat
simple_model_RS1n_FI_Jan09_n1.mat
simple_model_RS1sn_FI_Jan09_n250.mat
simple_model_RS2_ext_FI_Jan09_n1.mat
simple_model_RS2_FI_Jan09_n1.mat *
simple_model_RS2gn_FI_Jan09_n250.mat
simple_model_RS2n_FI_Jan09_n1.mat
simple_model_RS2sn_FI_Jan01_n5000.mat
simple_model_RS2sn_FI_Jan01_n5000.txt
simple_model_RS2sn_FI_Jan09_n250.mat *
simple_model_RS2sn_FI_Jul10_n5000.mat
simple_model_RS2sn_FI_Jul10_n5000.txt
simple_model_RS2sn_FI_Jul11_n5000.mat
simple_model_RS2sn_FI_Jul11_n5000.txt
                            
5000	0.01	100	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	6.66969	0.149934	0.00060003	296.421	90	802
5000	0.01	101	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	6.84793	0.146031	0.000502165	391.016	93	826
5000	0.01	102	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.02331	0.142385	0.000580346	271.376	95	844
5000	0.01	103	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.18981	0.139088	0.000522536	312.021	98	912
5000	0.01	104	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.35617	0.135942	0.000483084	340.853	100	983
5000	0.01	105	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.51821	0.133012	0.000420154	422.087	103	1018
5000	0.01	106	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.67417	0.130309	0.000410233	416.301	105	1036
5000	0.01	107	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.83062	0.127705	0.000334436	589.581	108	1061
5000	0.01	108	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.98189	0.125285	0.000347781	514.791	110	1082
5000	0.01	109	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.13245	0.122965	0.000348926	483.537	112	1100
5000	0.01	110	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.28211	0.120743	0.000311141	575.733	114	1118
5000	0.01	111	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.42909	0.118638	0.000305898	565.021	117	1146
5000	0.01	112	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.57448	0.116626	0.000314471	507.898	119	1178
5000	0.01	113	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.72021	0.114677	0.000301319	525.925	121	1207
5000	0.01	114	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.86602	0.112791	0.00031258	464.999	123	1254
5000	0.01	115	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.00933	0.110997	0.000279093	555.883	125	1299
5000	0.01	116	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.15221	0.109264	0.00025779	621.51	127	1338
5000	0.01	117	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.29647	0.107568	0.000281203	498.385	130	1369
5000	0.01	118	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.44098	0.105922	0.000278851	483.909	132	1402
5000	0.01	119	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.58676	0.104311	0.000255661	549.812	134	1418
5000	0.01	120	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.73486	0.102724	0.000264854	489.279	136	1434
5000	0.01	121	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.88367	0.101178	0.000242258	558.789	138	1435
5000	0.01	122	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	10.0342	0.0996603	0.000268296	435.4	141	1451
5000	0.01	123	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	10.1874	0.0981608	0.000213962	654.174	143	1443
5000	0.01	124	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	10.3396	0.0967166	0.00026379	411.657	145	1434
5000	0.01	125	256	15	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	10.4991	0.0952473	0.000251376	432.973	148	1438
5000	0.01	100.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	6.75856	0.147962	0.000449885	506.754	63	546
5000	0.01	101.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	6.93464	0.144205	0.000405686	576.911	64	555
5000	0.01	102.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.10894	0.14067	0.000505042	345.539	66	580
5000	0.01	103.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.27373	0.137482	0.000441484	422.145	68	644
5000	0.01	104.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.43543	0.134493	0.000460919	362.574	69	667
5000	0.01	105.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.596	0.131649	0.000374117	516.167	71	685
5000	0.01	106.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.75241	0.128993	0.000376508	479.405	73	704
5000	0.01	107.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	7.90622	0.126484	0.00038429	433.848	74	713
5000	0.01	108.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.05909	0.124084	0.000298464	679.073	76	731
5000	0.01	109.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.2073	0.121844	0.00042068	323.638	77	740
5000	0.01	110.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.35567	0.11968	0.000261347	794.65	79	760
5000	0.01	111.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.50292	0.117607	0.000325217	486.979	80	769
5000	0.01	112.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.64574	0.115665	0.000302407	535.758	82	799
5000	0.01	113.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.7942	0.113712	0.000297714	525.257	83	830
5000	0.01	114.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	8.93596	0.111908	0.000309074	464.528	85	859
5000	0.01	115.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.08069	0.110124	0.00026119	619.852	86	881
5000	0.01	116.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.22525	0.108399	0.000273008	541.094	87	908
5000	0.01	117.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.37036	0.10672	0.000278053	497.777	89	925
5000	0.01	118.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.51553	0.105092	0.000306201	391.966	90	937
5000	0.01	119.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.66433	0.103474	0.000262226	510.142	92	947
5000	0.01	120.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.80926	0.101945	0.000260232	495.367	93	959
5000	0.01	121.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	9.95816	0.100421	0.000300671	354.684	95	970
5000	0.01	122.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	10.1092	0.0989208	0.000253778	475.887	96	974
5000	0.01	123.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	10.261	0.0974571	0.000293574	340.061	98	964
5000	0.01	124.5	256	10	100	4.5	0.0006	100	-60	-40	0.7	0.03	2	-50	100	35	10.4211	0.0959596	0.000248226	454.068	99	946