https://crcns.org These are the search results for the query, showing results 661 to 674. https://crcns.org/forum/using-datasets/115877553/655840342 I think all the data is there.   Only the first 100 items are shown on the first page.   You need to clink on the link for page 2 or the link that says "Next 19 items."     No publisher jteeters 2014-05-17T01:37:16Z Comment https://crcns.org/forum/using-datasets/115877553/655840341 Hello,   Some data that explained in CRCNS.org hc3 data description document is missing in the hc-3 downloads folder such as gor01 or vvp01. It seems like these are the only experiments that have simultaneous CA3 and CA1 recordings that I need :) Is is possible for you to upload them too?   Thank you,   Ali No publisher argunsah 2014-05-15T14:44:34Z Comment https://crcns.org/marketplace/ideas-methods-for-data-analysis/747828408/541159442 Here are two ideas for data analysis that could be used to explore the hc-2/3 data provided by the Buzsaki group: (1) I once plotted CA1 place field size (the distance between the first and last spike) as a function of velocity (the amount of time it took the rat to run between the spikes). If you look at the figure I uploaded, the first interesting thing to note is that there are striations. These striations are due to the fact that hippocampal place field size is a somewhat quantal function of the number of theta cycles that the neuron is active for (for example, if the rat is moving at 3cm/theta cycle, then a place field can be roughly 3,6,9,12... cm in size). You will also note that these striations "fan out". This suggests that the distance the rodent covers within a theta cycle increases with velocity, which is somewhat counter-intuitive: theta frequency should increase with velocity (Michael Recce's thesis shows a nice curve that increases and then plateaus). In my personal experience, sometimes theta frequency changes with velocity and other times it does not. So here are a few questions- "Does place field size increase as a function of velocity or were my data the abnormality?", "Does theta frequency change as a function of velocity?", "Is there a novelty/familiarity effect?",  "Is the size increase with velocity also present in the entorhinal cortex?" In my experiments on circular tracks, there was an issue that velocity was coupled to location (rats moved slow when they were near a food dish and fast when they were away from a food dish). These data (open-field, linear tracks and wheels) provide a great database to explore this phenomenon more intensively than my data could.    (2) It may be worthwhile to re-visit the "center-of-mass shift" phenomenon (Mehta et al., 1997) with respect to the different sub-fields of the hippocampus and how this alters the "phase-position" density profiles (Mehta et al., 2002). Note that CA3 may or may not show a COM shift (Lee et al., 2004). I am unaware of anyone looking at this in the dentate. Hafting et al., (2008) explored center of mass within the MEC, but this provides a chance to replicate results.  Please email me if you are interested: Drewmaurer at ufl dot edu No publisher AndrewMaurer 2014-05-01T18:10:07Z Comment https://crcns.org/forum/using-datasets/716725845/937235040 The data description document for the data set has been updated to include this information.   No publisher admin 2015-12-21T00:09:00Z Comment https://crcns.org/forum/using-datasets/94806677/503175600 Hello, Thanks a lot for your interest in our data set. Since I haven't been working on this data set for more than two years, I'm afraid that I have to say that I cannot recall every single details right now, but I hope the following explanation might help. For the depth profile of CA1 channels, please download crcns-hc3-channelorder.zip and take a look at CorrectChaOrderCA1.m file. As you mentioned, bad channels were removed. CorrectChaOrderCA1.m tells you the positions of the intact (recorded) channels in each shank. Distance between shanks from the same probe was 200 micrometer. The channel with the smallest channel ID # in each shank is closest to the tip of the shank (but please double check with visual inspection...)   Thanks. Kenji         No publisher kmizuseki 2015-11-16T00:27:40Z Comment https://crcns.org/forum/using-datasets/6343405/510526015 Hi, Mark I found that EPI images and T1 structure images were provided in this data set. It is hard to register EPI images to the T1 structure images cause EPI images was not for the whole brain. Sform matrixes of T1 and EPI were provided in .header file. However, Both of these matrixes match T1 and EPI image to original point in voxel coordinate, which means we could not use inv(Sform_T1)*Sform_EPI transform EPI to T1. How could I match EPI images to T1?   Previously Mark Lescroart wrote: Hello Dai (and anyone else with the same question), We will not be providing the code for the basis-restricted separable model.  However, you may find the MATLAB code in GLMdenoise ( http://kendrickkay.net/GLMdenoise/ ) useful, specifically, the GLMestimatemodel.m function.  This function implements a separable model with a FIR basis, and could be relatively easily tweaked to use a different basis (e.g. sines and cosines).  (GLMdenoise itself provides denoising benefits, but that was not used on the 2008 data.)     No publisher todaizhang 2014-01-15T08:25:20Z Comment https://crcns.org/forum/using-datasets/527189382/140442209 Hello, thanks for the follow up. 1. Sorry for the confusion. Indeed, the amplification factor is 1,000x, and not 20,000x. The 16-bit number includes a sign bit, and spans a 20 V range. 2. Since one can specify which shanks to load using LoadCluRes, the first column contains arbitrarily assigned cluster numbers. The 2nd column indicates the shank on which the spike was detected, and the 3rd column indicates the original number assigned to a given cluster. Thus, if there were 20 unique clusters detected on a shank, the 3rd column would contain values ranging from 1 to 20. Best Gautam   Previously unset wrote: Thanks very much for Gautam’s reply! But I still have two questions. 1. I'm still confused about the unit of the LFP waveform in the *.eeg files. In the "crcns.org hc2 data description" pdf file, it is mentioned that the neurophysiological signals were amplified 1,000x, but you said  the voltage was amplified 20,000x, why?  Additionally, in the 16-bit coded numbers, whether it includes a sign bit? 2. I've comprehended that the spike clusters are unrelated to the number of channels, but I want to know that how a specified cluster is assigned to the corresponding electrode. In the "LoadCluRes" script, it is commented that "Map is a matrix displaying the correspondance between new cluster numbers (first column) and inital shank # (second column) and cluster # (third column)". In the matrix "Map" of "ec013.527", for example, the 9th line is " 9 1 12", what the "12" means? In otheer words, how can I find the spike train of a neuron is recorded by which electrode? Thamks!         No publisher synapse 2012-03-08T18:11:36Z Comment https://crcns.org/forum/using-datasets/527189382/393388514 Thanks very much for Gautam’s reply! But I still have two questions. 1. I'm still confused about the unit of the LFP waveform in the *.eeg files. In the "crcns.org hc2 data description" pdf file, it is mentioned that the neurophysiological signals were amplified 1,000x, but you said  the voltage was amplified 20,000x, why?  Additionally, in the 16-bit coded numbers, whether it includes a sign bit? 2. I've comprehended that the spike clusters are unrelated to the number of channels, but I want to know that how a specified cluster is assigned to the corresponding electrode. In the "LoadCluRes" script, it is commented that "Map is a matrix displaying the correspondance between new cluster numbers (first column) and inital shank # (second column) and cluster # (third column)". In the matrix "Map" of "ec013.527", for example, the 9th line is " 9 1 12", what the "12" means? In otheer words, how can I find the spike train of a neuron is recorded by which electrode? Thamks!       No publisher lizhaohui 2012-03-06T14:45:31Z Comment https://crcns.org/forum/using-datasets/527189381/253998231 Hello, I have two questions about the hc-2 data. I will take the dataset 'ec013.527' for example. 1.       I have got use LFP signals using the LoadBinary’ script, and the data is a matrix of 31*1325125 elements. On the other hand, the channels is 0:30, what is their relations? And, what is the unit of the data in the matrix? 2.       There are 31 channels in the recording data, but I have got 37 clusters of spikes. How can I assign each cluster to the electrodes.   Thank you very much!   No publisher lizhaohui 2012-02-27T14:49:25Z Comment https://crcns.org/forum/using-datasets/276820825/456899295 I am working with the HC-3 dataset and I am encountering some error when trying to get the timestamps of the spiking units and the position to match up in seconds.  In this example, I am using the file gor01-6-7/2006-6-7_11-26-53/2006-6-7_11-26-53   I import the timestamps of the units using this command in Matlab: [T, G, Map, Par]=LoadCluRes(fileName); Since these timestamps are sampled at 20kHz, I converted to seconds by Tsec = T/20000; I then load the position data: whl = load([fileName,'.whl']); I then create a vector of sample times (based on 39.06 Hz sampling rate) from this vector with this code:   xStart = 0; dx = 1/39.06; N = length(whl); positionSampling = xStart + (0:N-1)*dx;   In the end, my Tsec vector reaches 4381.8 seconds but the positionSampling vector only reaches 3038.4 seconds.  This is a difference of 1343 seconds or 22 minutes.  Was the spiking data recorded for 22 minutes longer than the position data?  Is there an offset between the beginning of the spike train acquisition and the position acquisition that I am missing something? Or is my conversion to seconds incorrect somewhere? No publisher DFetterhoff 2016-04-12T15:08:25Z Comment https://crcns.org/forum/using-datasets/708216874/684924502 Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} Hello, This question has been posted a while ago but it seems to me that the answer is not clear enough. I am using the MATLAB scripts to view the hc-2 dataset (file ec013.529) and I  followed the instructions for hc-1 conversion. For ec013.529 file the LoadPar function returns the following:  nBits: 16                       SampleRate: 20000                      SampleTime: 50 VoltageRange: 20     Amplification: 1000                      Offset: 0 So according to this parameters the conversion should be something like: Vm = data./2^15*VoltageRange;      But seeing the traces I am not sure that the y-axis values are correct.   For dataset hc-1 file d533101.dat (original question posted in 2012) the parameters are  nBits: 16                      SampleRate: 10000                      SampleTime: 100  VoltageRange: 20    Amplification: 1000                      Offset: 2048 But the solution given was Vm = (data-Offset)./2^11*200; My questions are -Why is 200 used as system voltage range if in the parameters is listed as 20? -Were xml data description files always verified? Thank you! No publisher Lixxbeth 2015-09-22T11:26:11Z Comment https://crcns.org/forum/using-datasets/6343405/561006863 Previously Mark Lescroart wrote: Hello Dai (and anyone else with the same question), We will not be providing the code for the basis-restricted separable model.  However, you may find the MATLAB code in GLMdenoise ( http://kendrickkay.net/GLMdenoise/ ) useful, specifically, the GLMestimatemodel.m function.  This function implements a separable model with a FIR basis, and could be relatively easily tweaked to use a different basis (e.g. sines and cosines).  (GLMdenoise itself provides denoising benefits, but that was not used on the 2008 data.)   Hello Mark, I tried using GLMdenoise, but this technique assumes that we have at least 2 runs with same experimental conditions (which I believe is not there in vim-1). Further, using  GLMestimate.m I get an error stating  "Error using olsmatrix2 (line 56)  Matrix is singular, close to singular or badly scaled. Results may be inaccurate. RCOND = NaN."  Is there a mistake in my implementation? p.s. I have concatenated data and assumed it to be single run.   Thank you   Aakash No publisher aakash 2015-09-05T03:44:48Z Comment https://crcns.org/forum/using-datasets/537575916/199758561 Hi all, I'm trying to analyze the vim-1 data and then perform category based similarity analysis, here are two questions I got: 1) converting the voxIdx<S1/S2> to image coordinates(i, j, k): I tried the ind2sub function in MATLAB - to extract image coordinates for V1 region, I used: [i, j, k] = ind2sub([64, 64, 18], voxIdx(roiS1 == 1)) and then I generated the corresponding mask for V1. But when I overlayed the mask on the bold data, the V1 region is wrong. My guess is that for MATLAB, the indexing starts from the top slice, but for the BOLD data, since it's in LPI system, the indexing starts from the bottom slice. So I wonder if it's the case and what's the correct way to convert voxIdx to ijk coordinates. 2) I'm trying to calculate the similarity between brain patterns of different categories, so I wonder if there is any category information of the image stimuli for each run.  Best, Xixi No publisher xixiwang 2015-08-18T20:20:31Z Comment https://crcns.org/forum/using-datasets/537575915/358214518 I am trying to use CRCNS dataset in my saliency detection method. How can I find the ground truth(or density map of subject)? I have read the readme.txt file. I found the eye-tracker data which contain all the data of every subject. Is there any code that can help me to create density map from that? Thank you very much! No publisher BraidTim 2015-08-05T09:37:11Z Comment