--- /tmp/bmtk-0.0+git20210109.8572664+ds-1tr4uzqgz/debian/python3-bmtk-doc_0.0+git20210109.8572664+ds-1_all.deb +++ python3-bmtk-doc_0.0+git20210109.8572664+ds-1_all.deb ├── file list │ @@ -1,3 +1,3 @@ │ -rw-r--r-- 0 0 0 4 2021-01-22 07:46:41.000000 debian-binary │ -rw-r--r-- 0 0 0 10612 2021-01-22 07:46:41.000000 control.tar.xz │ --rw-r--r-- 0 0 0 9070372 2021-01-22 07:46:41.000000 data.tar.xz │ +-rw-r--r-- 0 0 0 9070368 2021-01-22 07:46:41.000000 data.tar.xz ├── control.tar.xz │ ├── control.tar │ │ ├── ./md5sums │ │ │ ├── ./md5sums │ │ │ │┄ Files differ ├── data.tar.xz │ ├── data.tar │ │ ├── ./usr/share/doc/python3-bmtk-doc/html/tutorial_filter_models.html │ │ │ @@ -1157,16 +1157,16 @@ │ │ │
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Units

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simple ON/OFF cells

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These are cells that respond by either increasing or decreasing firing rates to brightness changes in their receptive fields. They are also the basis for more advanced cell types. When building a network you can set a cell or set of cells with model_template='lgnmodel:tON' or model_template='lgnmodel:tOFF' during the call to add_nodes(...). There are also a number of special types │ │ │ (tON_TF8, sON_TF1, sON_TF2, sON_TF4, sON_TF8, sON_TF15, tOFF_TF1, tOFF_TF2, tOFF_TF4, tOFF_TF8, tOFF_TF15, sOFF_TF1, sOFF_TF2, sOFF_TF4, sOFF_TF8, sOFF_TF15) which have properties that have been pre-calculated using electrophysiological recordings from the mouse LGN (Durant et. al 2016, Billeh et. al 2019).

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A combination of a Gaussian spatial filter and double cosine temporal filter are used to filter the receptive field.

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4da9d79a39954ef1936f9c90a2d33505

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c802aee06f2640b0ba9e598ae80eeed2

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1df2bd128da14167aa0a43aea4ab81c4

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4222e2aaee614b19a8b01559a70ff2f8

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parameters

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The parameters for the cell are by default set in the .json file pointed to by the dynamic_params parameter are calculated from extracellular recordings from the mouse LGN (Durand et. al 2016, Billeh et. al, 2019). These parameters may be over ridden to be unique to each cell by passing in a list of values in the call to add_nodes. For example to given every cell a randomized spontaneous firing rate using the spont_fr variable (see below) you would just add the following:

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net.add_nodes(  # add 10 simple ON cells
│ │ │      N=20,
│ │ │      model_template='lgnmodel:tON',
│ │ ├── ./usr/share/doc/python3-bmtk-doc/html/tutorial_pointnet_modeling.html
│ │ │ @@ -1658,15 +1658,15 @@
│ │ │  _images/tutorial_pointnet_modeling_33_0.png
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Perturbation simulations

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A common use case is to mimick a network where the firing rate of select cells are being depressed or stimulated using current clamps or optogenetic methods. The best way to do this is by applying a current clamp in a similar manner used in bionet.

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The point_120cells network in the bmtk examples consists of 120 recurrently connected excitatory and inhibitory cells with randomized inputs. Normally the results without any type of perturbations will look as follows: 8097ac81619445bcb0677721685791e9

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The point_120cells network in the bmtk examples consists of 120 recurrently connected excitatory and inhibitory cells with randomized inputs. Normally the results without any type of perturbations will look as follows: 6ba542dbfaea4c2ab0a5a1a6755f7abe

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To artifically excite some of the cells we can open the config.json file and add the following section to the “inputs”:

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{
│ │ │    "exc_perturbation": {
│ │ │      "input_type": "current_clamp",
│ │ │      "module": "IClamp",
│ │ │      "node_set": {
│ │ │        "population": "cortex",
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│ │ │      "amp": -230.0,
│ │ │      "delay": 1.0,
│ │ │      "duration": 3000.0
│ │ │    }
│ │ │  }
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After we rerun the simulation: f0bb589b110041eb8440fc29ec5a582a

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After we rerun the simulation: b533c5e125d944ac8647c4722723f1d0

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