new selection of grid cells color changes and histogram overview of change
This commit is contained in:
Mikkel Elle Lepperød
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@ -13173,7 +13173,7 @@ div#notebook {
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<div class="output_subarea output_stream output_stderr output_text">
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<pre>14:18:41 [I] klustakwik KlustaKwik2 version 0.2.6
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<pre>17:02:17 [I] klustakwik KlustaKwik2 version 0.2.6
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</pre>
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</div>
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</div>
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@ -13196,6 +13196,9 @@ div#notebook {
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<span class="n">bin_size</span> <span class="o">=</span> <span class="mf">0.02</span>
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<span class="n">smoothing_low</span> <span class="o">=</span> <span class="mf">0.03</span>
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<span class="n">smoothing_high</span> <span class="o">=</span> <span class="mf">0.06</span>
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<span class="n">stim_mask</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="n">baseline_duration</span> <span class="o">=</span> <span class="mi">600</span>
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</pre></div>
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</div>
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@ -13225,8 +13228,7 @@ div#notebook {
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<div class="inner_cell">
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<div class="input_area">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">identify_neurons</span> <span class="o">=</span> <span class="n">actions</span><span class="p">[</span><span class="s1">'identify-neurons'</span><span class="p">]</span>
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<span class="n">units</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">identify_neurons</span><span class="o">.</span><span class="n">data_path</span><span class="p">(</span><span class="s1">'all_non_identified_units'</span><span class="p">))</span>
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<span class="c1"># units = pd.read_csv(identify_neurons.data_path('units'))</span>
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<span class="n">units</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">identify_neurons</span><span class="o">.</span><span class="n">data_path</span><span class="p">(</span><span class="s1">'units'</span><span class="p">))</span>
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<span class="n">units</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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</pre></div>
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@ -13265,39 +13267,63 @@ div#notebook {
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<th></th>
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<th>action</th>
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<th>channel_group</th>
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<th>max_depth_delta</th>
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<th>max_dissimilarity</th>
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<th>unit_id</th>
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<th>unit_idnum</th>
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<th>unit_name</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>0</th>
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<td>1849-060319-3</td>
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<td>1</td>
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<td>104</td>
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<td>1834-010319-1</td>
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<td>0</td>
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<td>100</td>
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<td>0.05</td>
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<td>ae0353a9-a406-409e-8ff7-2e940b8af03f</td>
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<td>327</td>
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<td>2</td>
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</tr>
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<tr>
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<th>1</th>
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<td>1849-060319-3</td>
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<td>1</td>
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<td>108</td>
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<td>1834-010319-1</td>
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<td>0</td>
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<td>100</td>
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<td>0.05</td>
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<td>7f514d43-17ba-4d88-a390-20eec8bc1378</td>
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<td>328</td>
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<td>39</td>
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</tr>
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<tr>
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<th>2</th>
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<td>1849-060319-3</td>
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<td>1834-010319-3</td>
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<td>0</td>
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<td>100</td>
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<td>0.05</td>
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<td>c977aa51-06cc-4d54-9430-a94ad422a03b</td>
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<td>329</td>
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<td>1</td>
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<td>85</td>
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</tr>
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<tr>
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<th>3</th>
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<td>1849-060319-3</td>
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<td>1</td>
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<td>94</td>
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<td>1834-010319-3</td>
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<td>0</td>
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<td>100</td>
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<td>0.05</td>
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<td>bd96a67d-ee7d-4cb6-90ab-a5fa751891b9</td>
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<td>330</td>
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<td>12</td>
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</tr>
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<tr>
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<th>4</th>
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<td>1849-060319-3</td>
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<td>1</td>
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<td>98</td>
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<td>1834-010319-4</td>
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<td>0</td>
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<td>100</td>
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<td>0.05</td>
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<td>abc01041-2971-4f62-bf06-5132cf356737</td>
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<td>332</td>
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<td>7</td>
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</tr>
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</tbody>
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</table>
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@ -13335,7 +13361,7 @@ div#notebook {
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<div class="output_text output_subarea output_execute_result">
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<pre><matplotlib.axes._subplots.AxesSubplot at 0x7fd37b11ed30></pre>
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<pre><matplotlib.axes._subplots.AxesSubplot at 0x7f9a2e1f4dd8></pre>
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</div>
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</div>
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@ -13348,7 +13374,7 @@ div#notebook {
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<div class="output_png output_subarea ">
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<img src="data:image/png;base64,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<img src="data:image/png;base64,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
|
||||
"
|
||||
>
|
||||
</div>
|
||||
|
|
@ -13367,7 +13393,7 @@ div#notebook {
|
|||
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_loader</span> <span class="o">=</span> <span class="n">dp</span><span class="o">.</span><span class="n">Data</span><span class="p">(</span>
|
||||
<span class="n">position_sampling_rate</span><span class="o">=</span><span class="n">position_sampling_rate</span><span class="p">,</span>
|
||||
<span class="n">position_low_pass_frequency</span><span class="o">=</span><span class="n">position_low_pass_frequency</span><span class="p">,</span>
|
||||
<span class="n">box_size</span><span class="o">=</span><span class="n">box_size</span><span class="p">,</span> <span class="n">bin_size</span><span class="o">=</span><span class="n">bin_size</span>
|
||||
<span class="n">box_size</span><span class="o">=</span><span class="n">box_size</span><span class="p">,</span> <span class="n">bin_size</span><span class="o">=</span><span class="n">bin_size</span><span class="p">,</span> <span class="n">stim_mask</span><span class="o">=</span><span class="n">stim_mask</span><span class="p">,</span> <span class="n">baseline_duration</span><span class="o">=</span><span class="n">baseline_duration</span>
|
||||
<span class="p">)</span>
|
||||
</pre></div>
|
||||
|
||||
|
|
@ -13403,15 +13429,41 @@ div#notebook {
|
|||
<span class="c1"># common values for all units == faster calculations</span>
|
||||
<span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">speed</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="n">data_loader</span><span class="o">.</span><span class="n">tracking</span><span class="p">(</span><span class="n">action_id</span><span class="p">)</span><span class="o">.</span><span class="n">get</span><span class="p">,</span> <span class="p">[</span><span class="s1">'x'</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">,</span> <span class="s1">'t'</span><span class="p">,</span> <span class="s1">'v'</span><span class="p">])</span>
|
||||
<span class="n">ang</span><span class="p">,</span> <span class="n">ang_t</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="n">data_loader</span><span class="o">.</span><span class="n">head_direction</span><span class="p">(</span><span class="n">action_id</span><span class="p">)</span><span class="o">.</span><span class="n">get</span><span class="p">,</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'t'</span><span class="p">])</span>
|
||||
|
||||
<span class="n">occupancy_map</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">occupancy</span><span class="p">(</span><span class="n">action_id</span><span class="p">)</span>
|
||||
<span class="n">xbins</span><span class="p">,</span> <span class="n">ybins</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">spatial_bins</span>
|
||||
<span class="n">box_size_</span><span class="p">,</span> <span class="n">bin_size_</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">box_size_</span><span class="p">,</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">bin_size_</span>
|
||||
<span class="n">prob_dist</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">prob_dist</span><span class="p">(</span><span class="n">action_id</span><span class="p">)</span>
|
||||
|
||||
<span class="n">smooth_low_occupancy_map</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">maps</span><span class="o">.</span><span class="n">smooth_map</span><span class="p">(</span><span class="n">occupancy_map</span><span class="p">,</span> <span class="n">bin_size</span><span class="o">=</span><span class="n">bin_size_</span><span class="p">,</span> <span class="n">smoothing</span><span class="o">=</span><span class="n">smoothing_low</span><span class="p">)</span>
|
||||
<span class="n">smooth_high_occupancy_map</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">maps</span><span class="o">.</span><span class="n">smooth_map</span><span class="p">(</span><span class="n">occupancy_map</span><span class="p">,</span> <span class="n">bin_size</span><span class="o">=</span><span class="n">bin_size_</span><span class="p">,</span> <span class="n">smoothing</span><span class="o">=</span><span class="n">smoothing_high</span><span class="p">)</span>
|
||||
<span class="n">smooth_low_occupancy_map</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">maps</span><span class="o">.</span><span class="n">smooth_map</span><span class="p">(</span>
|
||||
<span class="n">occupancy_map</span><span class="p">,</span> <span class="n">bin_size</span><span class="o">=</span><span class="n">bin_size_</span><span class="p">,</span> <span class="n">smoothing</span><span class="o">=</span><span class="n">smoothing_low</span><span class="p">)</span>
|
||||
<span class="n">smooth_high_occupancy_map</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">maps</span><span class="o">.</span><span class="n">smooth_map</span><span class="p">(</span>
|
||||
<span class="n">occupancy_map</span><span class="p">,</span> <span class="n">bin_size</span><span class="o">=</span><span class="n">bin_size_</span><span class="p">,</span> <span class="n">smoothing</span><span class="o">=</span><span class="n">smoothing_high</span><span class="p">)</span>
|
||||
|
||||
<span class="n">spike_times</span> <span class="o">=</span> <span class="n">data_loader</span><span class="o">.</span><span class="n">spike_train</span><span class="p">(</span><span class="n">action_id</span><span class="p">,</span> <span class="n">channel_id</span><span class="p">,</span> <span class="n">unit_id</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">spike_times</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">result</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span>
|
||||
<span class="s1">'average_rate'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'speed_score'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'out_field_mean_rate'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'in_field_mean_rate'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'max_field_mean_rate'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'max_rate'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'sparsity'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'selectivity'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'interspike_interval_cv'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'burst_event_ratio'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'bursty_spike_ratio'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'gridness'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'border_score'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'information_rate'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'information_specificity'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'head_mean_ang'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'head_mean_vec_len'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'spacing'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span>
|
||||
<span class="s1">'orientation'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
<span class="p">})</span>
|
||||
<span class="k">return</span> <span class="n">result</span>
|
||||
|
||||
<span class="c1"># common</span>
|
||||
<span class="n">spike_map</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">maps</span><span class="o">.</span><span class="n">_spike_map</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">spike_times</span><span class="p">,</span> <span class="n">xbins</span><span class="p">,</span> <span class="n">ybins</span><span class="p">)</span>
|
||||
|
|
@ -13541,25 +13593,25 @@ div#notebook {
|
|||
|
||||
|
||||
<div class="output_text output_subarea output_execute_result">
|
||||
<pre>average_rate 3.095328
|
||||
speed_score -0.063922
|
||||
out_field_mean_rate 1.837642
|
||||
in_field_mean_rate 5.122323
|
||||
<pre>average_rate 3.168492
|
||||
speed_score -0.068927
|
||||
out_field_mean_rate 1.857990
|
||||
in_field_mean_rate 5.257561
|
||||
max_field_mean_rate 8.882211
|
||||
max_rate 23.006163
|
||||
sparsity 0.468122
|
||||
selectivity 7.306812
|
||||
interspike_interval_cv 3.970863
|
||||
burst_event_ratio 0.397921
|
||||
bursty_spike_ratio 0.676486
|
||||
gridness -0.459487
|
||||
border_score 0.078474
|
||||
information_rate 0.965845
|
||||
information_specificity 0.309723
|
||||
head_mean_ang 5.788704
|
||||
head_mean_vec_len 0.043321
|
||||
spacing 0.624971
|
||||
orientation 22.067900
|
||||
sparsity 0.466751
|
||||
selectivity 7.153172
|
||||
interspike_interval_cv 3.807699
|
||||
burst_event_ratio 0.398230
|
||||
bursty_spike_ratio 0.678064
|
||||
gridness -0.466923
|
||||
border_score 0.029328
|
||||
information_rate 1.009215
|
||||
information_specificity 0.317256
|
||||
head_mean_ang 5.438033
|
||||
head_mean_vec_len 0.040874
|
||||
spacing 0.628784
|
||||
orientation 20.224859
|
||||
dtype: float64</pre>
|
||||
</div>
|
||||
|
||||
|
|
@ -13597,13 +13649,13 @@ dtype: float64</pre>
|
|||
|
||||
|
||||
|
||||
<div id="c75fbc7b-721e-4d42-9315-b47198e54bff"></div>
|
||||
<div id="a40b72ae-f0e6-42c9-aab9-adcae33d48c4"></div>
|
||||
<div class="output_subarea output_widget_view ">
|
||||
<script type="text/javascript">
|
||||
var element = $('#c75fbc7b-721e-4d42-9315-b47198e54bff');
|
||||
var element = $('#a40b72ae-f0e6-42c9-aab9-adcae33d48c4');
|
||||
</script>
|
||||
<script type="application/vnd.jupyter.widget-view+json">
|
||||
{"model_id": "df0e286d762c4ef3b5a6a00a3b82eee6", "version_major": 2, "version_minor": 0}
|
||||
{"model_id": "2372ae1b2bea435dbc5bbfe2453747a6", "version_major": 2, "version_minor": 0}
|
||||
</script>
|
||||
</div>
|
||||
|
||||
|
|
@ -13615,24 +13667,13 @@ var element = $('#c75fbc7b-721e-4d42-9315-b47198e54bff');
|
|||
|
||||
|
||||
<div class="output_subarea output_stream output_stderr output_text">
|
||||
<pre>/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:56: RuntimeWarning: Mean of empty slice.
|
||||
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars
|
||||
ret = ret.dtype.type(ret / rcount)
|
||||
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:57: RuntimeWarning: Mean of empty slice.
|
||||
/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2
|
||||
<pre>/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2
|
||||
return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
|
||||
/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2
|
||||
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:110: RuntimeWarning: invalid value encountered in long_scalars
|
||||
/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2
|
||||
return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
|
||||
/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply
|
||||
return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *
|
||||
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice
|
||||
keepdims=keepdims)
|
||||
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:110: RuntimeWarning: invalid value encountered in true_divide
|
||||
arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
|
||||
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars
|
||||
ret = ret.dtype.type(ret / rcount)
|
||||
/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/quantities/quantity.py:624: RuntimeWarning: Mean of empty slice.
|
||||
ret = self.magnitude.mean(axis, dtype, None if out is None else out.magnitude)
|
||||
</pre>
|
||||
</div>
|
||||
</div>
|
||||
|
|
@ -13728,7 +13769,9 @@ var element = $('#c75fbc7b-721e-4d42-9315-b47198e54bff');
|
|||
<span class="s1">'box_size'</span><span class="p">:</span> <span class="n">box_size</span><span class="p">,</span>
|
||||
<span class="s1">'bin_size'</span><span class="p">:</span> <span class="n">bin_size</span><span class="p">,</span>
|
||||
<span class="s1">'smoothing_low'</span><span class="p">:</span> <span class="n">smoothing_low</span><span class="p">,</span>
|
||||
<span class="s1">'smoothing_high'</span><span class="p">:</span> <span class="n">smoothing_high</span>
|
||||
<span class="s1">'smoothing_high'</span><span class="p">:</span> <span class="n">smoothing_high</span><span class="p">,</span>
|
||||
<span class="s1">'stim_mask'</span><span class="p">:</span> <span class="n">stim_mask</span><span class="p">,</span>
|
||||
<span class="s1">'baseline_duration'</span><span class="p">:</span> <span class="n">baseline_duration</span>
|
||||
<span class="p">}</span>
|
||||
</pre></div>
|
||||
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@
|
|||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"14:18:41 [I] klustakwik KlustaKwik2 version 0.2.6\n"
|
||||
"17:02:17 [I] klustakwik KlustaKwik2 version 0.2.6\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -65,7 +65,10 @@
|
|||
"box_size = [1.0, 1.0]\n",
|
||||
"bin_size = 0.02\n",
|
||||
"smoothing_low = 0.03\n",
|
||||
"smoothing_high = 0.06"
|
||||
"smoothing_high = 0.06\n",
|
||||
"\n",
|
||||
"stim_mask = True\n",
|
||||
"baseline_duration = 600"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -108,51 +111,82 @@
|
|||
" <th></th>\n",
|
||||
" <th>action</th>\n",
|
||||
" <th>channel_group</th>\n",
|
||||
" <th>max_depth_delta</th>\n",
|
||||
" <th>max_dissimilarity</th>\n",
|
||||
" <th>unit_id</th>\n",
|
||||
" <th>unit_idnum</th>\n",
|
||||
" <th>unit_name</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>1849-060319-3</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>104</td>\n",
|
||||
" <td>1834-010319-1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>100</td>\n",
|
||||
" <td>0.05</td>\n",
|
||||
" <td>ae0353a9-a406-409e-8ff7-2e940b8af03f</td>\n",
|
||||
" <td>327</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>1849-060319-3</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>108</td>\n",
|
||||
" <td>1834-010319-1</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>100</td>\n",
|
||||
" <td>0.05</td>\n",
|
||||
" <td>7f514d43-17ba-4d88-a390-20eec8bc1378</td>\n",
|
||||
" <td>328</td>\n",
|
||||
" <td>39</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>1849-060319-3</td>\n",
|
||||
" <td>1834-010319-3</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>100</td>\n",
|
||||
" <td>0.05</td>\n",
|
||||
" <td>c977aa51-06cc-4d54-9430-a94ad422a03b</td>\n",
|
||||
" <td>329</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>85</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>1849-060319-3</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>94</td>\n",
|
||||
" <td>1834-010319-3</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>100</td>\n",
|
||||
" <td>0.05</td>\n",
|
||||
" <td>bd96a67d-ee7d-4cb6-90ab-a5fa751891b9</td>\n",
|
||||
" <td>330</td>\n",
|
||||
" <td>12</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>1849-060319-3</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>98</td>\n",
|
||||
" <td>1834-010319-4</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>100</td>\n",
|
||||
" <td>0.05</td>\n",
|
||||
" <td>abc01041-2971-4f62-bf06-5132cf356737</td>\n",
|
||||
" <td>332</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" action channel_group unit_name\n",
|
||||
"0 1849-060319-3 1 104\n",
|
||||
"1 1849-060319-3 1 108\n",
|
||||
"2 1849-060319-3 1 85\n",
|
||||
"3 1849-060319-3 1 94\n",
|
||||
"4 1849-060319-3 1 98"
|
||||
" action channel_group max_depth_delta max_dissimilarity \\\n",
|
||||
"0 1834-010319-1 0 100 0.05 \n",
|
||||
"1 1834-010319-1 0 100 0.05 \n",
|
||||
"2 1834-010319-3 0 100 0.05 \n",
|
||||
"3 1834-010319-3 0 100 0.05 \n",
|
||||
"4 1834-010319-4 0 100 0.05 \n",
|
||||
"\n",
|
||||
" unit_id unit_idnum unit_name \n",
|
||||
"0 ae0353a9-a406-409e-8ff7-2e940b8af03f 327 2 \n",
|
||||
"1 7f514d43-17ba-4d88-a390-20eec8bc1378 328 39 \n",
|
||||
"2 c977aa51-06cc-4d54-9430-a94ad422a03b 329 1 \n",
|
||||
"3 bd96a67d-ee7d-4cb6-90ab-a5fa751891b9 330 12 \n",
|
||||
"4 abc01041-2971-4f62-bf06-5132cf356737 332 7 "
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
|
|
@ -162,8 +196,7 @@
|
|||
],
|
||||
"source": [
|
||||
"identify_neurons = actions['identify-neurons']\n",
|
||||
"units = pd.read_csv(identify_neurons.data_path('all_non_identified_units'))\n",
|
||||
"# units = pd.read_csv(identify_neurons.data_path('units'))\n",
|
||||
"units = pd.read_csv(identify_neurons.data_path('units'))\n",
|
||||
"units.head()"
|
||||
]
|
||||
},
|
||||
|
|
@ -175,7 +208,7 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd37b11ed30>"
|
||||
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9a2e1f4dd8>"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
|
|
@ -184,7 +217,7 @@
|
|||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
|
|
@ -209,7 +242,7 @@
|
|||
"data_loader = dp.Data(\n",
|
||||
" position_sampling_rate=position_sampling_rate, \n",
|
||||
" position_low_pass_frequency=position_low_pass_frequency,\n",
|
||||
" box_size=box_size, bin_size=bin_size\n",
|
||||
" box_size=box_size, bin_size=bin_size, stim_mask=stim_mask, baseline_duration=baseline_duration\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
|
|
@ -241,25 +274,25 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"average_rate 3.095328\n",
|
||||
"speed_score -0.063922\n",
|
||||
"out_field_mean_rate 1.837642\n",
|
||||
"in_field_mean_rate 5.122323\n",
|
||||
"average_rate 3.168492\n",
|
||||
"speed_score -0.068927\n",
|
||||
"out_field_mean_rate 1.857990\n",
|
||||
"in_field_mean_rate 5.257561\n",
|
||||
"max_field_mean_rate 8.882211\n",
|
||||
"max_rate 23.006163\n",
|
||||
"sparsity 0.468122\n",
|
||||
"selectivity 7.306812\n",
|
||||
"interspike_interval_cv 3.970863\n",
|
||||
"burst_event_ratio 0.397921\n",
|
||||
"bursty_spike_ratio 0.676486\n",
|
||||
"gridness -0.459487\n",
|
||||
"border_score 0.078474\n",
|
||||
"information_rate 0.965845\n",
|
||||
"information_specificity 0.309723\n",
|
||||
"head_mean_ang 5.788704\n",
|
||||
"head_mean_vec_len 0.043321\n",
|
||||
"spacing 0.624971\n",
|
||||
"orientation 22.067900\n",
|
||||
"sparsity 0.466751\n",
|
||||
"selectivity 7.153172\n",
|
||||
"interspike_interval_cv 3.807699\n",
|
||||
"burst_event_ratio 0.398230\n",
|
||||
"bursty_spike_ratio 0.678064\n",
|
||||
"gridness -0.466923\n",
|
||||
"border_score 0.029328\n",
|
||||
"information_rate 1.009215\n",
|
||||
"information_specificity 0.317256\n",
|
||||
"head_mean_ang 5.438033\n",
|
||||
"head_mean_vec_len 0.040874\n",
|
||||
"spacing 0.628784\n",
|
||||
"orientation 20.224859\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
|
|
@ -277,15 +310,41 @@
|
|||
" # common values for all units == faster calculations\n",
|
||||
" x, y, t, speed = map(data_loader.tracking(action_id).get, ['x', 'y', 't', 'v'])\n",
|
||||
" ang, ang_t = map(data_loader.head_direction(action_id).get, ['a', 't'])\n",
|
||||
" \n",
|
||||
" occupancy_map = data_loader.occupancy(action_id)\n",
|
||||
" xbins, ybins = data_loader.spatial_bins\n",
|
||||
" box_size_, bin_size_ = data_loader.box_size_, data_loader.bin_size_\n",
|
||||
" prob_dist = data_loader.prob_dist(action_id)\n",
|
||||
" \n",
|
||||
" smooth_low_occupancy_map = sp.maps.smooth_map(occupancy_map, bin_size=bin_size_, smoothing=smoothing_low)\n",
|
||||
" smooth_high_occupancy_map = sp.maps.smooth_map(occupancy_map, bin_size=bin_size_, smoothing=smoothing_high)\n",
|
||||
" smooth_low_occupancy_map = sp.maps.smooth_map(\n",
|
||||
" occupancy_map, bin_size=bin_size_, smoothing=smoothing_low)\n",
|
||||
" smooth_high_occupancy_map = sp.maps.smooth_map(\n",
|
||||
" occupancy_map, bin_size=bin_size_, smoothing=smoothing_high)\n",
|
||||
" \n",
|
||||
" spike_times = data_loader.spike_train(action_id, channel_id, unit_id)\n",
|
||||
" if len(spike_times) == 0:\n",
|
||||
" result = pd.Series({\n",
|
||||
" 'average_rate': np.nan,\n",
|
||||
" 'speed_score': np.nan,\n",
|
||||
" 'out_field_mean_rate': np.nan,\n",
|
||||
" 'in_field_mean_rate': np.nan,\n",
|
||||
" 'max_field_mean_rate': np.nan,\n",
|
||||
" 'max_rate': np.nan,\n",
|
||||
" 'sparsity': np.nan,\n",
|
||||
" 'selectivity': np.nan,\n",
|
||||
" 'interspike_interval_cv': np.nan,\n",
|
||||
" 'burst_event_ratio': np.nan,\n",
|
||||
" 'bursty_spike_ratio': np.nan,\n",
|
||||
" 'gridness': np.nan,\n",
|
||||
" 'border_score': np.nan,\n",
|
||||
" 'information_rate': np.nan,\n",
|
||||
" 'information_specificity': np.nan,\n",
|
||||
" 'head_mean_ang': np.nan,\n",
|
||||
" 'head_mean_vec_len': np.nan,\n",
|
||||
" 'spacing': np.nan,\n",
|
||||
" 'orientation': np.nan\n",
|
||||
" })\n",
|
||||
" return result\n",
|
||||
"\n",
|
||||
" # common\n",
|
||||
" spike_map = sp.maps._spike_map(x, y, t, spike_times, xbins, ybins)\n",
|
||||
|
|
@ -397,12 +456,12 @@
|
|||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "df0e286d762c4ef3b5a6a00a3b82eee6",
|
||||
"model_id": "2372ae1b2bea435dbc5bbfe2453747a6",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"HBox(children=(IntProgress(value=0, max=1298), HTML(value='')))"
|
||||
"HBox(children=(IntProgress(value=0, max=1284), HTML(value='')))"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
|
@ -412,24 +471,13 @@
|
|||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:56: RuntimeWarning: Mean of empty slice.\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
|
||||
" ret = ret.dtype.type(ret / rcount)\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:57: RuntimeWarning: Mean of empty slice.\n",
|
||||
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
||||
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
||||
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in log2\n",
|
||||
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
||||
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/ipykernel_launcher.py:110: RuntimeWarning: invalid value encountered in long_scalars\n",
|
||||
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: divide by zero encountered in log2\n",
|
||||
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
|
||||
" keepdims=keepdims)\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:110: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
" arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
|
||||
" ret = ret.dtype.type(ret / rcount)\n",
|
||||
"/home/mikkel/.virtualenvs/expipe/lib/python3.6/site-packages/quantities/quantity.py:624: RuntimeWarning: Mean of empty slice.\n",
|
||||
" ret = self.magnitude.mean(axis, dtype, None if out is None else out.magnitude)\n"
|
||||
"/home/mikkel/apps/expipe-project/spatial-maps/spatial_maps/stats.py:13: RuntimeWarning: invalid value encountered in multiply\n",
|
||||
" return (np.nansum(np.ravel(tmp_rate_map * np.log2(tmp_rate_map/avg_rate) *\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -507,7 +555,9 @@
|
|||
" 'box_size': box_size,\n",
|
||||
" 'bin_size': bin_size,\n",
|
||||
" 'smoothing_low': smoothing_low,\n",
|
||||
" 'smoothing_high': smoothing_high\n",
|
||||
" 'smoothing_high': smoothing_high,\n",
|
||||
" 'stim_mask': stim_mask,\n",
|
||||
" 'baseline_duration': baseline_duration\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
|
|
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|
|||
bin_size: 0.02
|
||||
smoothing_low: 0.03
|
||||
smoothing_high: 0.06
|
||||
stim_mask: true
|
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baseline_duration: 600
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@ -1,19 +1,19 @@
|
|||
\begin{tabular}{lllll}
|
||||
\toprule
|
||||
{} & Baseline & Stimulated & MWU & PRS \\
|
||||
{} & Baseline & Stimulated & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 10.05 ± 0.65 (147) & 9.81 ± 0.69 (124) & 9040.00, 0.909 & 0.56, 0.717 \\
|
||||
Gridness & 0.54 ± 0.03 (147) & 0.43 ± 0.03 (124) & 7516.00, 0.013 & 0.17, 0.004 \\
|
||||
Sparsity & 0.66 ± 0.02 (147) & 0.69 ± 0.02 (124) & 10275.00, 0.071 & 0.04, 0.161 \\
|
||||
Selectivity & 5.35 ± 0.24 (147) & 5.28 ± 0.32 (124) & 8488.00, 0.330 & 0.23, 0.450 \\
|
||||
Information specificity & 0.21 ± 0.02 (147) & 0.18 ± 0.02 (124) & 7883.00, 0.056 & 0.03, 0.103 \\
|
||||
Max rate & 37.74 ± 1.40 (147) & 34.65 ± 1.30 (124) & 8165.00, 0.140 & 2.31, 0.108 \\
|
||||
Information rate & 1.18 ± 0.05 (147) & 0.93 ± 0.04 (124) & 6772.00, 0.000 & 0.18, 0.008 \\
|
||||
Interspike interval cv & 2.34 ± 0.06 (147) & 2.25 ± 0.07 (124) & 8361.00, 0.242 & 0.07, 0.500 \\
|
||||
In-field mean rate & 15.79 ± 0.82 (147) & 14.46 ± 0.79 (124) & 8526.00, 0.361 & 0.67, 0.638 \\
|
||||
Out-field mean rate & 7.41 ± 0.58 (147) & 7.43 ± 0.62 (124) & 9193.00, 0.903 & 0.88, 0.456 \\
|
||||
Burst event ratio & 0.22 ± 0.01 (147) & 0.21 ± 0.01 (124) & 8548.00, 0.379 & 0.01, 0.370 \\
|
||||
Specificity & 0.45 ± 0.02 (147) & 0.42 ± 0.02 (124) & 8221.00, 0.165 & 0.03, 0.167 \\
|
||||
Speed score & 0.13 ± 0.01 (147) & 0.11 ± 0.01 (124) & 7793.00, 0.040 & 0.02, 0.046 \\
|
||||
Average rate & 8.90 ± 0.67 (129) & 8.39 ± 0.60 (102) & 6514.00, 0.898 & 0.38, 0.786 \\
|
||||
Gridness & 0.52 ± 0.03 (129) & 0.44 ± 0.04 (102) & 5681.00, 0.075 & 0.13, 0.065 \\
|
||||
Sparsity & 0.62 ± 0.02 (129) & 0.66 ± 0.02 (102) & 7486.00, 0.072 & 0.06, 0.124 \\
|
||||
Selectivity & 5.93 ± 0.28 (129) & 5.98 ± 0.37 (102) & 6254.00, 0.520 & 0.10, 0.803 \\
|
||||
Information specificity & 0.23 ± 0.02 (129) & 0.22 ± 0.02 (102) & 5573.00, 0.046 & 0.05, 0.031 \\
|
||||
Max rate & 37.44 ± 1.44 (129) & 33.72 ± 1.31 (102) & 5851.00, 0.149 & 3.66, 0.072 \\
|
||||
Information rate & 1.25 ± 0.05 (129) & 0.96 ± 0.06 (102) & 4646.00, 0.000 & 0.29, 0.001 \\
|
||||
Interspike interval cv & 2.40 ± 0.07 (129) & 2.22 ± 0.08 (102) & 5516.00, 0.035 & 0.14, 0.270 \\
|
||||
In-field mean rate & 14.72 ± 0.82 (129) & 12.94 ± 0.71 (102) & 6026.00, 0.273 & 0.76, 0.414 \\
|
||||
Out-field mean rate & 6.35 ± 0.60 (129) & 6.12 ± 0.53 (102) & 6535.00, 0.931 & 0.08, 0.921 \\
|
||||
Burst event ratio & 0.21 ± 0.01 (129) & 0.20 ± 0.01 (102) & 5792.00, 0.119 & 0.02, 0.071 \\
|
||||
Specificity & 0.48 ± 0.02 (129) & 0.46 ± 0.02 (102) & 5962.00, 0.222 & 0.06, 0.181 \\
|
||||
Speed score & 0.14 ± 0.01 (129) & 0.10 ± 0.01 (102) & 5128.00, 0.004 & 0.02, 0.008 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
|||
|
|
|
@ -1,19 +1,19 @@
|
|||
\begin{tabular}{lllll}
|
||||
\toprule
|
||||
{} & Baseline & Stimulated & MWU & PRS \\
|
||||
{} & Baseline & Stimulated & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 10.05 ± 0.65 (147) & 9.81 ± 0.69 (124) & 9040.00, 0.909 & 0.56, 0.717 \\
|
||||
Gridness & 0.54 ± 0.03 (147) & 0.43 ± 0.03 (124) & 7516.00, 0.013 & 0.17, 0.004 \\
|
||||
Sparsity & 0.66 ± 0.02 (147) & 0.69 ± 0.02 (124) & 10275.00, 0.071 & 0.04, 0.161 \\
|
||||
Selectivity & 5.35 ± 0.24 (147) & 5.28 ± 0.32 (124) & 8488.00, 0.330 & 0.23, 0.450 \\
|
||||
Information specificity & 0.21 ± 0.02 (147) & 0.18 ± 0.02 (124) & 7883.00, 0.056 & 0.03, 0.103 \\
|
||||
Max rate & 37.74 ± 1.40 (147) & 34.65 ± 1.30 (124) & 8165.00, 0.140 & 2.31, 0.108 \\
|
||||
Information rate & 1.18 ± 0.05 (147) & 0.93 ± 0.04 (124) & 6772.00, 0.000 & 0.18, 0.008 \\
|
||||
Interspike interval cv & 2.34 ± 0.06 (147) & 2.25 ± 0.07 (124) & 8361.00, 0.242 & 0.07, 0.500 \\
|
||||
In-field mean rate & 15.79 ± 0.82 (147) & 14.46 ± 0.79 (124) & 8526.00, 0.361 & 0.67, 0.638 \\
|
||||
Out-field mean rate & 7.41 ± 0.58 (147) & 7.43 ± 0.62 (124) & 9193.00, 0.903 & 0.88, 0.456 \\
|
||||
Burst event ratio & 0.22 ± 0.01 (147) & 0.21 ± 0.01 (124) & 8548.00, 0.379 & 0.01, 0.370 \\
|
||||
Specificity & 0.45 ± 0.02 (147) & 0.42 ± 0.02 (124) & 8221.00, 0.165 & 0.03, 0.167 \\
|
||||
Speed score & 0.13 ± 0.01 (147) & 0.11 ± 0.01 (124) & 7793.00, 0.040 & 0.02, 0.046 \\
|
||||
Average rate & 8.90 ± 0.67 (129) & 8.39 ± 0.60 (102) & 6514.00, 0.898 & 0.38, 0.786 \\
|
||||
Gridness & 0.52 ± 0.03 (129) & 0.44 ± 0.04 (102) & 5681.00, 0.075 & 0.13, 0.065 \\
|
||||
Sparsity & 0.62 ± 0.02 (129) & 0.66 ± 0.02 (102) & 7486.00, 0.072 & 0.06, 0.124 \\
|
||||
Selectivity & 5.93 ± 0.28 (129) & 5.98 ± 0.37 (102) & 6254.00, 0.520 & 0.10, 0.803 \\
|
||||
Information specificity & 0.23 ± 0.02 (129) & 0.22 ± 0.02 (102) & 5573.00, 0.046 & 0.05, 0.031 \\
|
||||
Max rate & 37.44 ± 1.44 (129) & 33.72 ± 1.31 (102) & 5851.00, 0.149 & 3.66, 0.072 \\
|
||||
Information rate & 1.25 ± 0.05 (129) & 0.96 ± 0.06 (102) & 4646.00, 0.000 & 0.29, 0.001 \\
|
||||
Interspike interval cv & 2.40 ± 0.07 (129) & 2.22 ± 0.08 (102) & 5516.00, 0.035 & 0.14, 0.270 \\
|
||||
In-field mean rate & 14.72 ± 0.82 (129) & 12.94 ± 0.71 (102) & 6026.00, 0.273 & 0.76, 0.414 \\
|
||||
Out-field mean rate & 6.35 ± 0.60 (129) & 6.12 ± 0.53 (102) & 6535.00, 0.931 & 0.08, 0.921 \\
|
||||
Burst event ratio & 0.21 ± 0.01 (129) & 0.20 ± 0.01 (102) & 5792.00, 0.119 & 0.02, 0.071 \\
|
||||
Specificity & 0.48 ± 0.02 (129) & 0.46 ± 0.02 (102) & 5962.00, 0.222 & 0.06, 0.181 \\
|
||||
Speed score & 0.14 ± 0.01 (129) & 0.10 ± 0.01 (102) & 5128.00, 0.004 & 0.02, 0.008 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
|||
|
|
@ -2,18 +2,18 @@
|
|||
\toprule
|
||||
{} & Baseline & 11 Hz & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 9.82 ± 0.91 (70) & 9.28 ± 0.90 (65) & 2175.00, 0.661 & 0.18, 0.933 \\
|
||||
Gridness & 0.54 ± 0.05 (70) & 0.42 ± 0.05 (65) & 1822.00, 0.046 & 0.17, 0.052 \\
|
||||
Sparsity & 0.65 ± 0.02 (70) & 0.69 ± 0.02 (65) & 2578.00, 0.183 & 0.06, 0.147 \\
|
||||
Selectivity & 5.25 ± 0.35 (70) & 5.43 ± 0.48 (65) & 2214.00, 0.790 & 0.05, 0.961 \\
|
||||
Information specificity & 0.22 ± 0.03 (70) & 0.19 ± 0.03 (65) & 1888.00, 0.089 & 0.05, 0.020 \\
|
||||
Max rate & 36.77 ± 1.96 (70) & 33.16 ± 1.79 (65) & 1971.00, 0.181 & 3.18, 0.250 \\
|
||||
Information rate & 1.22 ± 0.06 (70) & 0.89 ± 0.06 (65) & 1431.00, 0.000 & 0.20, 0.006 \\
|
||||
Interspike interval cv & 2.37 ± 0.09 (70) & 2.24 ± 0.09 (65) & 2022.00, 0.266 & 0.12, 0.520 \\
|
||||
In-field mean rate & 15.52 ± 1.15 (70) & 13.80 ± 1.06 (65) & 2064.00, 0.354 & 0.63, 0.738 \\
|
||||
Out-field mean rate & 7.09 ± 0.77 (70) & 7.00 ± 0.80 (65) & 2236.00, 0.865 & 0.01, 0.979 \\
|
||||
Burst event ratio & 0.23 ± 0.01 (70) & 0.23 ± 0.01 (65) & 2307.00, 0.890 & 0.01, 0.732 \\
|
||||
Specificity & 0.45 ± 0.03 (70) & 0.42 ± 0.03 (65) & 2049.00, 0.321 & 0.01, 0.476 \\
|
||||
Speed score & 0.14 ± 0.01 (70) & 0.12 ± 0.01 (65) & 1939.00, 0.140 & 0.03, 0.069 \\
|
||||
Average rate & 8.96 ± 0.80 (63) & 8.80 ± 0.85 (58) & 1781.00, 0.813 & 0.04, 0.969 \\
|
||||
Gridness & 0.53 ± 0.05 (63) & 0.41 ± 0.05 (58) & 1459.00, 0.057 & 0.21, 0.038 \\
|
||||
Sparsity & 0.63 ± 0.02 (63) & 0.67 ± 0.03 (58) & 2138.00, 0.107 & 0.07, 0.126 \\
|
||||
Selectivity & 5.76 ± 0.40 (63) & 5.69 ± 0.50 (58) & 1687.00, 0.469 & 0.00, 0.981 \\
|
||||
Information specificity & 0.24 ± 0.03 (63) & 0.21 ± 0.03 (58) & 1452.00, 0.052 & 0.06, 0.031 \\
|
||||
Max rate & 37.39 ± 1.91 (63) & 33.11 ± 1.85 (58) & 1538.00, 0.134 & 4.06, 0.128 \\
|
||||
Information rate & 1.31 ± 0.08 (63) & 0.94 ± 0.08 (58) & 1143.00, 0.000 & 0.32, 0.003 \\
|
||||
Interspike interval cv & 2.39 ± 0.10 (63) & 2.19 ± 0.12 (58) & 1462.00, 0.059 & 0.18, 0.135 \\
|
||||
In-field mean rate & 14.88 ± 1.05 (63) & 13.27 ± 1.04 (58) & 1633.00, 0.315 & 0.77, 0.683 \\
|
||||
Out-field mean rate & 6.37 ± 0.67 (63) & 6.57 ± 0.77 (58) & 1795.00, 0.870 & 0.47, 0.719 \\
|
||||
Burst event ratio & 0.22 ± 0.01 (63) & 0.22 ± 0.01 (58) & 1897.00, 0.718 & 0.00, 0.824 \\
|
||||
Specificity & 0.47 ± 0.03 (63) & 0.44 ± 0.03 (58) & 1605.00, 0.250 & 0.06, 0.398 \\
|
||||
Speed score & 0.14 ± 0.01 (63) & 0.11 ± 0.01 (58) & 1378.00, 0.020 & 0.04, 0.023 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
|||
|
|
|
@ -2,18 +2,18 @@
|
|||
\toprule
|
||||
{} & Baseline & 11 Hz & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 9.82 ± 0.91 (70) & 9.28 ± 0.90 (65) & 2175.00, 0.661 & 0.18, 0.933 \\
|
||||
Gridness & 0.54 ± 0.05 (70) & 0.42 ± 0.05 (65) & 1822.00, 0.046 & 0.17, 0.052 \\
|
||||
Sparsity & 0.65 ± 0.02 (70) & 0.69 ± 0.02 (65) & 2578.00, 0.183 & 0.06, 0.147 \\
|
||||
Selectivity & 5.25 ± 0.35 (70) & 5.43 ± 0.48 (65) & 2214.00, 0.790 & 0.05, 0.961 \\
|
||||
Information specificity & 0.22 ± 0.03 (70) & 0.19 ± 0.03 (65) & 1888.00, 0.089 & 0.05, 0.020 \\
|
||||
Max rate & 36.77 ± 1.96 (70) & 33.16 ± 1.79 (65) & 1971.00, 0.181 & 3.18, 0.250 \\
|
||||
Information rate & 1.22 ± 0.06 (70) & 0.89 ± 0.06 (65) & 1431.00, 0.000 & 0.20, 0.006 \\
|
||||
Interspike interval cv & 2.37 ± 0.09 (70) & 2.24 ± 0.09 (65) & 2022.00, 0.266 & 0.12, 0.520 \\
|
||||
In-field mean rate & 15.52 ± 1.15 (70) & 13.80 ± 1.06 (65) & 2064.00, 0.354 & 0.63, 0.738 \\
|
||||
Out-field mean rate & 7.09 ± 0.77 (70) & 7.00 ± 0.80 (65) & 2236.00, 0.865 & 0.01, 0.979 \\
|
||||
Burst event ratio & 0.23 ± 0.01 (70) & 0.23 ± 0.01 (65) & 2307.00, 0.890 & 0.01, 0.732 \\
|
||||
Specificity & 0.45 ± 0.03 (70) & 0.42 ± 0.03 (65) & 2049.00, 0.321 & 0.01, 0.476 \\
|
||||
Speed score & 0.14 ± 0.01 (70) & 0.12 ± 0.01 (65) & 1939.00, 0.140 & 0.03, 0.069 \\
|
||||
Average rate & 8.96 ± 0.80 (63) & 8.80 ± 0.85 (58) & 1781.00, 0.813 & 0.04, 0.969 \\
|
||||
Gridness & 0.53 ± 0.05 (63) & 0.41 ± 0.05 (58) & 1459.00, 0.057 & 0.21, 0.038 \\
|
||||
Sparsity & 0.63 ± 0.02 (63) & 0.67 ± 0.03 (58) & 2138.00, 0.107 & 0.07, 0.126 \\
|
||||
Selectivity & 5.76 ± 0.40 (63) & 5.69 ± 0.50 (58) & 1687.00, 0.469 & 0.00, 0.981 \\
|
||||
Information specificity & 0.24 ± 0.03 (63) & 0.21 ± 0.03 (58) & 1452.00, 0.052 & 0.06, 0.031 \\
|
||||
Max rate & 37.39 ± 1.91 (63) & 33.11 ± 1.85 (58) & 1538.00, 0.134 & 4.06, 0.128 \\
|
||||
Information rate & 1.31 ± 0.08 (63) & 0.94 ± 0.08 (58) & 1143.00, 0.000 & 0.32, 0.003 \\
|
||||
Interspike interval cv & 2.39 ± 0.10 (63) & 2.19 ± 0.12 (58) & 1462.00, 0.059 & 0.18, 0.135 \\
|
||||
In-field mean rate & 14.88 ± 1.05 (63) & 13.27 ± 1.04 (58) & 1633.00, 0.315 & 0.77, 0.683 \\
|
||||
Out-field mean rate & 6.37 ± 0.67 (63) & 6.57 ± 0.77 (58) & 1795.00, 0.870 & 0.47, 0.719 \\
|
||||
Burst event ratio & 0.22 ± 0.01 (63) & 0.22 ± 0.01 (58) & 1897.00, 0.718 & 0.00, 0.824 \\
|
||||
Specificity & 0.47 ± 0.03 (63) & 0.44 ± 0.03 (58) & 1605.00, 0.250 & 0.06, 0.398 \\
|
||||
Speed score & 0.14 ± 0.01 (63) & 0.11 ± 0.01 (58) & 1378.00, 0.020 & 0.04, 0.023 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
|||
|
|
@ -2,18 +2,18 @@
|
|||
\toprule
|
||||
{} & 11 Hz & 30 Hz & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 9.28 ± 0.90 (65) & 9.94 ± 1.17 (49) & 1641.00, 0.784 & 0.09, 0.925 \\
|
||||
Gridness & 0.42 ± 0.05 (65) & 0.46 ± 0.06 (49) & 1739.00, 0.403 & 0.09, 0.420 \\
|
||||
Sparsity & 0.69 ± 0.02 (65) & 0.69 ± 0.03 (49) & 1618.00, 0.886 & 0.01, 0.660 \\
|
||||
Selectivity & 5.43 ± 0.48 (65) & 5.21 ± 0.46 (49) & 1548.00, 0.801 & 0.17, 0.835 \\
|
||||
Information specificity & 0.19 ± 0.03 (65) & 0.18 ± 0.03 (49) & 1569.00, 0.895 & 0.01, 0.783 \\
|
||||
Max rate & 33.16 ± 1.79 (65) & 34.42 ± 1.99 (49) & 1681.00, 0.614 & 1.38, 0.740 \\
|
||||
Information rate & 0.89 ± 0.06 (65) & 0.95 ± 0.07 (49) & 1701.00, 0.536 & 0.07, 0.480 \\
|
||||
Interspike interval cv & 2.24 ± 0.09 (65) & 2.24 ± 0.11 (49) & 1583.00, 0.959 & 0.05, 0.814 \\
|
||||
In-field mean rate & 13.80 ± 1.06 (65) & 14.54 ± 1.29 (49) & 1658.00, 0.710 & 0.88, 0.678 \\
|
||||
Out-field mean rate & 7.00 ± 0.80 (65) & 7.54 ± 1.06 (49) & 1631.00, 0.828 & 0.38, 0.923 \\
|
||||
Burst event ratio & 0.23 ± 0.01 (65) & 0.19 ± 0.01 (49) & 1093.00, 0.004 & 0.05, 0.004 \\
|
||||
Specificity & 0.42 ± 0.03 (65) & 0.42 ± 0.03 (49) & 1559.00, 0.850 & 0.01, 0.597 \\
|
||||
Speed score & 0.12 ± 0.01 (65) & 0.11 ± 0.01 (49) & 1459.00, 0.446 & 0.01, 0.397 \\
|
||||
Average rate & 8.80 ± 0.85 (58) & 7.61 ± 0.87 (38) & 1010.00, 0.493 & 0.23, 0.888 \\
|
||||
Gridness & 0.41 ± 0.05 (58) & 0.48 ± 0.06 (38) & 1259.00, 0.241 & 0.13, 0.096 \\
|
||||
Sparsity & 0.67 ± 0.03 (58) & 0.64 ± 0.03 (38) & 1002.00, 0.456 & 0.04, 0.569 \\
|
||||
Selectivity & 5.69 ± 0.50 (58) & 6.42 ± 0.60 (38) & 1260.00, 0.238 & 0.85, 0.332 \\
|
||||
Information specificity & 0.21 ± 0.03 (58) & 0.22 ± 0.03 (38) & 1231.00, 0.336 & 0.01, 0.729 \\
|
||||
Max rate & 33.11 ± 1.85 (58) & 33.49 ± 1.89 (38) & 1136.00, 0.802 & 0.07, 0.991 \\
|
||||
Information rate & 0.94 ± 0.08 (58) & 0.98 ± 0.09 (38) & 1171.00, 0.608 & 0.01, 0.783 \\
|
||||
Interspike interval cv & 2.19 ± 0.12 (58) & 2.23 ± 0.11 (38) & 1228.00, 0.347 & 0.17, 0.334 \\
|
||||
In-field mean rate & 13.27 ± 1.04 (58) & 12.21 ± 0.98 (38) & 1058.00, 0.744 & 0.97, 0.636 \\
|
||||
Out-field mean rate & 6.57 ± 0.77 (58) & 5.36 ± 0.73 (38) & 1019.00, 0.537 & 0.04, 0.961 \\
|
||||
Burst event ratio & 0.22 ± 0.01 (58) & 0.16 ± 0.01 (38) & 552.00, 0.000 & 0.06, 0.000 \\
|
||||
Specificity & 0.44 ± 0.03 (58) & 0.48 ± 0.04 (38) & 1233.00, 0.328 & 0.07, 0.385 \\
|
||||
Speed score & 0.11 ± 0.01 (58) & 0.11 ± 0.01 (38) & 1022.00, 0.551 & 0.02, 0.149 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
|||
|
|
|
@ -2,18 +2,18 @@
|
|||
\toprule
|
||||
{} & 11 Hz & 30 Hz & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 9.28 ± 0.90 (65) & 9.94 ± 1.17 (49) & 1641.00, 0.784 & 0.09, 0.925 \\
|
||||
Gridness & 0.42 ± 0.05 (65) & 0.46 ± 0.06 (49) & 1739.00, 0.403 & 0.09, 0.420 \\
|
||||
Sparsity & 0.69 ± 0.02 (65) & 0.69 ± 0.03 (49) & 1618.00, 0.886 & 0.01, 0.660 \\
|
||||
Selectivity & 5.43 ± 0.48 (65) & 5.21 ± 0.46 (49) & 1548.00, 0.801 & 0.17, 0.835 \\
|
||||
Information specificity & 0.19 ± 0.03 (65) & 0.18 ± 0.03 (49) & 1569.00, 0.895 & 0.01, 0.783 \\
|
||||
Max rate & 33.16 ± 1.79 (65) & 34.42 ± 1.99 (49) & 1681.00, 0.614 & 1.38, 0.740 \\
|
||||
Information rate & 0.89 ± 0.06 (65) & 0.95 ± 0.07 (49) & 1701.00, 0.536 & 0.07, 0.480 \\
|
||||
Interspike interval cv & 2.24 ± 0.09 (65) & 2.24 ± 0.11 (49) & 1583.00, 0.959 & 0.05, 0.814 \\
|
||||
In-field mean rate & 13.80 ± 1.06 (65) & 14.54 ± 1.29 (49) & 1658.00, 0.710 & 0.88, 0.678 \\
|
||||
Out-field mean rate & 7.00 ± 0.80 (65) & 7.54 ± 1.06 (49) & 1631.00, 0.828 & 0.38, 0.923 \\
|
||||
Burst event ratio & 0.23 ± 0.01 (65) & 0.19 ± 0.01 (49) & 1093.00, 0.004 & 0.05, 0.004 \\
|
||||
Specificity & 0.42 ± 0.03 (65) & 0.42 ± 0.03 (49) & 1559.00, 0.850 & 0.01, 0.597 \\
|
||||
Speed score & 0.12 ± 0.01 (65) & 0.11 ± 0.01 (49) & 1459.00, 0.446 & 0.01, 0.397 \\
|
||||
Average rate & 8.80 ± 0.85 (58) & 7.61 ± 0.87 (38) & 1010.00, 0.493 & 0.23, 0.888 \\
|
||||
Gridness & 0.41 ± 0.05 (58) & 0.48 ± 0.06 (38) & 1259.00, 0.241 & 0.13, 0.096 \\
|
||||
Sparsity & 0.67 ± 0.03 (58) & 0.64 ± 0.03 (38) & 1002.00, 0.456 & 0.04, 0.569 \\
|
||||
Selectivity & 5.69 ± 0.50 (58) & 6.42 ± 0.60 (38) & 1260.00, 0.238 & 0.85, 0.332 \\
|
||||
Information specificity & 0.21 ± 0.03 (58) & 0.22 ± 0.03 (38) & 1231.00, 0.336 & 0.01, 0.729 \\
|
||||
Max rate & 33.11 ± 1.85 (58) & 33.49 ± 1.89 (38) & 1136.00, 0.802 & 0.07, 0.991 \\
|
||||
Information rate & 0.94 ± 0.08 (58) & 0.98 ± 0.09 (38) & 1171.00, 0.608 & 0.01, 0.783 \\
|
||||
Interspike interval cv & 2.19 ± 0.12 (58) & 2.23 ± 0.11 (38) & 1228.00, 0.347 & 0.17, 0.334 \\
|
||||
In-field mean rate & 13.27 ± 1.04 (58) & 12.21 ± 0.98 (38) & 1058.00, 0.744 & 0.97, 0.636 \\
|
||||
Out-field mean rate & 6.57 ± 0.77 (58) & 5.36 ± 0.73 (38) & 1019.00, 0.537 & 0.04, 0.961 \\
|
||||
Burst event ratio & 0.22 ± 0.01 (58) & 0.16 ± 0.01 (38) & 552.00, 0.000 & 0.06, 0.000 \\
|
||||
Specificity & 0.44 ± 0.03 (58) & 0.48 ± 0.04 (38) & 1233.00, 0.328 & 0.07, 0.385 \\
|
||||
Speed score & 0.11 ± 0.01 (58) & 0.11 ± 0.01 (38) & 1022.00, 0.551 & 0.02, 0.149 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
|||
|
|
@ -2,18 +2,18 @@
|
|||
\toprule
|
||||
{} & Baseline & 30 Hz & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 10.08 ± 1.05 (61) & 9.94 ± 1.17 (49) & 1491.00, 0.986 & 0.24, 0.763 \\
|
||||
Gridness & 0.53 ± 0.05 (61) & 0.46 ± 0.06 (49) & 1342.00, 0.361 & 0.08, 0.289 \\
|
||||
Sparsity & 0.67 ± 0.02 (61) & 0.69 ± 0.03 (49) & 1622.00, 0.445 & 0.03, 0.466 \\
|
||||
Selectivity & 5.34 ± 0.38 (61) & 5.21 ± 0.46 (49) & 1372.00, 0.463 & 0.37, 0.420 \\
|
||||
Information specificity & 0.19 ± 0.02 (61) & 0.18 ± 0.03 (49) & 1380.00, 0.493 & 0.01, 0.725 \\
|
||||
Max rate & 37.61 ± 2.31 (61) & 34.42 ± 1.99 (49) & 1342.00, 0.361 & 2.37, 0.351 \\
|
||||
Information rate & 1.08 ± 0.08 (61) & 0.95 ± 0.07 (49) & 1321.00, 0.298 & 0.14, 0.413 \\
|
||||
Interspike interval cv & 2.28 ± 0.09 (61) & 2.24 ± 0.11 (49) & 1419.00, 0.652 & 0.06, 0.740 \\
|
||||
In-field mean rate & 15.61 ± 1.32 (61) & 14.54 ± 1.29 (49) & 1418.00, 0.648 & 0.64, 0.675 \\
|
||||
Out-field mean rate & 7.65 ± 0.96 (61) & 7.54 ± 1.06 (49) & 1487.00, 0.966 & 0.37, 0.789 \\
|
||||
Burst event ratio & 0.21 ± 0.01 (61) & 0.19 ± 0.01 (49) & 1241.00, 0.128 & 0.04, 0.037 \\
|
||||
Specificity & 0.42 ± 0.03 (61) & 0.42 ± 0.03 (49) & 1429.00, 0.696 & 0.03, 0.495 \\
|
||||
Speed score & 0.12 ± 0.01 (61) & 0.11 ± 0.01 (49) & 1335.00, 0.339 & 0.01, 0.545 \\
|
||||
Average rate & 8.29 ± 0.87 (52) & 7.61 ± 0.87 (38) & 958.00, 0.810 & 0.27, 0.805 \\
|
||||
Gridness & 0.54 ± 0.04 (52) & 0.48 ± 0.06 (38) & 914.00, 0.548 & 0.04, 0.608 \\
|
||||
Sparsity & 0.63 ± 0.03 (52) & 0.64 ± 0.03 (38) & 1040.00, 0.674 & 0.06, 0.401 \\
|
||||
Selectivity & 5.96 ± 0.46 (52) & 6.42 ± 0.60 (38) & 1019.00, 0.803 & 0.20, 0.850 \\
|
||||
Information specificity & 0.21 ± 0.02 (52) & 0.22 ± 0.03 (38) & 950.00, 0.759 & 0.04, 0.505 \\
|
||||
Max rate & 36.27 ± 2.34 (52) & 33.49 ± 1.89 (38) & 943.00, 0.716 & 2.90, 0.558 \\
|
||||
Information rate & 1.13 ± 0.08 (52) & 0.98 ± 0.09 (38) & 827.00, 0.190 & 0.07, 0.332 \\
|
||||
Interspike interval cv & 2.37 ± 0.09 (52) & 2.23 ± 0.11 (38) & 869.00, 0.333 & 0.17, 0.470 \\
|
||||
In-field mean rate & 13.79 ± 1.12 (52) & 12.21 ± 0.98 (38) & 912.00, 0.537 & 1.06, 0.452 \\
|
||||
Out-field mean rate & 5.80 ± 0.72 (52) & 5.36 ± 0.73 (38) & 959.00, 0.816 & 0.13, 0.916 \\
|
||||
Burst event ratio & 0.20 ± 0.01 (52) & 0.16 ± 0.01 (38) & 676.00, 0.011 & 0.05, 0.007 \\
|
||||
Specificity & 0.47 ± 0.03 (52) & 0.48 ± 0.04 (38) & 976.00, 0.925 & 0.00, 0.985 \\
|
||||
Speed score & 0.12 ± 0.01 (52) & 0.11 ± 0.01 (38) & 784.00, 0.096 & 0.01, 0.241 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
|||
|
|
|
@ -2,18 +2,18 @@
|
|||
\toprule
|
||||
{} & Baseline & 30 Hz & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 10.08 ± 1.05 (61) & 9.94 ± 1.17 (49) & 1491.00, 0.986 & 0.24, 0.763 \\
|
||||
Gridness & 0.53 ± 0.05 (61) & 0.46 ± 0.06 (49) & 1342.00, 0.361 & 0.08, 0.289 \\
|
||||
Sparsity & 0.67 ± 0.02 (61) & 0.69 ± 0.03 (49) & 1622.00, 0.445 & 0.03, 0.466 \\
|
||||
Selectivity & 5.34 ± 0.38 (61) & 5.21 ± 0.46 (49) & 1372.00, 0.463 & 0.37, 0.420 \\
|
||||
Information specificity & 0.19 ± 0.02 (61) & 0.18 ± 0.03 (49) & 1380.00, 0.493 & 0.01, 0.725 \\
|
||||
Max rate & 37.61 ± 2.31 (61) & 34.42 ± 1.99 (49) & 1342.00, 0.361 & 2.37, 0.351 \\
|
||||
Information rate & 1.08 ± 0.08 (61) & 0.95 ± 0.07 (49) & 1321.00, 0.298 & 0.14, 0.413 \\
|
||||
Interspike interval cv & 2.28 ± 0.09 (61) & 2.24 ± 0.11 (49) & 1419.00, 0.652 & 0.06, 0.740 \\
|
||||
In-field mean rate & 15.61 ± 1.32 (61) & 14.54 ± 1.29 (49) & 1418.00, 0.648 & 0.64, 0.675 \\
|
||||
Out-field mean rate & 7.65 ± 0.96 (61) & 7.54 ± 1.06 (49) & 1487.00, 0.966 & 0.37, 0.789 \\
|
||||
Burst event ratio & 0.21 ± 0.01 (61) & 0.19 ± 0.01 (49) & 1241.00, 0.128 & 0.04, 0.037 \\
|
||||
Specificity & 0.42 ± 0.03 (61) & 0.42 ± 0.03 (49) & 1429.00, 0.696 & 0.03, 0.495 \\
|
||||
Speed score & 0.12 ± 0.01 (61) & 0.11 ± 0.01 (49) & 1335.00, 0.339 & 0.01, 0.545 \\
|
||||
Average rate & 8.29 ± 0.87 (52) & 7.61 ± 0.87 (38) & 958.00, 0.810 & 0.27, 0.805 \\
|
||||
Gridness & 0.54 ± 0.04 (52) & 0.48 ± 0.06 (38) & 914.00, 0.548 & 0.04, 0.608 \\
|
||||
Sparsity & 0.63 ± 0.03 (52) & 0.64 ± 0.03 (38) & 1040.00, 0.674 & 0.06, 0.401 \\
|
||||
Selectivity & 5.96 ± 0.46 (52) & 6.42 ± 0.60 (38) & 1019.00, 0.803 & 0.20, 0.850 \\
|
||||
Information specificity & 0.21 ± 0.02 (52) & 0.22 ± 0.03 (38) & 950.00, 0.759 & 0.04, 0.505 \\
|
||||
Max rate & 36.27 ± 2.34 (52) & 33.49 ± 1.89 (38) & 943.00, 0.716 & 2.90, 0.558 \\
|
||||
Information rate & 1.13 ± 0.08 (52) & 0.98 ± 0.09 (38) & 827.00, 0.190 & 0.07, 0.332 \\
|
||||
Interspike interval cv & 2.37 ± 0.09 (52) & 2.23 ± 0.11 (38) & 869.00, 0.333 & 0.17, 0.470 \\
|
||||
In-field mean rate & 13.79 ± 1.12 (52) & 12.21 ± 0.98 (38) & 912.00, 0.537 & 1.06, 0.452 \\
|
||||
Out-field mean rate & 5.80 ± 0.72 (52) & 5.36 ± 0.73 (38) & 959.00, 0.816 & 0.13, 0.916 \\
|
||||
Burst event ratio & 0.20 ± 0.01 (52) & 0.16 ± 0.01 (38) & 676.00, 0.011 & 0.05, 0.007 \\
|
||||
Specificity & 0.47 ± 0.03 (52) & 0.48 ± 0.04 (38) & 976.00, 0.925 & 0.00, 0.985 \\
|
||||
Speed score & 0.12 ± 0.01 (52) & 0.11 ± 0.01 (38) & 784.00, 0.096 & 0.01, 0.241 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,19 @@
|
|||
\begin{tabular}{lllll}
|
||||
\toprule
|
||||
{} & Baseline I & 30 Hz & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 8.96 ± 0.80 (63) & 7.61 ± 0.87 (38) & 1081.00, 0.418 & 0.27, 0.803 \\
|
||||
Gridness & 0.53 ± 0.05 (63) & 0.48 ± 0.06 (38) & 1094.00, 0.472 & 0.08, 0.363 \\
|
||||
Sparsity & 0.63 ± 0.02 (63) & 0.64 ± 0.03 (38) & 1261.00, 0.656 & 0.03, 0.641 \\
|
||||
Selectivity & 5.76 ± 0.40 (63) & 6.42 ± 0.60 (38) & 1276.00, 0.582 & 0.86, 0.283 \\
|
||||
Information specificity & 0.24 ± 0.03 (63) & 0.22 ± 0.03 (38) & 1076.00, 0.398 & 0.05, 0.161 \\
|
||||
Max rate & 37.39 ± 1.91 (63) & 33.49 ± 1.89 (38) & 1027.00, 0.235 & 3.99, 0.182 \\
|
||||
Information rate & 1.31 ± 0.08 (63) & 0.98 ± 0.09 (38) & 797.00, 0.005 & 0.32, 0.045 \\
|
||||
Interspike interval cv & 2.39 ± 0.10 (63) & 2.23 ± 0.11 (38) & 1100.00, 0.499 & 0.01, 0.993 \\
|
||||
In-field mean rate & 14.88 ± 1.05 (63) & 12.21 ± 0.98 (38) & 1018.00, 0.211 & 1.74, 0.272 \\
|
||||
Out-field mean rate & 6.37 ± 0.67 (63) & 5.36 ± 0.73 (38) & 1079.00, 0.410 & 0.51, 0.644 \\
|
||||
Burst event ratio & 0.22 ± 0.01 (63) & 0.16 ± 0.01 (38) & 675.00, 0.000 & 0.05, 0.006 \\
|
||||
Specificity & 0.47 ± 0.03 (63) & 0.48 ± 0.04 (38) & 1206.00, 0.952 & 0.01, 0.869 \\
|
||||
Speed score & 0.14 ± 0.01 (63) & 0.11 ± 0.01 (38) & 835.00, 0.011 & 0.06, 0.005 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
@ -0,0 +1,19 @@
|
|||
\begin{tabular}{lllll}
|
||||
\toprule
|
||||
{} & Baseline I & 30 Hz & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 8.96 ± 0.80 (63) & 7.61 ± 0.87 (38) & 1081.00, 0.418 & 0.27, 0.803 \\
|
||||
Gridness & 0.53 ± 0.05 (63) & 0.48 ± 0.06 (38) & 1094.00, 0.472 & 0.08, 0.363 \\
|
||||
Sparsity & 0.63 ± 0.02 (63) & 0.64 ± 0.03 (38) & 1261.00, 0.656 & 0.03, 0.641 \\
|
||||
Selectivity & 5.76 ± 0.40 (63) & 6.42 ± 0.60 (38) & 1276.00, 0.582 & 0.86, 0.283 \\
|
||||
Information specificity & 0.24 ± 0.03 (63) & 0.22 ± 0.03 (38) & 1076.00, 0.398 & 0.05, 0.161 \\
|
||||
Max rate & 37.39 ± 1.91 (63) & 33.49 ± 1.89 (38) & 1027.00, 0.235 & 3.99, 0.182 \\
|
||||
Information rate & 1.31 ± 0.08 (63) & 0.98 ± 0.09 (38) & 797.00, 0.005 & 0.32, 0.045 \\
|
||||
Interspike interval cv & 2.39 ± 0.10 (63) & 2.23 ± 0.11 (38) & 1100.00, 0.499 & 0.01, 0.993 \\
|
||||
In-field mean rate & 14.88 ± 1.05 (63) & 12.21 ± 0.98 (38) & 1018.00, 0.211 & 1.74, 0.272 \\
|
||||
Out-field mean rate & 6.37 ± 0.67 (63) & 5.36 ± 0.73 (38) & 1079.00, 0.410 & 0.51, 0.644 \\
|
||||
Burst event ratio & 0.22 ± 0.01 (63) & 0.16 ± 0.01 (38) & 675.00, 0.000 & 0.05, 0.006 \\
|
||||
Specificity & 0.47 ± 0.03 (63) & 0.48 ± 0.04 (38) & 1206.00, 0.952 & 0.01, 0.869 \\
|
||||
Speed score & 0.14 ± 0.01 (63) & 0.11 ± 0.01 (38) & 835.00, 0.011 & 0.06, 0.005 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
@ -2,18 +2,18 @@
|
|||
\toprule
|
||||
{} & Baseline I & Baseline II & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 9.82 ± 0.91 (70) & 10.08 ± 1.05 (61) & 2166.00, 0.888 & 0.15, 0.852 \\
|
||||
Gridness & 0.54 ± 0.05 (70) & 0.53 ± 0.05 (61) & 2158.00, 0.917 & 0.00, 0.983 \\
|
||||
Sparsity & 0.65 ± 0.02 (70) & 0.67 ± 0.02 (61) & 2001.00, 0.538 & 0.04, 0.361 \\
|
||||
Selectivity & 5.25 ± 0.35 (70) & 5.34 ± 0.38 (61) & 2062.00, 0.738 & 0.25, 0.594 \\
|
||||
Information specificity & 0.22 ± 0.03 (70) & 0.19 ± 0.02 (61) & 2329.00, 0.372 & 0.05, 0.143 \\
|
||||
Max rate & 36.77 ± 1.96 (70) & 37.61 ± 2.31 (61) & 2088.00, 0.830 & 0.58, 0.784 \\
|
||||
Information rate & 1.22 ± 0.06 (70) & 1.08 ± 0.08 (61) & 2501.00, 0.092 & 0.14, 0.151 \\
|
||||
Interspike interval cv & 2.37 ± 0.09 (70) & 2.28 ± 0.09 (61) & 2257.00, 0.575 & 0.01, 0.928 \\
|
||||
In-field mean rate & 15.52 ± 1.15 (70) & 15.61 ± 1.32 (61) & 2162.00, 0.903 & 0.87, 0.724 \\
|
||||
Out-field mean rate & 7.09 ± 0.77 (70) & 7.65 ± 0.96 (61) & 2115.00, 0.928 & 0.02, 0.986 \\
|
||||
Burst event ratio & 0.23 ± 0.01 (70) & 0.21 ± 0.01 (61) & 2299.00, 0.451 & 0.00, 0.830 \\
|
||||
Specificity & 0.45 ± 0.03 (70) & 0.42 ± 0.03 (61) & 2245.00, 0.613 & 0.01, 0.921 \\
|
||||
Speed score & 0.14 ± 0.01 (70) & 0.12 ± 0.01 (61) & 2423.00, 0.185 & 0.04, 0.042 \\
|
||||
Average rate & 8.96 ± 0.80 (63) & 8.29 ± 0.87 (52) & 1756.00, 0.509 & 0.55, 0.674 \\
|
||||
Gridness & 0.53 ± 0.05 (63) & 0.54 ± 0.04 (52) & 1664.00, 0.886 & 0.04, 0.546 \\
|
||||
Sparsity & 0.63 ± 0.02 (63) & 0.63 ± 0.03 (52) & 1652.00, 0.940 & 0.03, 0.651 \\
|
||||
Selectivity & 5.76 ± 0.40 (63) & 5.96 ± 0.46 (52) & 1542.00, 0.592 & 0.66, 0.361 \\
|
||||
Information specificity & 0.24 ± 0.03 (63) & 0.21 ± 0.02 (52) & 1718.00, 0.655 & 0.01, 0.813 \\
|
||||
Max rate & 37.39 ± 1.91 (63) & 36.27 ± 2.34 (52) & 1757.00, 0.505 & 1.09, 0.612 \\
|
||||
Information rate & 1.31 ± 0.08 (63) & 1.13 ± 0.08 (52) & 1929.00, 0.103 & 0.25, 0.133 \\
|
||||
Interspike interval cv & 2.39 ± 0.10 (63) & 2.37 ± 0.09 (52) & 1572.00, 0.713 & 0.16, 0.479 \\
|
||||
In-field mean rate & 14.88 ± 1.05 (63) & 13.79 ± 1.12 (52) & 1763.00, 0.484 & 0.68, 0.693 \\
|
||||
Out-field mean rate & 6.37 ± 0.67 (63) & 5.80 ± 0.72 (52) & 1737.00, 0.580 & 0.38, 0.572 \\
|
||||
Burst event ratio & 0.22 ± 0.01 (63) & 0.20 ± 0.01 (52) & 1834.00, 0.272 & 0.01, 0.671 \\
|
||||
Specificity & 0.47 ± 0.03 (63) & 0.47 ± 0.03 (52) & 1588.00, 0.781 & 0.01, 0.772 \\
|
||||
Speed score & 0.14 ± 0.01 (63) & 0.12 ± 0.01 (52) & 1943.00, 0.087 & 0.04, 0.016 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
|||
|
|
|
@ -2,18 +2,18 @@
|
|||
\toprule
|
||||
{} & Baseline I & Baseline II & MWU & PRS \\
|
||||
\midrule
|
||||
Average rate & 9.82 ± 0.91 (70) & 10.08 ± 1.05 (61) & 2166.00, 0.888 & 0.15, 0.852 \\
|
||||
Gridness & 0.54 ± 0.05 (70) & 0.53 ± 0.05 (61) & 2158.00, 0.917 & 0.00, 0.983 \\
|
||||
Sparsity & 0.65 ± 0.02 (70) & 0.67 ± 0.02 (61) & 2001.00, 0.538 & 0.04, 0.361 \\
|
||||
Selectivity & 5.25 ± 0.35 (70) & 5.34 ± 0.38 (61) & 2062.00, 0.738 & 0.25, 0.594 \\
|
||||
Information specificity & 0.22 ± 0.03 (70) & 0.19 ± 0.02 (61) & 2329.00, 0.372 & 0.05, 0.143 \\
|
||||
Max rate & 36.77 ± 1.96 (70) & 37.61 ± 2.31 (61) & 2088.00, 0.830 & 0.58, 0.784 \\
|
||||
Information rate & 1.22 ± 0.06 (70) & 1.08 ± 0.08 (61) & 2501.00, 0.092 & 0.14, 0.151 \\
|
||||
Interspike interval cv & 2.37 ± 0.09 (70) & 2.28 ± 0.09 (61) & 2257.00, 0.575 & 0.01, 0.928 \\
|
||||
In-field mean rate & 15.52 ± 1.15 (70) & 15.61 ± 1.32 (61) & 2162.00, 0.903 & 0.87, 0.724 \\
|
||||
Out-field mean rate & 7.09 ± 0.77 (70) & 7.65 ± 0.96 (61) & 2115.00, 0.928 & 0.02, 0.986 \\
|
||||
Burst event ratio & 0.23 ± 0.01 (70) & 0.21 ± 0.01 (61) & 2299.00, 0.451 & 0.00, 0.830 \\
|
||||
Specificity & 0.45 ± 0.03 (70) & 0.42 ± 0.03 (61) & 2245.00, 0.613 & 0.01, 0.921 \\
|
||||
Speed score & 0.14 ± 0.01 (70) & 0.12 ± 0.01 (61) & 2423.00, 0.185 & 0.04, 0.042 \\
|
||||
Average rate & 8.96 ± 0.80 (63) & 8.29 ± 0.87 (52) & 1756.00, 0.509 & 0.55, 0.674 \\
|
||||
Gridness & 0.53 ± 0.05 (63) & 0.54 ± 0.04 (52) & 1664.00, 0.886 & 0.04, 0.546 \\
|
||||
Sparsity & 0.63 ± 0.02 (63) & 0.63 ± 0.03 (52) & 1652.00, 0.940 & 0.03, 0.651 \\
|
||||
Selectivity & 5.76 ± 0.40 (63) & 5.96 ± 0.46 (52) & 1542.00, 0.592 & 0.66, 0.361 \\
|
||||
Information specificity & 0.24 ± 0.03 (63) & 0.21 ± 0.02 (52) & 1718.00, 0.655 & 0.01, 0.813 \\
|
||||
Max rate & 37.39 ± 1.91 (63) & 36.27 ± 2.34 (52) & 1757.00, 0.505 & 1.09, 0.612 \\
|
||||
Information rate & 1.31 ± 0.08 (63) & 1.13 ± 0.08 (52) & 1929.00, 0.103 & 0.25, 0.133 \\
|
||||
Interspike interval cv & 2.39 ± 0.10 (63) & 2.37 ± 0.09 (52) & 1572.00, 0.713 & 0.16, 0.479 \\
|
||||
In-field mean rate & 14.88 ± 1.05 (63) & 13.79 ± 1.12 (52) & 1763.00, 0.484 & 0.68, 0.693 \\
|
||||
Out-field mean rate & 6.37 ± 0.67 (63) & 5.80 ± 0.72 (52) & 1737.00, 0.580 & 0.38, 0.572 \\
|
||||
Burst event ratio & 0.22 ± 0.01 (63) & 0.20 ± 0.01 (52) & 1834.00, 0.272 & 0.01, 0.671 \\
|
||||
Specificity & 0.47 ± 0.03 (63) & 0.47 ± 0.03 (52) & 1588.00, 0.781 & 0.01, 0.772 \\
|
||||
Speed score & 0.14 ± 0.01 (63) & 0.12 ± 0.01 (52) & 1943.00, 0.087 & 0.04, 0.016 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
|
|
|||
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