|
|
@@ -2,7 +2,7 @@
|
|
|
"cells": [
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 123,
|
|
|
+ "execution_count": 1,
|
|
|
"id": "7dbf62e3",
|
|
|
"metadata": {},
|
|
|
"outputs": [],
|
|
|
@@ -29,7 +29,7 @@
|
|
|
},
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 124,
|
|
|
+ "execution_count": 2,
|
|
|
"id": "220a45cd",
|
|
|
"metadata": {},
|
|
|
"outputs": [
|
|
|
@@ -112,7 +112,7 @@
|
|
|
},
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 125,
|
|
|
+ "execution_count": 3,
|
|
|
"id": "2ccab1dd",
|
|
|
"metadata": {},
|
|
|
"outputs": [],
|
|
|
@@ -146,7 +146,7 @@
|
|
|
},
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 126,
|
|
|
+ "execution_count": 4,
|
|
|
"id": "a54d07da",
|
|
|
"metadata": {},
|
|
|
"outputs": [],
|
|
|
@@ -167,7 +167,7 @@
|
|
|
},
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 127,
|
|
|
+ "execution_count": 5,
|
|
|
"id": "3400d646",
|
|
|
"metadata": {},
|
|
|
"outputs": [
|
|
|
@@ -177,7 +177,7 @@
|
|
|
"((5067, 200), (73, 5))"
|
|
|
]
|
|
|
},
|
|
|
- "execution_count": 127,
|
|
|
+ "execution_count": 5,
|
|
|
"metadata": {},
|
|
|
"output_type": "execute_result"
|
|
|
}
|
|
|
@@ -199,7 +199,7 @@
|
|
|
},
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 41,
|
|
|
+ "execution_count": 6,
|
|
|
"id": "5d287f18",
|
|
|
"metadata": {},
|
|
|
"outputs": [],
|
|
|
@@ -216,7 +216,7 @@
|
|
|
},
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 42,
|
|
|
+ "execution_count": 7,
|
|
|
"id": "696df740",
|
|
|
"metadata": {
|
|
|
"scrolled": true
|
|
|
@@ -228,7 +228,7 @@
|
|
|
"Text(0, 0.5, 'LFP, uv')"
|
|
|
]
|
|
|
},
|
|
|
- "execution_count": 42,
|
|
|
+ "execution_count": 7,
|
|
|
"metadata": {},
|
|
|
"output_type": "execute_result"
|
|
|
},
|
|
|
@@ -265,7 +265,9 @@
|
|
|
" \n",
|
|
|
" Example 1 second of raw LFP recording (channel 2) of the SIT frequency discrimination session.\n",
|
|
|
" Sound stimulations are highlighted in gray. \n",
|
|
|
- " Note very dynamic structure of AEP responses. Note that the AEP to the second pulse is almost absent."
|
|
|
+ " Note very dynamic structure of AEP responses. \n",
|
|
|
+ " Note that the AEP to the second pulse is almost absent.\n",
|
|
|
+ " Note the P1 / N1 components are almost always there, however P2 / P3 components may be absent (2-4th AEPs)."
|
|
|
]
|
|
|
},
|
|
|
{
|
|
|
@@ -278,7 +280,7 @@
|
|
|
},
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 128,
|
|
|
+ "execution_count": 8,
|
|
|
"id": "ddbdc8ae",
|
|
|
"metadata": {
|
|
|
"scrolled": true
|
|
|
@@ -385,9 +387,11 @@
|
|
|
" \n",
|
|
|
" Dynamics of AEP components (N1, P1, P2, P3) for selected period from the example session.\n",
|
|
|
" Blue: z-scored raw AEP metric values; black: z-scored smoothed AEP metric values, green shadings: time in target island.\n",
|
|
|
+ " P1, N1, P2, P3 components are computed as just areas under curve (AUC).\n",
|
|
|
+ " TBD: find more ways to compute AEP metrics (peak-to-trough ratio, etc.) and select the best.\n",
|
|
|
" Inter-trial intervals (strait lines) have no AEP data, just hard to plot without them.\n",
|
|
|
" Note the raw metric values are very noizy and require smoothing.\n",
|
|
|
- " Note in this example P1, P2 are strongly modulated by target frequency."
|
|
|
+ " Note in this example P2, P3 are strongly modulated by target frequency."
|
|
|
]
|
|
|
},
|
|
|
{
|
|
|
@@ -592,11 +596,16 @@
|
|
|
"\n",
|
|
|
" Difference of the AEP components in target / background for an example session.\n",
|
|
|
" Note almost all the components are significantly (Kolmogorov-Smirnov 2-sample) different between target / background sounds.\n",
|
|
|
+ " \n",
|
|
|
+ " Discussion: simple different frequency tuning might be an explanation. But then:\n",
|
|
|
+ " - we wouldn't see a change between successful / missed target entrances (see next plot)\n",
|
|
|
+ " - we shouldn't see such variance in the dynamics WITHIN the same frequency presentation;\n",
|
|
|
+ " So we hypothesize that it is due to behavioral relevance and top-down modulation.\n",
|
|
|
+ " \n",
|
|
|
" Very important: it doesn't hold for all the sessions! In this example animal was very engaged in the task.\n",
|
|
|
" In many sessions animal engagement is not that clear, so just averaging doesn't look that good.\n",
|
|
|
- " TBD: compute across sessions.\n",
|
|
|
- " \n",
|
|
|
- " Discussion: simple target frequency tuning might be an explanation. But then we shouldn't see such variance in the dynamics WITHIN the same frequency presentation. So more likely that it is due to behavioral relevance and top-down modulation."
|
|
|
+ " Let's discuss this.\n",
|
|
|
+ " TBD: compute across sessions."
|
|
|
]
|
|
|
},
|
|
|
{
|
asobolev