Luận án Research on establishing the neural stimulation system and apply for evaluating the spatial response of hippocampal place neurons

Start
Chammui
End
Figure 2.11. Flow chart of the NPT test. 
in which: 
- Pt: number of rewards. 
- maxPt: the maximum number of rewards in a single episode (conditions to 
stop the program). 
- chammui: When the mice touches the nose with the sensor. 
46 
- t: timer variable. 
- maxT: maximum time for one exercise (condition to stop the program). 
- ptDelta: reward for the delta period. 
- tDelta: variable counting in delta time. 
- delta: the amount of time to count the reward. 
- countInterVal: number of stops to adjust the parameter. 
- Interval: interval to stop for parameter adjustment. 
The flowchart of the excitation algorithm for the NPT test is explained by 
the following steps: 
 Step 1: Initialize values: 
- Pt: number of rewards assigned to 0. 
- chammui: the number of mouse touches the nose assigned to 0. 
- T: timer variable is assigned to 0. 
- tDelta: variable counting in delta time is assigned to 0. 
- delta: initialize the variable to count the number of rewards in a period of time. 
- countInterVal: number of stops to adjust the parameter is set to 0. 
- interval: interval to stop parameter adjustment. 
- maxPt: the maximum number of rewards once a set, is set in advance 
(conditions stop the program). 
- maxT: time limit once a set, set before (conditions stop the program). 
 Step 2: 
- Increase timer variable. 
- The data reading unit reads the pulses sent from the sensor to meet the 
condition of the nose poke. 
 Step 3: 
If mouse pokes nose, reward count is returned chammui = 0, reward is 
cumulative, the number of rewards in the delta time period is cumulative. 
 Step 4: 
47 
- If the timer by tDelta is equal to the counting time, save the number of 
rewards received and reset the reward and time counter. 
- Also compare the time with the Interval time and count down the Interval time. 
 Step 5: 
If pt = maxPt or t = maxT then the program is stopped, if else check the 
Interval, count down and repeat Step 2. 
2.3.2. Model and algorithm of electrical stimulation of neurons with spatial 
response tests 
In order to assess the response of neurons to electrical impulse stimulation, 
it is necessary to develop related exercises to assess spatial response through 
animal behavior, the dissertation builds a stimulation algorithm through post 
spatial response exercises: distant movement task (DMT), random-reward 
place search task (RRPST) and Place learning task (PLT). 
 Establish virtual open field: 
The mice performed the exercise in an open environment, a black painted 
cylindrical box with a diameter of 80cm, a height of 25cm, placed on a rotating 
cylinder 80cm high above the floor so that it could be rotated under 
experimental conditions. The mouse is mounted with an LED to track 
coordinates as well as running paths in an open environment (Open Field: OF). 
The virtual OF program has a specific diameter and center. Set the fixed leds 
to OF and rotate a closed circle. Two-dimensional coordinates (X, Y) on the 
position of the leds provided by Cineplex version 2.0 (Plexon Inc., USA) with 
a frequency of 30FPS. Determine the values 𝑋𝑚𝑖𝑛, 𝑋𝑚𝑎𝑥, 𝑌𝑚𝑖𝑛, 𝑌𝑚𝑎𝑥 as unit 
of pixel of led in virtual OF. 
- Determine diameter of virtual OF: Compare 𝑑𝑋 = 𝑋𝑚𝑎𝑥 − 𝑋𝑚𝑖𝑛 with 𝑑𝑌 =
𝑌𝑚𝑎𝑥 − 𝑌𝑚𝑖𝑛. The larger is the diameter of virtual OF. 
- Determine cm – pixel ratio: real OF diameter is 80cm, virtual OF 
diameter is as above. In this study, the cm-pixel ratio is 9,35. 
48 
 Description: 
- Mice move in an open environment. 
- The coordinates of the mouse in an open environment are determined by 
the program in real time. 
 Test stopping conditions 
Conditions to stop the exercise when either of the following conditions are met: 
- The number of rewards achieved is equal to the maximum number of 
rewards established (usually set by 50 rewards). 
- Training time is equal to the established time of the exercise (usually set to 600s). 
2.3.2.1. Model of stimulating nerve cell with DMT test 
Figure 2.12. The model for the DMT task. 
In the model described in Figure 2.12, the mouse is placed in an open 
environment and the coordinates are monitored by the CCD camera system and 
the central processor by Cineplex software version 2.0. 
The distance of the mouse moving in an open environment has a diameter 
of 80cm and is simulated with the running path of the mouse in virtual OF with 
pixel value: 1cm = 9.35pixel. During the exercise, when the reward condition 
of the required problem is satisfied, the central microprocessor sends the 
control signal to the excitation system via the NI6051 USB peripheral device. 
49 
The control signal activates an electric stimulus with a set parameter from the 
Stimulator and is transferred to the Isolator device to the mouse nerve cell via 
a stimulating electrode. 
2.3.2.2. Develop electric stimulation algorithm for DMT test 
a) Significance of DMT test: 
The DMT exercise algorithm is based on strict requirements about 
rewarding conditions. If the distance traveled by the mouse from the previous 
reward position of the mouse at the time 0t , to the present time t and DMTd ≥ 
delta, then the mouse will be awarded, in which: 
𝑑𝐷𝑀𝑇 = √(𝑥𝑡 − 𝑥0)2 + (𝑦𝑡 − 𝑦0)2 
𝑥𝑡, 𝑦𝑡: coordinates of mouse at 𝑡; 𝑥0, 𝑦0: reward coordinates of mouse 
before t. 
The algorithm of the DMT task (Distance Movement Task) is based on 
strict requirements of reward conditions. The movements of mice will be 
trained with easy to difficult requirements by the experimental tasks for 
asserting the optimal intensity and frequency parameters of the stimulating 
electrical pulses. Those parameters were already determined in the NPT task 
previously. The electric stimulation algorithm evaluating motor skills is the 
basis for building the program and interface of DMT exercises presented in the 
content of chapter 3. The DMT exercise program is shown visually on the 
program interface with the study subjects were mice. In addition to the program 
of closely monitoring the conditions of awarding and automatically awarding 
when the conditions of exercise are satisfied; The number of times the prize or 
the distance traveled by the mouse is visually simulated the mouse track and 
update the number of awards and the distance the mouse performed. The values 
are saved as a file and analyzed objectively to assess the mouse's ability to move 
50 
in a given space. The expectation of the exercise is that the mouse will travel 
the longest distance and the track will cover the open environment. 
b) DMT test: 
 Description: 
In an open environment, the mouse will receive a stimulus reward after 
moving a certain deltax distance (distance can be set and adjusted on the 
software interface). This distance is set according to how easy or difficult it is 
and usually varies from 50 - 100cm. This set up motivates the animals to move 
in an open environment to gain more BSR over the training days. 
 Attention: 
In case of loss of grip (suddenly losing the signal of the camera), the 
mouse position will return to the center of the open environment [coordinates 
(x; y) = (0; 0)]. There must be a condition algorithm to control this loss 
phenomenon to avoid large errors in the distance of travel, and the mouse is 
incorrectly rewarded. 
 Rewarding conditions: 
The movement distance of the mouse is a certain delta. In this exercise, 
the requirements of the problem are increased, often placing the delta region in 
increments within a certain limit to ensure that the mouse is well trained. 
 Test stopping conditions 
Conditions to stop the exercise when either of the following conditions are met: 
- The number of rewards achieved is equal to the maximum number of 
rewards established (usually set by 50 rewards). 
- Training time is equal to the established time of the exercise (usually set to 600s). 
c) Flowchart 
51 
Pt = 0; s = 0; t = 0; xt-1 = x0; yt-1 = y0; 
xt = x0; yt = y0; 
maxpt; maxT; delta
t++; 
xt-1 = xt; yt-1 = yt
s+ = sqrt[(yt-yt-1)
2 + (xt-xt-1)
2]
s = 0
Pt++
Pt = maxPt
|| t = maxT
yes
yes
no
no
s >= delta
Reading data
xt; yt
Start
End
Figure 2.13. Stimulating algorithm flowchart for DMT test. 
Flowchart of DMT test is described in figure 2.13, in which: 
- Pt: number of rewards. 
- maxPt: the maximum number of rewards in a single episode (conditions to 
stop the program). 
- t: timer variable. 
52 
- maxT: maximum time for one exercise (condition to stop the program). 
- xt, yt: the x, y coordinates of the current position of the mouse, respectively 
- xs, ys: the x and y coordinates of the previous position of the mouse, 
respectively 
- delta: distance required to hover to get reward. 
The flowchart of stimulus algorithms for DMT test is explained by the 
following steps: 
 Step 1: Initialize values 
- Pt: The number of rewards is assigned 0. 
- s: The travel distance of the mouse is assigned 0. 
- t: The timer variable is assigned by 0. 
- xt, yt: the coordinates of the mouse at the time t -1 is assigned with x0, y0 
which is the original coordinates of the mouse. 
- xs, ys: the coordinates of the mice at the time t is assigned with x0, y0 which 
is the original position of the mice. 
- maxPt: the maximum number of rewards in a set, pre-set (conditions to stop 
the program). 
- maxT: time limit once a set, set before (conditions stop the program). 
- delta: limits the distance the mice moves to get the reward. 
 Step 2: at each t , sx is assigned as ; t sx y is assigned as ty (of the past t). 
Data reading unit reads mouse’s location and assigned to xt, yt. 
 Step 3: Accumulate the total moving distance of mouse using formula. 
 Step 4: If the mice's travel distance is greater than the delta (limit the 
distance to receive the reward), the number of rewards is increased by 1 unit 
and the travel distance is set to 0, then go to step 5. 
If not repeat from step 2 (repeat the assignment of coordinates and reading data). 
53 
 Step 5: If pt = maxPt or t = maxT then stop the program, if else repeat 
from step 2. 
2.3.2.3. Stimulation model for RRPST and PLT tests 
The model of neuron stimulation for RRPST and PLT spatial response 
evaluation tests is depicted in Figure 2.14 as follows: 
Isolator Stimulator
USB
6501
Plexon
Central 
processing 
system
AMP
C
C
D
 cam
era
Monitor
S
tim
ulating 
electrode
recording 
electrode
Mice
Figure 2.14. The system for stimulation and recording the action potential of 
neurons on mice. 
In order to assess the response of neurons to electrical impulsive 
stimulation, related exercises should be developed to assess spatial response 
through animal behavior. The dissertation has built a stimulation algorithm, 
recorded the electrical activity of nerve cells through exercises to find random 
reward place search task (RRPST also known as RND) and place learning task 
(PLT) tests. 
From the models and algorithms for stimulating nerve cells that have been 
developed in the DMT exercises, this is an important step to build the model of 
the stimulating system and record the electric activity of nerve cells in the PLT 
and RRPST test with the incentive mechanism to determine eligibility for 
reward and distribute the rewards that has been established and recorded 
completely automatically (Figure 2.14). 
54 
The mouse performs the test in an open environment that is monitored by 
the camera system and sent to the central system. The electrical activity of the 
cell is recorded through an electrode that is amplified 1000 times through two 
amplification floors and processed by the Plexon system. During the exercise, 
when the prize conditions are met, the central processor will emit a control 
signal via the NI6501 USB peripheral to the device set by the Stimulator, via 
an isolator and digital converter - similar to the Isolator to the electrode that 
enters the mouse nerve cell. The electrical activity of nerve cells is recorded via 
an electrode system, the signal is amplified, filtered and displayed in the Plexon 
system. The coordinates of the mouse, the time of awarding the "reward" when 
the mouse satisfies the condition of receiving the "reward" and the time when 
the action potential of the nerve cell is synchronized, stored in file format and 
analyzed in specialized software to evaluate the ability to remember, seek 
rewards and spatial response of the mouse. 
 Electrical stimulation with RRPST test 
a) Significance of RRPST test 
The RRPST exercise algorithm is based on strict requirements about the 
conditions for rewarding when 𝑑𝑅𝑅𝑃𝑆𝑇 ≤ 𝑤𝑧 in which: 𝑤𝑧 (radius of the reward 
area) and 𝑑𝑅𝑅𝑃𝑆𝑇 (distance from the center of the reward area to the position of 
the mouse) is calculated by: 𝑑𝑅𝑅𝑃𝑆𝑇 = √(𝑥𝑡 − x𝑧)2 + (𝑦𝑡 − y𝑧)2 
In which: (𝑥𝑧, 𝑦𝑧), (𝑥𝑡 , 𝑦𝑡): the coordinates of the center of the reward area and 
the coordinates of the mouse at t, respectively. 
The electric stimulation algorithm that evaluates motor skills and the need 
to find a reward is the basis for building the program and interface of the 
RRPST test presented in the content of the next chapter. Through this 
experimental exercise, we will train the mouse to search for rewards with the 
randomly generated reward area. RRPST test program is shown visually on the 
55 
program interface, the study subject is the mouse. In addition, the program 
closely monitors the conditions of rewarding and automatically rewards when 
the conditions of the test are satisfied; the number of rewarding or the distance 
traveled by the mouse is visually displayed by the mouse track and updated the 
number of rewards and the distance the mouse travelled. Those values are saved 
as a file and analyzed objectively to assess the mouse's motility for finding 
rewards in a given space. The expectation of an test is that the mouse receives 
the maximum number of rewards, moving the longest distance in a given time 
period of the test. 
b) RRPST test 
 Description: 
For this exercise, the central processing program will define a circular 
reward area, with a delta diameter (pre-set and possibly changing the radius of 
a circle) with the center randomly defined and lying. in the circumscribed circle 
the open environment circle. The program will issue a control signal that 
activates an electrical pulse as soon as the animal's coordinates touch this 
reward area (for a condition of Reward delay = 0s). After the reward has been 
issued (or after the maximum duration of 30s of the reward area) this reward 
area disappears and a new reward area will appear and activate after a certain 
time (usually set 5s). 
 Attention: 
The mouse only receives the reward when the coordinates of the animal 
intersect the reward area. The maximum duration of the reward area is 30 
seconds, after 30 seconds, if the mouse does not reach the reward, the reward 
area will disappear and the new reward area will be activated after the next 5s. 
If during the 30s existence of the reward area the animal gains the reward, the 
reward area will be inactivated and a new reward area will be randomly 
activated after the interval of 5s. 
56 
 Rewarding conditions 
- The mouse moves to the reward area and the next prize area only appears 
and is activated in a certain period of time. 
- The mouse in the reward area z a certain amount of time (reward delay) 
if the program set (the time is adjustable), the time of reward delay is defined 
as the time period since the mouse touches the area of reward until the mouse 
receives the reward and the mouse's position remains in the reward area. 
 Test stopping conditions 
Conditions to stop the exercise when either of the following conditions are met: 
- The number of rewards achieved is equal to the maximum number of 
rewards established (usually set by 50 rewards). 
- Training time is equal to the established time of the exercise (usually set to 600s). 
c) Algorithm flowchart 
Algorithm of the RRPST test is described in Figure 2.15, in which: 
- Pt: number of rewards. 
- maxPt: the maximum number of rewards once a set, is set in advance 
(conditions stop the program). 
- maxT: the maximum time once a set, is set before (condition to stop the 
program). 
- t: timer variable. 
- xt, yt: x, y coordinates of the current position of the mouse. 
- xzt, yzt: x, y coordinates of the center of the current reward area. 
- wz: radius of the reward area. 
- maxwidth: radius of mouse area moving. 
- deltaTime: The time it takes to count from the time the mouse receives the 
prize until the new reward area appears. 
- delayTime: the minimum time from when the mouse receives the reward 
until the new reward area appears. 
57 
- vungnhanthuong: equal to true if the mouse has just received a reward and 
the new reward area has not yet appeared, with false if the mouse has not 
received a reward when the new reward area appears. 
Pt = 0; t= 0; xt = x0; yt = y0; xzt = xz0; yzt = yz0; wz
deltaTime = 0; delayTime; maxwidth;
maxPt; maxT; taovungpt = false
 t++; deltaTime++;
delta = sqrt[(xt – xzt)
2 + (yt – yzt)
2 ]
delta = 0
taovungpt = true
Pt++; deltaTime =0
delta <= wz
& taovungpt = false
yes
yes
no
deltaTime>= delayTime
& taovungpt = true
xzt = rand(0,maxwidth) 
yzt = rand(0,maxwidth)
Pt == maxPt
|| t == maxT
no
no
yes
Reading data
xt; yt
End
Start
Figure 2.15. Algorithm flowchart for the RRPST test. 
58 
The flowchart of the stimulus algorithm for RRPST exercises is explained 
as follows: 
 Step 1: Initialize values 
- Pt: The number of rewards is assigned 0. 
- t: The timer variable is assigned by 0. 
- xt, yt: the coordinates of the mouse at the time t -1 is assigned with x0, y0 
which is the original coordinates of the mouse. 
- xz1, yz1: coordinates of center of rewarding zone. 
- maxPt: the maximum number of rewards in a set, pre-set (conditions to stop 
the program). 
- maxT: time limit once a set, set before (conditions stop the program). 
- delta: limits the distance the mouse moves to get the reward. 
- wz: radius of the reward area. 
 Step 2: At each t, data reading block reads the mouse's new position on 
the variable xt, yt. 
Increase deltaTime. 
 Step 3: If deltaTime is greater than or equal to the delay time and the 
distance from the mouse to the center of the prize area is equal to or smaller 
than the radius of the reward area, the mouse is rewarded. 
If these conditions are not met, calculate the distance traveled by the 
mouse and move to step 4. 
 Step 4: If mouse is in rewarding zone and nhanthuong = false then 
proceed to step 5. 
If else repeat step 2. 
 Step 5: Assign delta = 0, nhanthuong = true, increase rewards and assign 
deltaTime = 0. 
59 
 Step 6: If pt = maxPt or t = maxT then stop the program, if else repeat 
from step 2. 
 Electrical stimulation with PLT 
a) Significance of PLT test 
The PLT exercise algorithm relies on strict requirements about the 
conditions for awarding when 𝑑𝑃𝐿𝑇 ≤ 𝑤𝑧 in which: 𝑤𝑧 (radius of active reward 
zone) và 𝑑𝑃𝐿𝑇 (distance from center of reward area to mouse position) is 
calculated similar to the RRPST test: 
in which: 
- 𝑥𝑧, 𝑦𝑧: x, y coordinates of center of rewarding zone. 
- 𝑥𝑡, 𝑦𝑡: x, y coordinates of mouse at t, respectively. 
The electric stimulation algorithm that evaluates motor skills and the need 
to find a reward is the basis for building the program and interface of the PLT 
test presented in the content of the next chapter. Through this experimental 
exercise, we will train the mouse to search for rewards with the randomly 
generated reward area. PLT t