## 2 Methodology
### Performance Evaluation Metrics
Simultaneously, the sequence of sensor firings, detecting targets and obstacles at specific intervals, demonstrates the initial random movements of the robot towards the target and responses to obstacles. Figure 6A provides a visual representation of the location of the robot throughout this training phase. We tested the system after training to see how it works. In the test, we gave random inputs to the motor neurons on both sides. But it was difficult to always see the targets and obstacles because the range of the sensor was limited. To solve this, we added random background currents to the neurons, which affected movement without favoring right or left motors (see Figure 7C). We wanted to see how the sensory neurons control the direction of the robot by firing. In Figure 7B, the synaptic weights remained the same during testing because there were no changes made. So, there was no diagram for this part. Next, we focused on studying how the firing patterns of sensory neurons during testing affected the motor neurons' activation and, therefore, controlled the movement of the robot and directed it to the target. Figure 7A shows where the robot is during the testing phase. ### Synaptic Weight at Training Time

This robot uses a type of learning called STDP, a form of Hebbian learning. It adjusts the connections between the sensors of the robot and motors based on the timing of their signals. When a sensor signal comes before a motor signal, the connection is strengthened, while the opposite weakens it. This allows the robot to adapt to its environment in real-time, improving its ability to navigate complex and changing surroundings. In the training phase of the mobile robot's SNN, Figure 8 illustrates the synaptic changes triggered by the first release of dopamine when the robot reaches the target. This is the initial phase of the experiment. Examining the synaptic changes within this diagram reveals that most synaptic

Figure 6: The robot in the training phase to teach itself about surrounding. weights undergo modifications. However, it is important to note that these changes occur in the early stages, and the differences are relatively small, remaining close to their initial values, which are set to 1.0. While these initial changes are promising, further experiments are needed to achieve more substantial alterations and fine-tune the system. Figure 9 clearly illustrates how the synaptic weights are adjusted. These weights reflect the connections between the sensors and motors of the robot and are constantly tweaked to help the robot learn and adjust to its surroundings. Overall, there are distinct patterns in the synaptic weight adjustments that can be summarized as follows:

* The synaptic connection between the left target sensor and the suitable motor begins with a train-time synaptic weight of 0 and gradually increases, ultimately reaching 3.45 over time. * The left motor is linked to the left target sensor and exhibits a stable train-time synaptic weight of 200, varying from 1 to 2.2. Figure 8: Synaptic weight changes in initial steps. Figure 7: A mobile robot undergoing testing in an environment with three obstacles and one target. Similarly, the left motor connects to the suitable target sensor with a train-time synaptic weight of 400, and this weight undergoes fluctuations from 1.02 to 2.09. The synaptic connection between the left motor and the right obstacle sensor begins with a train-time synaptic weight of 600 and varies between 1 and 3.52 over time. Conversely, the synaptic connection between the suitable motor and the left obstacle sensor starts with a train-time synaptic weight of 800 and gradually decreases, reaching 0.2. The left motor is associated with the left obstacle sensor, maintaining a stable train-time synaptic weight of 1000, with fluctuations within the range of 1 to 1.2. Finally, the right obstacle sensor connects to the suitable motor, initially featuring a train-time synaptic weight of 0, increasing to 1.8 over time.