## 2 Methodology
### Network Architecture and Training Algorithm  
An SNN is a network of interconnected neurons communicating through electrical activity spikes. It initiates with the input layer receiving signals from the surrounding environment, which are then converted into electrical impulses by the input neurons. These impulses traverse the network and ultimately lead to the generation of a signal in the output layer, predicated on the spikes produced by the output neurons. The process involves neurons emitting pulses to downstream neurons when they reach a specific threshold, establishing a communication chain [33]. Training SNNs involves adjusting synaptic weights connecting neurons to enhance the network's overall performance. STDP is a prevalent training algorithm for SNNs. STDP operates by modifying the synaptic weights based on the relative timing of spikes. This learning mechanism takes inspiration from biological learning principles, reinforcing connections between neurons that fire synchronously while weakening connections between neurons that fire at differing times. This intricate process allows the network to comprehend the temporal relationships between input and output signals, a critical aspect for practical applications [34]. Figure 2 serves as the proposed framework, which utilizes a spiking neural network (SNN) architecture, incorporating spike-timing-dependent plasticity (STDP), the Izhikevich model, and reinforcement learning (RL), to enable a robot to navigate towards a target while avoiding obstacles. The SNN consists of an input layer, hidden layer(s), and an output layer. The synaptic weights, representing the connections between neurons, are updated using STDP, which adjusts the weights based on the timing of spikes between connected neurons. Spike generation and propagation functions facilitate signal processing and information flow within the network. Neuron activation levels are determined by an activation function, while the Izhikevich model captures the spiking behavior of individual neurons based on inputs and activation levels. The Izhikevich model plays a crucial role in simulating the dynamics of neurons by considering important parameters, including a, b, c, and d. Parameter 'a' controls the sensitivity of the membrane recovery variable to the threshold fluctuations, while 'b' determines the sensitivity of the recovery variable to the membrane potential. Parameter 'c' represents the after-spike reset value of the membrane potential, and \"d\" governs the after-spike reset value of the recovery variable. The \"Izhikevich model\" and Tables 1 and 2 demonstrate how parameters influence the spiking patterns and excitability of neurons, enabling the representation of a wide range of neuronal behaviors. In the context of robot control, reinforcement learning incorporates feedback signals, such as rewards based on performance criteria (e.g., distance to the target, angle to the target, and distance to the nearest obstacle), to optimize the network's performance. By integrating these components and considering the parameters of the Izhikevich model, the SNN framework can adapt its behavior by adjusting synaptic weights. This adaptation allows the robot to effectively navigate towards the target while avoiding collisions. With this comprehensive approach, the SNN framework offers a promising solution for achieving efficient and safe robot control in unknown environments. Refer to Figure 2 for an illustration of the general SNN framework with STDP and our model. \begin{table}
\begin{tabular}{c c c c} \hline \hline Parameter & a & b & c & d \\ \hline Value & 0.02 & 0.2 & \(-65\) & \(8\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of parameters of the Izhikevich model.