## 3 Proposed Mechanism
### Place Sequence Learning  
In hippocampal research, it has been found that the place neuron is activated earlier before reaching to the corresponding place of the place neuron. I also found the change in the activation pattern based on changing the current task and the goal location. This behavior shows the learning of the activation sequence of place neurons with respect to tasks or the goal location. Our proposed model has been incorporated with the same using the place sequence learner. The place sequence learner is shown in Figure 14. The sequence learner can recall the next sequential place neuron and its associated grid code. Since the recalled place neuron will always correspond to one of the neighboring places of the current activated one. Therefore, in navigation through neighboring place neuron's grid code, the chances of error would be muchless. A place sequence learner is used to reduce the accumulation of errors while moving to a distant location. The learner recalls the grid code of the next place that is learned during the trip. The grid code can tell the movement direction exactly and the magnitude to obtain the location of the recalled grid code from the current grid code. Since the recalled place always is the local one in place sequence learner, therefore, the accuracy would be very higher compared to using the grid code of a destination place to move towards its corresponding place of the grid code. Furthermore, a trip layer is used in sequence learners, which contains the neurons/nodes corresponding to each individual trip, which can be seen as an episodic memory of a trip. Here each trip is forgettable based on the frequency of recalling like the episodic events. Second is the place layer, which contains place neuron nodes. The third one is the grid layer contains the grid neurons. The grid layer nodes send the grid code to the place layer neurons, and the place layer nodes send the activation sequence to the trip layer nodes for learning. Each trip node has a weighted connection with each place neuron. The lowest is the weight earliest will be the position of the place neuron in the trip and vice versa. Initially, when the agent has not yet made any trip, no trip node will be there. With every new trip, a trip will be created whose weights with all place neurons will be initialized to zero; while navigation, the weight with the currently activated place neuron will be set to 1, and the weights with all other place neurons reduced by a factor \((1-\beta)\), as shown in the Equation (11). \[ \begin{array}{l}W_{i,j}=1\\ W_{i,k}=W_{i,k}(1-\beta)\forall k\
eq j\end{array}  \tag{11}\]  
Figure 14: Place sequence learner. where, \(W_{i,j}\) is the weight of \(i\)th place neuron with an \(i\)th trip node, \(j\) is the currently activated place neuron and \(\beta\) is the decaying factor. During a visit to any goal location, the agent tries to find the trip node that is not having a null weight value with the goal location's place neurons and the current location. If any such trip node is found, then it means that the trip is having a path from the current to the goal location. In case, if the weight value of the current place neuron is higher than the goal place neuron, then it shows that the path is from the goal to the current location, i.e., backward path. In the case of a forward path, the recalling starts with the place neuron having the next lowest weight value. In the case of the backward path, choose the next highest weight value. The process will be repeated until the place neuron corresponding to the goal location is recalled. Trip strength is suggested to manage memory efficiently. If a trip frequently used, the trip strength for that trip increases with a learning rate; otherwise, it decreases. Let \(TS_{i}\in[0,1]\) be a trip strength for the \(i\)th trip node. The calculation for the trip strength is shown below in Equation (12). \[TS_{i}(new)=\left\{