Title: A Proposal on Graph-Based Connectivity, Mini Memory, and Selective Adaptation Mechanisms in Nature-Inspired Dynamic Adaptive Neural Networks
Abstract: Conventional deep learning models employ fixed and dense connections, requiring all neurons to be active at every computation step. However, the principle of natural selection observed in nature — survival of the strong, elimination of the weak — and the idea that individual neurons in biological neural systems possess local memory offer the potential for developing more efficient, dynamic, and adaptive architectures in artificial neural networks. This paper proposes a neural network architecture where each neuron establishes graph-based connections with a limited number of neighbors; stores past parameters in a mini-memory to exhibit multiple behaviors depending on the input; and during training, only successful paths are activated and rewarded, while weaker paths are revised through a feedback mechanism and pruned if necessary. This structure parallels nature’s “preserve the strong, eliminate the weak” principle, aiming for the model to form its own optimal architecture through an evolutionary process.
1. Introduction Traditional neural networks operate with fixed layers and dense connectivity, causing every neuron to be active in each computation step. This leads to high computational costs and memory usage. Observations from nature suggest an evolutionary selection principle — the survival of the strong and elimination of the weak — which aligns with reinforcing only successful paths and connections during training. Furthermore, the concept of each neuron in the brain possessing its own local memory, rather than relying on a single central memory, provides new approaches to enhance the flexibility of neural networks. This paper proposes a neural network architecture based on these principles, which can dynamically adapt during training and is supported by trackable, reward-based feedback mechanisms.
2. Related Work In the literature, aside from fixed dense networks, researchers have explored sparse connections, mixture-of-experts (MoE), dynamic neural network architectures, Graph Neural Networks (GNNs), and neuroevolution methods. Although these approaches aim to improve model efficiency and adaptation capabilities, our proposed architecture uniquely combines the following features:
Each neuron utilizing its own mini-memory to exhibit multiple behaviors specific to various inputs.
In line with the principle of natural selection, successful paths are rewarded, and weaker paths are pruned.
The implementation of trackability and feedback mechanisms during training to correct incorrect orientations.
3. Proposed Architecture
3.1 Graph-Based Connectivity
Modular and Limited Connections: Instead of the traditional layer-based structure, the network is modeled as a graph. Each neuron is connected to a predefined maximum number of neighbors (e.g., 5 or 10). Neurons communicate by sending only “wake-up” signals to their neighbors rather than directly sharing information. This approach enables each neuron to perform its own computations and decision-making, activating only when necessary.
Dynamic Path Formation: Starting from the input neuron, connections in the graph are triggered sequentially. Although potentially millions of paths can be generated during training, only input-specific, rewarded, and trackable paths are activated.
* 3.2 Mini-Memory and Multiple Behaviors
Local Memory: Each neuron stores the most recent few (e.g., 5) versions of its parameters in a mini-memory. These versions carry a “strength score,” calculated based on factors such as usage frequency, performance, and rewards. This allows the neuron to select the most suitable behavior based on previous successful connection configurations.
Multiple Behavior Capability: A neuron can activate different connection paths for various conditions. Thus, rather than displaying a single fixed behavior, the same neuron can generate diverse paths depending on input diversity. At certain intervals, each neuron compares the versions in its mini-memory and either eliminates or updates those with low “strength scores.” This process can be viewed as a dynamic tournament within each neuron’s mini-memory.
* 3.3 Feedback and Reward Mechanisms
Performance Monitoring and Feedback: During training, the loss and other performance metrics of each path are monitored. If a path’s results fall below a specified threshold, the system re-evaluates that same path via a feedback mechanism rather than re-updating the entire path from scratch. This identifies and corrects misguided orientations; however, if consistently low performance is observed on the same path, appropriate updates are made to prune that path.
Reward-Based Adaptation: Successful paths and connections receive rewards. The reward reinforces the successful parameters stored in the neurons’ mini-memory, ensuring they are more likely to be chosen in the future. Moreover, the reward mechanism can trigger the addition of new neurons or connections, which not only strengthens successful paths but also contributes to the network’s expansion and growth.
Structural Expansion: Beginning with neurons that receive the highest rewards, if the maximum number of neighbors for a neuron is exceeded, the process shifts to a lower layer, where new strong connection paths are sought. When neurons receive rewards, perform backpropagation, reach their connection capacity, or add new connections, they trigger a tournament among the versions in their mini-memory.
* 3.4 Selective Activation and Computational Efficiency
Selective Activation: During both training and inference, not all neurons are activated simultaneously; only the neurons in the rewarded and selected path participate in the computation. This significantly reduces overall computational and memory costs.
Efficient Computation: Because processing occurs only through specific and rewarded paths, billions of neurons — as in conventional dense models — do not need to be activated. This approach increases computational speed and optimizes resource usage. Pruning unnecessary connections and applying selective activation significantly boost the network’s efficiency in both computation and memory.
4. Philosophical Foundations and Discussion The proposed structure is grounded in the principle of natural selection: the survival of the strong and the elimination of the weak. During training, successful connection paths are reinforced and rewarded, while low-performance paths are pruned or corrected via the feedback mechanism. This evolutionary process allows the model to form its own optimal architecture based on principles of “fairness”. Each neuron’s local memory highlights a decentralized form of information processing; thus, every component of the network can produce the most appropriate response to an input, relying on past experiences. In this architecture, “strength” is defined as a combination of factors such as neurons’ interactions, mini-memory usage, behavioral diversity, and adaptability to requirements. Each neuron evolves toward an optimal structure through its internal and environmental interactions.
Advantages:
Computational and Memory Efficiency: Through selective activation and pruning of unnecessary connections, the overall computational load is significantly reduced.
Adaptive and Evolutionary Learning: Feedback and reward mechanisms enable the model to dynamically optimize itself. Strong connections are reinforced while weak paths are pruned, mirroring the principles of natural selection.
Flexible Architecture Formation: Throughout the training process, the model evolves continuously; successful paths multiply, new connections are added, and the structure is rearranged as needed.
Challenges:
Management and Coordination: Managing each neuron’s mini-memory, dynamic connection structures, and tournament mechanisms introduces additional computational and memory complexity. Optimal management of this complexity will require careful engineering and experimental research.
Feedback and Update Criteria: Determining under which conditions feedback is initiated, the reward thresholds, pruning strategies, and the tournament frequency all need to be optimally defined.
Traceability and Practical Implementation: Monitoring paths during training, integrating error path detection algorithms, and ensuring the seamless operation of new connection mechanisms all require detailed investigation in practical scenarios.
5. Conclusion The proposed dynamic adaptive neural network architecture offers a more efficient and adaptive learning process compared to conventional dense networks. This approach is inspired by evolutionary principles from nature, leveraging each neuron’s local memory to respond flexibly to inputs. During training, only rewarded paths are actively computed. Misguided orientations are corrected through the feedback mechanism, and underperforming paths are pruned, thereby allowing the network to dynamically form its own optimal architecture. The reward mechanism triggers opportunities to add new neurons or connections, facilitating the model’s evolutionary growth. This approach provides significant improvements in computational and memory costs, while also showing promise for the development of adaptive learning processes and evolutionary structural optimization. In the future, experimentally validating this architecture and integrating it into various application domains will be an exciting research topic for the deep learning community.
Acknowledgements
The authors would like to thank Mohammad Jamali Marandfor his valuable contributions, feedback, and inspiring discussions throughout this study. His insights were instrumental in the development of the ideas and concepts presented in this paper.
