The human brain is a complex organ that processes information hierarchically and in parallel. Can these techniques be applied to deep learning? Yes, researchers at the University of Copenhagen have developed a neural network called Neural Developmental Program (NDP) that uses hierarchy and parallel processing. The NDP architecture combines a Multilayer Perceptron and a Graph Cellular Automata. The NDP neural network can solve reinforcement learning and classification tasks. Researchers are also exploring automated methods to determine when to stop growing the network.
The Importance of Neural Developmental Programs
The human brain is highly complex, functioning through hierarchical organization and parallel processing. These techniques allow for efficient information processing, cognition, and decision-making. Can we adapt these techniques in the field of deep learning? Turns out, we can, through neural networks.
Researchers at the University of Copenhagen have introduced a type of encoding called Neural Developmental Programs (NDP) to mimic the brain’s ability to control the growth of a policy network. Inspired by biological processes, NDP utilizes indirect encoding, compressing the solution’s description and allowing for flexibility and reusability.
The NDP architecture is built upon a Multilayer Perceptron (MLP) and a Graph Cellular Automata (GNCA). Cellular automata are mathematical models that evolve based on preset rules, and the MLP and GNCA update node embeddings during the developmental phase. The number of parameters in NDP remains constant, regardless of the size of the graph it operates on. This makes NDP applicable to neural networks of any size or architecture and enables it to solve different tasks while incorporating topological properties.
However, researchers found that as the NDP grows with each step, its performance may decrease. Here, automated methods that determine the optimum number of growth steps become crucial. Future advancements in NDP involve activity-dependent and reward-modulated growth and adaptation techniques.
The introduction of Neural Developmental Programs bridges the gap between the growth of biological systems and artificial neural networks. By implementing practical AI tools like these, companies can enhance their operations, automate tasks, and redefine customer interaction points. It is essential to carefully consider KPIs and choose AI solutions that align with specific needs and business outcomes. Implementation should start with a pilot project and expanded gradually, gathering data along the way. ITINAI, a company experienced in leveraging AI, is available for consultation at hello@itinai.com.
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