Neural networks grown and self-organized by noise (1906.01039v1)

Abstract: Living neural networks emerge through a process of growth and self-organization that begins with a single cell and results in a brain, an organized and functional computational device. Artificial neural networks, however, rely on human-designed, hand-programmed architectures for their remarkable performance. Can we develop artificial computational devices that can grow and self-organize without human intervention? In this paper, we propose a biologically inspired developmental algorithm that can 'grow' a functional, layered neural network from a single initial cell. The algorithm organizes inter-layer connections to construct a convolutional pooling layer, a key constituent of convolutional neural networks (CNN's). Our approach is inspired by the mechanisms employed by the early visual system to wire the retina to the lateral geniculate nucleus (LGN), days before animals open their eyes. The key ingredients for robust self-organization are an emergent spontaneous spatiotemporal activity wave in the first layer and a local learning rule in the second layer that 'learns' the underlying activity pattern in the first layer. The algorithm is adaptable to a wide-range of input-layer geometries, robust to malfunctioning units in the first layer, and so can be used to successfully grow and self-organize pooling architectures of different pool-sizes and shapes. The algorithm provides a primitive procedure for constructing layered neural networks through growth and self-organization. Broadly, our work shows that biologically inspired developmental algorithms can be applied to autonomously grow functional 'brains' in-silico.

• The paper introduces a novel algorithm that grows layered neural networks from a single unit using noise-driven developmental processes.
• The approach employs Izhikevich neuron-based spontaneous activity and Hebbian-like local learning to emulate early visual system development.
• The self-organized networks demonstrate robust performance (~90% accuracy on MNIST) and adaptability to various geometries and defective nodes.

Insights into "Neural Networks Grown and Self-Organized by Noise"

This paper presents a biologically inspired developmental algorithm designed to autonomously grow and self-organize artificial neural networks (ANNs) from a single computational unit, without human-designed architectural input. The approach leverages biological mechanisms observed in early neural development, particularly focusing on the self-organization of neural circuits analogous to those in the visual system.

Core Contributions

The authors propose an algorithm inspired by early visual system development, which effectively grows a layered neural network from a simple precursor. The algorithm includes:

1. Spontaneous Activity Wave Generator: Modeled using Izhikevich neurons, this component introduces a noise-dependent, spatiotemporal wave-driving mechanism in the first network layer. This operation is facilitated by recreating a wiring topology that involves local excitation and global inhibition, emulating the anatomical layering process that occurs in the brain.
2. Local Learning Rule Implementation: Analogous to Hebbian learning, this element involves a local learning rule that adjusts synaptic weights in the second layer based on the temporal wave patterns generated in the first layer. This synaptic modification process is described as forming the basis of a convolutional pooling layer, a critical component of convolutional neural networks (CNNs).
3. Emergence of Pooling Architecture: Through continuous interaction between the wave generator and local learning rule, the authors demonstrate the self-organization of a pooling architecture—an essential aspect of CNNs—across various geometry configurations, input sizes, and even in the presence of defective nodes.

Numerical Results and Robustness

The paper reveals strong numerical results by proving that networks grown using their proposed algorithm perform similarly to handcrafted architectures on standard image classification tasks, such as the MNIST dataset. The self-organized ANNs achieved approximately 90% accuracy, comparable to manually configured networks. This matching performance is significant—it shows that through emulated developmental processes, neural networks can achieve functional utility demonstrated by established AI methodologies without explicit structural programming.

Implications and Future Directions

The algorithm’s adaptability to different geometries and robustness against defects suggests potential applications in developing self-organizing computing systems for various sensor configurations, possibly impacting sensor data processing technologies. The scalability of this learning and self-organization paradigm highlights its practical implications for developing efficient, autonomous AI systems.

From a theoretical perspective, this research demonstrates a successful abstraction of neural developmental processes into computational algorithms, providing a framework for growing more "life-like" ANN architectures. It suggests an interesting trajectory toward designing ANNs that inherently self-configure and adapt, offering potential insights into AI systems capable of more naturalistic and flexible interactions with their environment.

Future investigations may focus on extending these principles to more comprehensive neural architectures, potentially covering components analogous to other brain regions. Additionally, the exploration of interconnectivity and network interactions at higher architectural levels would be necessary to more fully understand and leverage the potential of emergent bio-inspired AI systems.