Another type of topology you’ll encounter in deep neural networks (DNNs) is network topology. This refers to the structure of the network — how the nodes are connected and how data flows between them. We already have several well-known examples, such as auto-encoders, convolutional neural networks (CNNs), and generative adversarial networks (GANs), all of which are bio-inspired.
However, we still have much to learn about the topology of the brain and its functional connectivity. In the coming years, we are likely to discover new architectures — both internal within individual layers/nodes and in the ways specialized networks connect and interact with each other.
Additionally, the brain doesn’t rely on a single network, but rather on several ones — often referred to as the "Big 7" — that operate in parallel and are deeply interconnected. Some of these include the Default Mode Network (DMN), the Central Executive Network (CEN) or the Limbic Network, among others. In fact, a single neuron can be part of multiple networks, each serving different functions.
We have not yet been able to fully replicate this complexity in artificial systems, and there is still much to be learned and inspired by from this "network topologies".
However, we still have much to learn about the topology of the brain and its functional connectivity. In the coming years, we are likely to discover new architectures — both internal within individual layers/nodes and in the ways specialized networks connect and interact with each other.
Additionally, the brain doesn’t rely on a single network, but rather on several ones — often referred to as the "Big 7" — that operate in parallel and are deeply interconnected. Some of these include the Default Mode Network (DMN), the Central Executive Network (CEN) or the Limbic Network, among others. In fact, a single neuron can be part of multiple networks, each serving different functions.
We have not yet been able to fully replicate this complexity in artificial systems, and there is still much to be learned and inspired by from this "network topologies".
So, "Topology is all you need" :-)