have been shown to have the potential to solve NP-complete problems and have been proposed as hardware implementations for unconventional computing. Biological approaches Biological computing, also known as bio-inspired computing or natural computation, is the study of using models inspired by biology to solve computer science problems, particularly in the fields of artificial intelligence and machine learning. It encompasses a range of computational paradigms including artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, and more, which can be implemented using traditional electronic hardware or alternative physical media such as biomolecules or trapped-ion quantum computing devices. It also includes the study of understanding biological systems through engineering semi-synthetic organisms and viewing natural processes as information processing. The concept of the universe itself as a computational mechanism has also been proposed. Neuroscience Neuromorphic computing involves using electronic circuits to mimic the neurobiological architectures found in the human nervous system, with the goal of creating artificial neural systems that are inspired by biological ones. These systems can be implemented using a variety of hardware, such as memristors, spintronic memories, and transistors, and can be trained using a range of software-based approaches, including error backpropagation and canonical learning rules. The field of neuromorphic engineering seeks to understand how the design and structure of artificial neural systems affects their computation, representation of information, adaptability, and overall function, with the ultimate aim of creating systems that exhibit similar properties to those found in nature. Wetware computers, which are composed of living neurons, are a conceptual form of neuromorphic computing that has been explored in limited prototypes. Cellular automata and amorphous computing Cellular automata are discrete models of computation consisting of a grid of cells in a finite number of states, such as on and off. The state of each cell is determined by a fixed rule based on the states of the cell and its neighbors. There are four primary classifications of cellular automata, ranging from patterns that stabilize into homogeneity to those that become extremely complex and potentially Turing-complete. Amorphous computing refers to the study of computational systems using large numbers of parallel processors with limited computational ability and local interactions, regardless of the physical substrate. Examples of naturally occurring amorphous computation can be found in developmental biology, molecular biology, neural networks, and chemical engineering. The goal of amorphous computation is to understand and engineer novel systems through the characterization of amorphous algorithms