Biological computation in the context of DNA computing

Chemical synapses are biological junctions through which neurons' signals can be sent to each other and to non-neuronal cells such as those in muscles or glands. Chemical synapses allow neurons to form circuits within the central nervous system. They are crucial to the biological computations that underlie perception and thought. They allow the nervous system to connect to and control other systems of the body.

At a chemical synapse, one neuron releases neurotransmitter molecules into a small space (the synaptic cleft) that is adjacent to the postsynaptic cell (e.g., another neuron). The neurotransmitter molecules are contained within small sacs called synaptic vesicles, and are released into the synaptic cleft by exocytosis. These molecules then bind to neurotransmitter receptors on the postsynaptic cell. Finally, to terminate its action, the neurotransmitter is cleared from the cleft through one of several mechanisms, including enzymatic degradation or re-uptake, by specific transporters, either into the presynaptic cell or to neuroglia.

Amorphous computing refers to computational systems that use very large numbers of identical, parallel processors each having limited computational ability and local interactions. The term amorphous computing was coined at MIT in 1996 in a paper entitled "Amorphous Computing Manifesto" by Abelson, Knight, Sussman, et al.

Examples of naturally occurring amorphous computations can be found in many fields, such as developmental biology (the development of multicellular organisms from a single cell), molecular biology (the organization of sub-cellular compartments and intra-cell signaling), neural networks, and chemical engineering (non-equilibrium systems). The study of amorphous computation is hardware agnostic—it is not concerned with the physical substrate (biological, electronic, nanotech, etc.) but rather with the characterization of amorphous algorithms as abstractions with the goal of both understanding existing natural examples and engineering novel systems. Ultimately, this field extenuates to Computational Intelligence, as this computational technique is an extenuation of Artificial Intelligence (but more specifically Artificial General Intelligence) for developing Biological Computation.