Autonomics and Cognition: hierarchies of abstractions – Part 1Thursday, February 25th, 2010 by Antonio Manzalini
Neuroscientists argue that brain networks show small world and exponentially truncated power law characteristics. These are considered, by the way, the best structural basis for coexistence of both informational processing segregation (in specialized regions) and integration (by coherent oscillations in wider regions).
Most of neuroscience investigations, as far as I know, are currently based on the assumption that thoughts, feelings, consciousness emerge from the electrical and chemical communications between brain cells. On one side there are experimental studies, using different anatomical technologies (e.g. magnetic resonance imaging) to observe and elaborate about brain patterns. On the other side, there are studies and simulations on various computational models of complex networks neurons: for example how neurons fire electrically in response to inputs of other neurons, or release neurotransmitters to communicate with each other.
Anyway, how such network of networks of cells can produce the subtlety of mind, or can perform the body autonomic control, is still a big open question.
An interesting approach  proposes alternatively to focus on the relevance of abstractions (and their relationships to the natural evolution, or the human design). Being an electronic engineer (fond of Physics) let me resume the example of the Ohm’s law, V = IR, where V, I and R denote abstract entities respectively known as voltage across a resistor, current through it and resistance. So, the movement of electrons, in a physical resistor, results in the realisation of an abstract scheme satisfying the Ohm’s law. Engineers make use of such (and many others) abstract schemes to design electronic systems performing particular functions (but, basically ignoring the laws behind the movement of electrons). The same ideas probably apply to bio-systems. Bio-systems contain several components of various kinds, conforming to some scheme of abstractions. On the other hand, they differ from machines in that the entities concerned often lack a formal definition, their properties being inferred from investigations (at different levels of observation) of several instances encountered in nature.
This reasoning equally applies to autonomic systems functioning. The human autonomic nervous system, in its environment, is probably characterised by a hierarchy of systems conforming to a range of abstract schemes; typically these schemes relate to particular neural circuits or biological sub-systems and their adaptive behaviour in a given environment.
Natural design and evolution are driving (indirectly) the human autonomic nervous system to conform to such abstract schemes, through development and cognition (learning, at least). I think this is an important point.
Following this reasoning, to engineer autonomic systems requires defining, not only control-loop circuitry (at the proper level), but also hierarchies of abstract schemes and the processes of evolutionary development and cognition in dynamic environments.
In a next post, we’ll elaborate about how cognition may control phase-transitions in autonomic networks.