Abstract: Tensor network topologies for function, first class or MFA I as BEAM circuits are described within the framework of complexity theory using Lie Computability definitions. An example of the design of opamp based Nv Neurons for the perception of shape from line detection Nv neurons is described, with circuits that detect number of lines and concavity and closure of lines in a finite region of interest. The possible role of BEAM robotics in 3R’s is described in nature inspired intent transcription, in multi -functionality and functionality driven evolution and transcription. Keywords: BEAM, MFA I, MFA II, Lie Computability, op amp circuits, Nv Neurons, Two Port Systems, large signal analysis, feedback principles, solitons, neuro-modulation. What: Nv Neurons are built from opamp based circuits, for line detection using an array or grid of inexpensive photo detectors, using an opamp based positive feedback loop and negative feedback loops for synergy principles. Story: The author first worked on this problem in his undergraduate senior year, when his advisor advised a bottom-up approach to BEAM based machine vision, biomimetics in synthetic neurons from discrete components and opamps. The problem was to differentiate a simple polygonal shape from the background. How: A simple polygon is found in traffic sign posts , creating the need for a hard wired circuit to recognize an octahedral stop sign and several triangular signs. We use line decomposition with a circuitry to compose the lines into polyhedral shapes.(Bheemaiah, n.d.) Why: BEAM is functional art, and forms the predecessor to MFA II or completely multi functional architecture of a broad umbrella of value addition in multi functionality, functoid and HOF based algebraic frameworks for MaC based definitions of architecture and design in code.(Autores and International Workshop on Higher-Order Algebra, Logic and Term Rewriting 1994; Kirchner and Wechler 1990; Dowek et al. 1996)