Bio-inspired Information and Communication Technologies. 11th EAI International Conference, BICT 2019, Pittsburgh, PA, USA, March 13–14, 2019, Proceedings

Research Article

Blinded by Biology: Bio-inspired Tech-Ontologies in Cognitive Brain Sciences

  • @INPROCEEDINGS{10.1007/978-3-030-24202-2_5,
        author={Paola Hern\^{a}ndez-Ch\^{a}vez},
        title={Blinded by Biology: Bio-inspired Tech-Ontologies in Cognitive Brain Sciences},
        proceedings={Bio-inspired Information and Communication Technologies. 11th EAI International Conference, BICT 2019, Pittsburgh, PA, USA, March 13--14, 2019, Proceedings},
        proceedings_a={BICT},
        year={2019},
        month={7},
        keywords={Natural kinds Cognitive brain sciences Ontology BioInspiration Technology},
        doi={10.1007/978-3-030-24202-2_5}
    }
    
  • Paola Hernández-Chávez
    Year: 2019
    Blinded by Biology: Bio-inspired Tech-Ontologies in Cognitive Brain Sciences
    BICT
    Springer
    DOI: 10.1007/978-3-030-24202-2_5
Paola Hernández-Chávez1,*
  • 1: University of Pittsburgh
*Contact email: hcpaola@gmail.com

Abstract

In his pioneering paper on neuromorphic systems, Carver Mead conveyed that: “Biological information-processing systems operate on completely different principles from those with which most engineers are familiar” (Mead 1990: 1629). This paper challenges his assertion. While honoring Mead’s exceptional contributions, specific purposes, and correct conclusions, I will use a different line of argumentation. I will make use of a debate on the classification and ordering of natural phenomena to illustrate how background notions of causality permeate particular theories in science, as in the case of cognitive brain sciences. This debate shows that failures in accounting for concrete scientific phenomena more often than not arise from (1) characterizations of the architecture of nature, (2) singular conceptions of causality, or (3) particular scientific theories – and not rather from (4) technology limitations . I aim to track the basic bio-inspiration and show how it spreads bottom-up throughout (1) to (4), in order to identify where bioinspiration started going wrong, as well as to point out where to intervene for improving technological implementations based on those bio-inspired assumptions.