Adam C., Le van Quyen M., Martinerie J., Clemenceau S., Baulac M., Renault B. & Varela F. J. (1999) Interactions entre réseau épileptique et fonctionnement cérébral: Approche par analyse non-lineaire de l’EEG intracrânien. Revue Neurologique 155: 489–494.
Baulac M., Le van Quyen M., Martinerie J., Clemeceau S., Adam C. & Varela F. J. (1999) Pre-ictal changes of the EGG dynamics in epileptic patients: clinical and neurobiological implications. In: Grassberger P. & Lehnertz K. (eds.) Chaos in the Brain?. World Scientific, Singapore: 77–86.
Bersini H. & Varela F. J. (1991) Hints for adaptive problem solving gleaned from immune networks. In: Schwefel H.-P. & Männer R. (eds.) Parallel Problem Solving from Nature, Lecture Notes in Computer Science Volume 496. Springer Verlag, Berlin: 343–354. https://cepa.info/1964
Biology gives us numerous examples of self-assertional systems whose essence does not precede their existence but is rather revealed through it. Immune system is one of them. The fact of behaving in order not only to satisfy external constraints as a pre-fixed set of possible environments and objectives, but also to satisfy internal “viability” constraints justifies a sharper focus. Adaptability, creativity and memory are certainly interesting “side-effects” of such a tendency for self-consistency. However in this paper, we adopted a largely pragmatic attitude attempting to find the best hybridizing between the biological lessons and the engineering needs. The great difficulty, also shared by neural net and GA users, remains the precise localisation of the frontier where the biological reality must give way to a directed design.
Bersini H. & Varela F. J. (1991) The Immune recruitment mechanism: A selective evolutionary strategy. In: Belew R. K. & Booker L. S. (eds.) Proceedings of the 4th International Conference on Genetic Algorithms. Morgan Kauffman Publishers, San Mateo CA: 520–526.
Bersini H. & Varela F. J. (1994) Learning and the Immune network: Reinforcement, recruitment and their applications. In: Patton G. (ed.) Biologically inspired computation. Chapman and Hill, London: 166–192.
Bersini H. & Varela F. J. (1994) The immune learning mechanisms: Reinforcement, recruitment and their applications. In: Paton R. (ed.) Computing with biological metaphors. Chapman and Hall, London: 166–192.
Bonnardel V. & Varela F. J. (1991) A frequency view of color vision: Measuring the human sensitivity to square-wave spectral power distributions. Proceedings of the Royal Society of London. Series B: Biological Sciences 245(1314): 165–171. https://cepa.info/2073
We have measured the chromatic threshold sensitivity to stimuli with spectral composition determined by a periodic function of energy over wavelength. This approach is analogous to frequency studies of spatial vision for the study of colour. A device was constructed permitting the synthesis of illuminants over the entire visible range (400–700 nm) in which phase, frequency and amplitude can be independently controlled. We have used 12 frequencies of square-wave functions (from 0.5 to 3.6 cycles/300 nm) and seven values of phase (between 0 degrees and 180 degrees) to obtain the contrast sensitivity function of the chromatic system in three normal trichromats. The results show maximum sensitivity around 1.5 cycles/300 nm and a high-frequency cut-off at 3.6 cycles/300 nm. These empirical values are compared with the predictions obtained from three current psychophysical models of opponent-colour process.
Bonnardel V., Martinoya C. & Varela F. J. (1991) Une méthode de mesure de la sensibilité aux modulations périodique d’énergie lumineuse spectrale chez l’Homme [Measuring the contrast sensitivity to periodic spectral modulations in man]. Comptes Rendus de l’Académie des Sciences Series III 312: 695–700.
Bourgine P. & Varela F. J. (1992) Towards a practice of autonomous systems. In: Bourgine P. & Varela F. J. (eds.) Toward a practice of autonomous systems. MIT Press/Bradford Books, Cambridge: xi–xvii. https://cepa.info/1972
This Introduction is our attempt to clarify further the cluster of key notions: autonomy, viability, abduction and adaptation. These notions form the conceptual scaffolding within which the individual contribution contained in this volume can be placed. Hopefully, these global concepts represent fundamental signposts for future research that can spare us a mere flurry of modelling and simulations into which this new field could fall.
Budnik V., Mpodozis J., Varela F. J. & Maturana H. R. (1984) Regional specialization of the quail retina: Ganglion cell density and oil droplet distribution. Neuroscience Letters 51(1): 145–150. https://cepa.info/571
The ganglion cell density of the quail’s retina was studied in sections and whole mounts. Two regions of high ganglion cell density were found, corresponding to an afoveate area centralis and an area dorsalis. Oil droplets were found to be isotropically distributed throughout the retina. It is proposed that the significance of such retinal regional specialization, in comparison to similar studies in the pigeon and the chick, is that regional specialization in the avian retina is more closely related to feeding habits than to phylogenetic descendence.