This article examines the intellectual and institutional factors that contributed to the col- laboration of neuropsychiatrist Warren McCulloch and mathematician Walter Pitts on the logic of neural networks, which culminated in their 1943 publication, “A Logical Calculus of the Ideas Immanent in Nervous Activity.” Historians and scientists alike often refer to the McCulloch–Pitts paper as a landmark event in the history of cybernetics, and fundamental to the development of cognitive science and artificial intelligence. This article seeks to bring some historical context to the McCulloch–Pitts collaboration itself, namely, their intellectual and scientific orientations and backgrounds, the key concepts that contributed to their paper, and the institutional context in which their collaboration was made. Al- though they were almost a generation apart and had dissimilar scientific backgrounds, McCulloch and Pitts had similar intellectual concerns, simultaneously motivated by issues in philosophy, neurology, and mathematics. This article demonstrates how these issues converged and found resonance in their model of neural networks. By examining the intellectual backgrounds of McCulloch and Pitts as individuals, it will be shown that besides being an important event in the history of cybernetics proper, the McCulloch– Pitts collaboration was an important result of early twentieth-century efforts to apply mathematics to neurological phenomena.

Much scholarship in the history of cybernetics has focused on the far-reaching cultural dimensions of the movement. What has garnered less attention are efforts by cyberneticians such as Warren McCulloch and Norbert Wiener to transform scientific practice in an array of disciplines in the biomedical sciences, and the complex ways these efforts were received by members of traditional disciplines. In a quest for scientific unity that had a decidedly imperialistic flavour, cyberneticians sought to apply practices common in the exact sciences – mainly theoretical modeling – to problems in disciplines that were traditionally defined by highly empirical practices, such as neurophysiology and neuroanatomy. Their efforts were met with mixed, often critical responses. This paper attempts to make sense of such dynamics by exploring the notion of a scientific style and its usefulness in accounting for the contrasts in scientific practice in brain research and in cybernetics during the 1940s. Focusing on two key institutional contexts of brain research and the role of the Rockefeller and Macy Foundations in directing brain research and cybernetics, the paper argues that the conflicts between these fields were not simply about experiment vs. theory but turned more closely on the questions that defined each area and the language used to elaborate answers.

Key words: Cybernetics, Macy foundation, Neurophysiology, Rockefeller Foundation, Theoretical modeling, Warren S. McCulloch

Excerpt: As a young man worrying about the fundamental questions of philosophy, metaphysics, and epistemology, McCulloch set himself the goal of developing an “experimental epistemology”: how can one really understand the mind in terms of the brain? More particularly, he sought to discover “A Logical Calculus Immanent in Nervous Activity.” The present paper will seek to provide some sense of McCulloch’s search for the logic of the nervous system, but will also show that his papers contain contributions to experimental epistemology which provide great insight into the mechanisms of nervous system function without fitting into the mold of a logical calculus. Moreover, McCulloch was not only a scientist but also a storyteller, poet, and memorable “character. ” I will thus interleave a number of characteristic anecdotes into the more objective attempts at scientific history that follow.

Excerpt: The purpose of this essay is to clarify some of the important senses in which the relationship between the brain and the computer might be considered as one of “modeling.” It also considers the meaning of “simulation” in the relationships between models, computers and brains. While there has been a fairly broad literature emerging on models and simulations in science, these have primarily focused on the physical sciences, rather than the mind and brain. And while the cognitive sciences have often invoked concepts of modeling and simulation, they have been frustratingly inconsistent in their use of these terms, and the implicit relations to their scientific roles. My approach is to consider the early convolution of brain models and computational models in cybernetics, with the aim of clarifying their significance for more current debates in the cognitive sciences. It is my belief that clarifying the historical senses in which the brain and computer serve as models of each other in the historical period prior to the birth of AI and cognitive science is a crucial task for an archeology of AI and the history of cognitive science.

The course on nature coincides with the re-working of Merleau-Ponty’s breakthrough towards an ontology and therefore plays a primordial role. The appearance of an interrogation of nature is inscribed in the movement of thought that comes after the Phenomenology of Perception. What is at issue is to show that the ontological mode of the perceived object – not the unity of a positive sense but the unity of a style that shows through in filigree in the sensible aspects has a universal meaning, that the description of the perceived world can give way to a philosophy of perception and therefore to a theory of truth. The analysis of linguistic expression to which the philosophy of perception leads opens out onto a definition of meaning as institution, understood as what inaugurates an open series of expressive appropriations. It is this theory of institution that turns the analysis of the perceived in the direction of a reflection on nature: the perceived is no longer the originary in its difference from the derived but the natural in its difference from the instituted. Nature is the “non-constructed, non-instituted,” and thereby, the source of expression: “nature is what has a sense without this sense having been posited by thought.”\\The first part of the course, which consists in a historical overview, must not be considered as a mere introduction. In fact, the problem of nature is brought out into the open by means of the history of Western metaphysics, in which Descartes is the emblematic figure. The problem consists in the duality at once unsatisfactory and unsurpassable – between two approaches to nature: the one which accentuates its determinability and therefore its transparency to the understanding; the other which emphasizes the irreducible facticity of nature and tends therefore to valorize the viewpoint of the senses. To conceive nature is to constitute a concept of it that allows us to “take possession” of this duality, that is, to found the duality. The second part of the course attempts to develop this concept of nature by drawing upon the results of contemporary science. Thus a philosophy of nature is sketched that can be summarized in four propositions: 1) the totality is no less real than the parts; 2) there is a reality of the negative and therefore no alternative between being and nothingmess; 3) a natural event is not assigned to a unique spatio-temporal localization; and 4) there is generality only as generativity.

Biosemiotics is the synthesis of biology and semiotics, and its main purpose is to show that semiosis is a fundamental component of life, i.e., that signs and meaning exist in all living systems. This idea started circulating in the 1960s and was proposed independently from enquires taking place at both ends of the Scala Naturae. At the molecular end it was expressed by Howard Pattee’s analysis of the genetic code, whereas at the human end it took the form of Thomas Sebeok’s investigation into the biological roots of culture. Other proposals appeared in the years that followed and gave origin to different theoretical frameworks, or different schools, of biosemiotics. They are: (1) the physical biosemiotics of Howard Pattee and its extension in Darwinian biosemiotics by Howard Pattee and by Terrence Deacon, (2) the zoosemiotics proposed by Thomas Sebeok and its extension in sign biosemiotics developed by Thomas Sebeok and by Jesper Hoffmeyer, (3) the code biosemiotics of Marcello Barbieri and (4) the hermeneutic biosemiotics of Anton Markoš. The differences that exist between the schools are a consequence of their different models of semiosis, but that is only the tip of the iceberg. In reality they go much deeper and concern the very nature of the new discipline. Is biosemiotics only a new way of looking at the known facts of biology or does it predict new facts? Does biosemiotics consist of testable hypotheses? Does it add anything to the history of life and to our understanding of evolution? These are the major issues of the young discipline, and the purpose of the present paper is to illustrate them by describing the origin and the historical development of its main schools.

Key words: biosemiotics, signs, meaning, codes, interpretation, semiosis

Social Systems Theory has a long and distinguished history. It has progressed from a mechanical model of social processes, to a biological model, to a process model, to models that encompass chaos, complexity, evolution and autopoiesis. Social systems design methodology is ready for the twenty-first century. From General Systems Theory’s early days of glory and hubris, through its days of decline and disparagement, through its diaspora into different disciplines, systems theory is today living up to its early expectations.

Key words: systemic model, roots of systems theory, branches of systems theory, history of systems theory.

A survey of the history of science shows that very similar conceptions have been developed independently in various branches of science. At present, for example, holistic interpretations are prevalent in all fields whereas in the past atomistic explanations were common. Such considerations lead to the postulation of General System Theory which is a logico-mathematical discipline applicable to all sciences concerned with systems. The fact that certain principles have general applicability to systems explains the occurrence of isomorphic laws in different scientific fields. Just as Aristotelian logic was a fundamental organon for the classificatory sciences of antiquity, so may General System Theory define the general principles of dynamic interaction which appears as the central problem of modern science.

The article presents a history of general systems theory and discusses several of its various aspects. According to the author, the notion of general systems theory first stemmed from the pre-Socratic philosophers, and evolved throughout the ages through different philosophic entities until it was eventually formally structured in the early 1900s. The theory has three main aspects. The first is called “systems science,” or the scientific exploration and theory of systems in various sciences. The second is called “systems technology,” or the problems arising in modern technology and society. The third aspect is called “systems philosophy” and refers to the reorientation of thought and world view.

We present a tentative proposal for a quantitative measure of autonomy. This is something that, surprisingly, is rarely found in the literature, even though autonomy is considered to be a basic concept in many disciplines, including artificial life. We work in an information theoretic setting for which the distinction between system and environment is the starting point. As a first measure for autonomy, we propose the conditional mutual information between consecutive states of the system conditioned on the history of the environment. This works well when the system cannot influence the environment at all and the environment does not interact synergetically with the system. When, in contrast, the system has full control over its environment, we should instead neglect the environment history and simply take the mutual information between consecutive system states as a measure of autonomy. In the case of mutual interaction between system and environment there remains an ambiguity regarding whether system or environment has caused observed correlations. If the interaction structure of the system is known, we define a “causal” autonomy measure which allows this ambiguity to be resolved. Synergetic interactions still pose a problem since in this case causation cannot be attributed to the system or the environment alone. Moreover, our analysis reveals some subtle facets of the concept of autonomy, in particular with respect to the seemingly innocent system–environment distinction we took for granted, and raises the issue of the attribution of control, i.e. the responsibility for observed effects. To further explore these issues, we evaluate our autonomy measure for simple automata, an agent moving in space, gliders in the game of life, and the tessellation automaton for autopoiesis of Varela et al.