Linking neuroscience and psychoanalysis from a developmental perspective: Why and how?

This paper aims to develop the rational to support why and how we should link neuroscience and psychoanalysis. Many of these points are derived from child development and child psychiatry. Neuroscience investigates developmental questions in a different way than psychoanalysis, while psychoanalysis itself has shifted towards new developmental paradigms. The rapprochement between neuroscience and psychoanalysis allows a new understanding of some concepts, including embodiment of mind, consciousness and attachment. The “double reading” paradigm allows a better understanding of symptomatic configurations. Linking neuroscience and psychoanalysis may improve treatments and result in new experimental neuroscientific paradigms involving changing the research object, changing the state of the research object, and investigating the structural changes in the brain following psychotherapy. The last aim is to create an epistemology of the articulation between the theoretical frameworks through phenomenology, “complementarism” and neuropsychoanalysis. We argue that it is necessary for clinicians to be aware of the advancements in each field. This is not only an epistemological question; we assume that new findings in neuroscience will change the way psychoanalysts think and approach treatment of their patients. We hope the present research will contribute to change the way that neuroscientists think and will provide new options to their set of experimental paradigms.     

Internal models for motor control and trajectory planning

A number of internal model concepts are now widespread in neuroscience and cognitive science. These concepts are supported by behavioral, neurophysiological, and imaging data; furthermore, these models have had their structures and functions revealed by such data. In particular, a specific theory on inverse dynamics model learning is directly supported by unit recordings from cerebellar Purkinje cells. Multiple paired forward inverse models describing how diverse objects and environments can be controlled and learned separately have recently been proposed. The 'minimum variance model' is another major recent advance in the computational theory of motor control. This model integrates two furiously disputed approaches on trajectory planning, strongly suggesting that both kinematic and dynamic internal models are utilized in movement planning and control.     

For the law, neuroscience changes nothing and everything

The rapidly growing field of cognitive neuroscience holds the promise of explaining the operations of the mind in terms of the physical operations of the brain. Some suggest that our emerging understanding of the physical causes of human (mis)behaviour will have a transformative effect on the law. Others argue that new neuroscience will provide only new details and that existing legal doctrine can accommodate whatever new information neuroscience will provide. We argue that neuroscience will probably have a transformative effect on the law, despite the fact that existing legal doctrine can, in principle, accommodate whatever neuroscience will tell us. New neuroscience will change the law, not by undermining its current assumptions, but by transforming people's moral intuitions about free will and responsibility. This change in moral outlook will result not from the discovery of crucial new facts or clever new arguments, but from a new appreciation of old arguments, bolstered by vivid new illustrations provided by cognitive neuroscience. We foresee, and recommend, a shift away from punishment aimed at retribution in favour of a more progressive, consequentialist approach to the criminal law.     

The Selective Allure of Neuroscientific Explanations

Some claim that recent advances in neuroscience will revolutionize the way we think about human nature and legal culpability. Empirical support for this proposition is mixed. Two highly-cited empirical studies found that irrelevant neuroscientific explanations and neuroimages were highly persuasive to laypersons. However, attempts to replicate these effects have largely been unsuccessful. Two separate experiments tested the hypothesis that neuroscience is susceptible to motivated reasoning, which refers to the tendency to selectively credit or discredit information in a manner that reinforces preexisting beliefs. Participants read a newspaper article about a cutting-edge neuroscience study. Consistent with the hypothesis, participants deemed the hypothetical study sound and the neuroscience persuasive when the outcome of the study was congruent with their prior beliefs, but gave the identical study and neuroscience negative evaluations when it frustrated their beliefs. Neuroscience, it appears, is subject to the same sort of cognitive dynamics as other types of scientific evidence. These findings qualify claims that neuroscience will play a qualitatively different role in the way in which it shapes people’s beliefs and informs issues of social policy.     

Kuhnian revolutions in neuroscience: the role of tool development

The terms “paradigm” and “paradigm shift” originated in “The Structure of Scientific Revolutions” by Thomas Kuhn. A paradigm can be defined as the generally accepted concepts and practices of a field, and a paradigm shift its replacement in a scientific revolution. A paradigm shift results from a crisis caused by anomalies in a paradigm that reduce its usefulness to a field. Claims of paradigm shifts and revolutions are made frequently in the neurosciences. In this article I will consider neuroscience paradigms, and the claim that new tools and techniques rather than crises have driven paradigm shifts. I will argue that tool development has played a minor role in neuroscience revolutions.     

Consumer nueroscience: a new area of study for biomedical engineers

In scientific literature, the most accepted definition of consumer neuroscience or neuromarketing is that it is a field of study concerning the application of neuroscience methods to analyze and understand human behavior related to markets and marketing exchanges. First, it might seem strange that marketers would be interested in using neuroscience to understand consumer's preferences. Yet in practice, the basic goal of marketers is to guide the design and presentation of products in such a way that they are highly compatible with consumer preferences. To understand consumers preferences, several standard research tools are commonly used by marketers, such as personal interviews with the consumers, scoring questionnaries gathered from consumers, and focus groups. The reason marketing researchers are interested in using brain imaging tools instead of simply asking people for their preferences in front of marketing stimuli, arises from the assumption that people cannot (or do not want to) fully explain their preference when explicitly asked. Researchers in the field hypothesize that neuroimaging tools can access information within the consumer's brain during the generation of a preference or the observation of a commercial advertisement. The question of will this information be useful in further promoting the product is still up for debate in marketing literature. From the marketing researchers point of view, there is a hope that this body of brain imaging techniques will provide an efficient tradeoff between costs and benefits of the research. Currently, neuroscience methodology includes powerful brain imaging tools based on the gathering of hemodynamic or electromagnetic signals related to the human brain activity during the performance of a relevant task for marketing objectives. These tools are briefly reviewed in this article.     

Quantum physics in neuroscience and psychology: a neurophysical model of mind-brain interaction

Neuropsychological research on the neural basis of behaviour generally posits that brain mechanisms will ultimately suffice to explain all psychologically described phenomena. This assumption stems from the idea that the brain is made up entirely of material particles and fields, and that all causal mechanisms relevant to neuroscience can therefore be formulated solely in terms of properties of these elements. Thus, terms having intrinsic mentalistic and/or experiential content (e.g. 'feeling', 'knowing' and 'effort') are not included as primary causal factors. This theoretical restriction is motivated primarily by ideas about the natural world that have been known to be fundamentally incorrect for more than three-quarters of a century. Contemporary basic physical theory differs profoundly from classic physics on the important matter of how the consciousness of human agents enters into the structure of empirical phenomena. The new principles contradict the older idea that local mechanical processes alone can account for the structure of all observed empirical data. Contemporary physical theory brings directly and irreducibly into the overall causal structure certain psychologically described choices made by human agents about how they will act. This key development in basic physical theory is applicable to neuroscience, and it provides neuroscientists and psychologists with an alternative conceptual framework for describing neural processes. Indeed, owing to certain structural features of ion channels critical to synaptic function, contemporary physical theory must in principle be used when analysing human brain dynamics. The new framework, unlike its classic-physics-based predecessor, is erected directly upon, and is compatible with, the prevailing principles of physics. It is able to represent more adequately than classic concepts the neuroplastic mechanisms relevant to the growing number of empirical studies of the capacity of directed attention and mental effort to systematically alter brain function.     

The status of cognitive neuroscience.

Cognitive neuroscience rests on findings, methods, and theory from three fields: experimental psychology, systems-level neuroscience, and computer science. The strong trend over the past few years has been for a greater integration across these fields. The influence of this interdisciplinary approach on current research on memory, perception, and language will be illustrated.     

The Biophysics of Regenerative Repair Suggests New Perspectives on Biological Causation

Evolution exploits the physics of non-neural bioelectricity to implement anatomical homeostasis: a process in which embryonic patterning, remodeling, and regeneration achieve invariant anatomical outcomes despite external interventions. Linear “developmental pathways” are often inadequate explanations for dynamic large-scale pattern regulation, even when they accurately capture relationships between molecular components. Biophysical and computational aspects of collective cell activity toward a target morphology reveal interesting aspects of causation in biology. This is critical not only for unraveling evolutionary and developmental events, but also for the design of effective strategies for biomedical intervention. Bioelectrical controls of growth and form, including stochastic behavior in such circuits, highlight the need for the formulation of nuanced views of pathways, drivers of system-level outcomes, and modularity, borrowing from concepts in related disciplines such as cybernetics, control theory, computational neuroscience, and information theory. This approach has numerous practical implications for basic research and for applications in regenerative medicine and synthetic bioengineering.     

On directed information theory and Granger causality graphs

Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.     

Towards new concepts for a biological neuroscience of consciousness

In the search for a sound model of consciousness, we aim at introducing new concepts: closure, compositionality, biobranes and autobranes. This is important to overcome reductionism and to bring life back into the neuroscience of consciousness. Using these definitions, we conjecture that consciousness co-arises with the non-trivial composition of biological closure in the form of biobranes and autobranes: conscious processes generate closed activity at various levels and are, in turn, themselves, supported by biobranes and autobranes. This approach leads to a non-reductionist biological and simultaneously phenomenological theory of conscious experience, giving new perspectives for a science of consciousness. Future works will implement experimental definitions and computational simulations to characterize these dynamical biobranes interacting.     

Functional brain networks: great expectations,hard times and the big leap forward

Many physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks has generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain. We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation of complex network theory, to account for its exquisitely complex functioning mode.     

The restless brain: how intrinsic activity organizes brain function

Traditionally studies of brain function have focused on task-evoked responses. By their very nature such experiments tacitly encourage a reflexive view of brain function. While such an approach has been remarkably productive at all levels of neuroscience, it ignores the alternative possibility that brain functions are mainly intrinsic and ongoing, involving information processing for interpreting, responding to and predicting environmental demands. I suggest that the latter view best captures the essence of brain function, a position that accords well with the allocation of the brain''s energy resources, its limited access to sensory information and a dynamic, intrinsic functional organization. The nature of this intrinsic activity, which exhibits a surprising level of organization with dimensions of both space and time, is revealed in the ongoing activity of the brain and its metabolism. As we look to the future, understanding the nature of this intrinsic activity will require integrating knowledge from cognitive and systems neuroscience with cellular and molecular neuroscience where ion channels, receptors, components of signal transduction and metabolic pathways are all in a constant state of flux. The reward for doing so will be a much better understanding of human behaviour in health and disease.     

Integrating recent advances in neuroscience into undergraduate neuroscience and physiology courses

Neuroscience has enjoyed tremendous growth over the past 20 years, including a substantial increase in the number of neuroscience departments, programs, and courses at the undergraduate level. To meet the need of new neuroscience courses, there has also been growth in the number of introductory neuroscience textbooks designed for undergraduates. However, textbooks typically trail current knowledge by five to ten years, especially in neuroscience where our understanding is increasing rapidly. Consequently, it is often important to supplement neuroscience and physiology textbooks with information about recent findings in neuroscience. To design supplementary educational material, it is essential first to identify the educational objectives of the program and the characteristics of the learners, which can differ dramatically between undergraduate and graduate or professional students. Four principles that may serve the selection and design of supplementary material for undergraduate neuroscience and physiology courses are that (1) material must be interesting to the undergraduates, (2) material should reinforce previously learned concepts, (3) students must be adequately prepared, and (4) the teacher and student must have sufficient appropriate resources.     

Selected effects and causal role functions in the brain: the case for an etiological approach to neuroscience

Despite the voluminous literature on biological functions produced over the last 40 years, few philosophers have studied the concept of function as it is used in neuroscience. Recently, Craver (forthcoming; also see Craver 2001) defended the causal role theory against the selected effects theory as the most appropriate theory of function for neuroscience. The following argues that though neuroscientists do study causal role functions, the scope of that theory is not as universal as claimed. Despite the strong prima facie superiority of the causal role theory, the selected effects theory (when properly developed) can handle many cases from neuroscience with equal facility. It argues this by presenting a new theory of function that generalizes the notion of a ‘selection process’ to include processes such as neural selection, antibody selection, and some forms of learning—that is, to include structures that have been differentially retained as well as those that have been differentially reproduced. This view, called the generalized selected effects theory of function, will be defended from criticism and distinguished from similar views in the literature.     

Neurocognitive insights on conceptual knowledge and its breakdown

Conceptual knowledge reflects our multi-modal ‘semantic database’. As such, it brings meaning to all verbal and non-verbal stimuli, is the foundation for verbal and non-verbal expression and provides the basis for computing appropriate semantic generalizations. Multiple disciplines (e.g. philosophy, cognitive science, cognitive neuroscience and behavioural neurology) have striven to answer the questions of how concepts are formed, how they are represented in the brain and how they break down differentially in various neurological patient groups. A long-standing and prominent hypothesis is that concepts are distilled from our multi-modal verbal and non-verbal experience such that sensation in one modality (e.g. the smell of an apple) not only activates the intramodality long-term knowledge, but also reactivates the relevant intermodality information about that item (i.e. all the things you know about and can do with an apple). This multi-modal view of conceptualization fits with contemporary functional neuroimaging studies that observe systematic variation of activation across different modality-specific association regions dependent on the conceptual category or type of information. A second vein of interdisciplinary work argues, however, that even a smorgasbord of multi-modal features is insufficient to build coherent, generalizable concepts. Instead, an additional process or intermediate representation is required. Recent multidisciplinary work, which combines neuropsychology, neuroscience and computational models, offers evidence that conceptualization follows from a combination of modality-specific sources of information plus a transmodal ‘hub’ representational system that is supported primarily by regions within the anterior temporal lobe, bilaterally.     

Complexity and information in regular and random phyllotactic patterns.

In biology, the theory of information has been used to study the degree of order of many living systems. Different concepts of entropy have been applied to the analysis of phyllotaxis. In the present paper we will determine the degree of order of disorganized patterns by using informational entropy concepts deduced from the work of Brillouin, Shannon, and Yagil. As case studies, we will apply these concepts of entropy to the disorganized patterns found in mutants of Arabidopsis. The calculation of entropy gives a precise idea of the degree of order of a phyllotactic system.     

Bipedal locomotion: toward unified concepts in robotics and neuroscience

This review is the result of a joint reflection carried out by researchers in the fields of robotics and automatic control on the one hand and neuroscience on the other, both trying to answer the same question: what are the functional bases of bipedal locomotion and how can they be controlled? The originality of this work is to synthesize the two approaches in order to take advantage of the knowledge concerning the adaptability and reactivity performances of humans and of the rich tools and formal concepts available in biped robotics. Indeed, we claim that the theoretical framework of robotics can enhance our understanding of human postural control by formally expressing the experimental concepts used in neuroscience. Conversely, biological knowledge of human posture and gait can inspire biped robot design and control. Therefore, both neuroscientists and roboticists should find useful information in this paper.     

Proteomics of multiprotein complexes: answering fundamental questions in neuroscience

Proteomics tools offer new ways to analyse networks of proteins that control important neurobiological phenomena such as learning and memory. In this review, we discuss how a combined proteomic, pharmacological and genetic approach reveals that multiprotein complexes process neural information and encode memories. Simultaneous analysis of multiple proteins enables the development of new concepts and approaches for neuroscience research.     

Proteomics of multiprotein complexes: answering fundamental questions in neuroscience

Proteomics tools offer new ways to analyse networks of proteins that control important neurobiological phenomena such as learning and memory. In this review, we discuss how a combined proteomic, pharmacological and genetic approach reveals that multiprotein complexes process neural information and encode memories. Simultaneous analysis of multiple proteins enables the development of new concepts and approaches for neuroscience research.