Brain matters…in social sciences

Here we offer a general introduction to cognitive neuroscience and provide examples relevant to psychology, healthcare and bioethics, law and criminology, information studies, of how brain studies have influenced, are influencing or show the potential to influence the social sciences. We argue that social scientists should read, and be enabled to understand, primary sources of evidence in cognitive neuroscience. We encourage cognitive neuroscientists to reflect upon the resonance that their work may have across the social sciences and to facilitate a mutually enriching interdisciplinary dialogue

The Philosophy and Neuroscience Movement

A movement dedicated to applying neuroscience to traditional philosophical problems and using philosophical methods to illuminate issues in neuroscience began about twenty-five years ago. Results in neuroscience have affected how we see traditional areas of philosophical concern such as perception, belief-formation, and consciousness. There is an interesting interaction between some of the distinctive features of neuroscience and important general issues in the philosophy of science. And recent neuroscience has thrown up a few conceptual issues that philosophers are perhaps best trained to deal with. After sketching the history of the movement, we explore the relationships between neuroscience and philosophy and introduce some of the specific issues that have arise

The philosophy of language and the Ontology of Knowledge

Objective The relations between thought and reality are studied in many fields of philosophy and science. Examples include ontology and metaphysics in general, linguistics, neuroscience and even mathematics. Each one has its postulates, its language, its methods and its own constraints. It would be unreasonable, however, for them to ignore each other. In the pages that follow we will try to identify areas of proximity between the ideas of contemporary philosophers of language and those issued mainly by Ontology of Knowledge but also by mathematics and neuroscience. We will try to take advantage of the clarity and the perfect structuring of the lecture « La philosophie contemporaine du langage » (the contemporary philosophy of language) given by Professor Denis Vernant . We will make use of this lecture, both for the ideas presented and as a reference process. The goal of this article is to bring out, through a benevolent confrontation, new ideas for the benefit of knowledge in general

A Roadmap for Integrating Neuroscience into Addiction Treatment:A Consensus of the Neuroscience Interest Group of the International Society of Addiction Medicine

Although there is general consensus that altered brain structure and function underpins addictive disorders, clinicians working in addiction treatment rarely incorporate neuroscience-informed approaches into their practice. We recently launched the Neuroscience Interest Group within the International Society of Addiction Medicine (ISAMNIG) to promote initiatives to bridge this gap. This article summarises the ISAM-NIG key priorities and strategies to achieve implementation of addiction neuroscience knowledge and tools forthe assessment and treatment of substance use disorders. We cover two assessment areas: cognitive assessment and neuroimaging, and two interventional areas: cognitive training/remediation and neuromodulation, where we identify key challenges and proposed solutions. We reason that incorporating cognitive assessment into clinical settings requires the identification of constructs that predict meaningful clinical outcomes. Other requirements are the development of measures that are easily-administered, reliable and ecologically-valid. Translation of neuroimaging techniques requires the development of diagnostic and prognostic biomarkers and testing the cost-effectiveness of these biomarkers in individualised prediction algorithms for relapse prevention and treatment selection. Integration of cognitive assessments with neuroimaging can provide multilevel targets including neural, cognitive, and behavioural outcomes for neuroscience-informed interventions. Application of neuroscience-informed interventions including cognitive training/remediation and neuromodulation requires clear pathways to design interventions based on multilevel targets, additional evidence from randomised trials and subsequent clinical implementation, including evaluation of cost-effectiveness. We propose to address these challenges by promoting international collaboration between researchers and clinicians, developing harmonised protocols and data management systems, and prioritising multi-site research that focuses on improving clinical outcomes

On the interpretability and computational reliability of frequency-domain Granger causality

This is a comment to the paper 'A study of problems encountered in Granger causality analysis from a neuroscience perspective'. We agree that interpretation issues of Granger Causality in Neuroscience exist (partially due to the historical unfortunate use of the name 'causality', as nicely described in previous literature). On the other hand we think that the paper uses a formulation of Granger causality which is outdated (albeit still used), and in doing so it dismisses the measure based on a suboptimal use of it. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even written in good faith, became a wildcard against all possible applications of Granger Causality, regardless of the hard work of colleagues aiming to seriously address the methodological and interpretation pitfalls. In order to provide a balanced view, we replicated their simulations used the updated State Space implementation, proposed already some years ago, in which the pitfalls are mitigated or directly solved

Analyzing Stability of Equilibrium Points in Neural Networks: A General Approach

Networks of coupled neural systems represent an important class of models in computational neuroscience. In some applications it is required that equilibrium points in these networks remain stable under parameter variations. Here we present a general methodology to yield explicit constraints on the coupling strengths to ensure the stability of the equilibrium point. Two models of coupled excitatory-inhibitory oscillators are used to illustrate the approach.Comment: 20 pages, 4 figure

Summary of Information Theoretic Quantities

Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical structure to function make it ideally suited for studying information flow in the nervous system. As a framework it has a number of useful properties: it provides a general measure sensitive to any relationship, not only linear effects; its quantities have meaningful units which in many cases allow direct comparison between different experiments; and it can be used to study how much information can be gained by observing neural responses in single experimental trials, rather than in averages over multiple trials. A variety of information theoretic quantities are in common use in neuroscience - including the Shannon entropy, Kullback-Leibler divergence, and mutual information. In this entry, we introduce and define these quantities. Further details on how these quantities can be estimated in practice are provided in the entry "Estimation of Information-Theoretic Quantities" and examples of application of these techniques in neuroscience can be found in the entry "Applications of Information-Theoretic Quantities in Neuroscience".Comment: 7 page

Sustainability and transparency in computational cognitive neuroscience

In this talk, I will discuss open science practices that aim to foster sustainability and transparency in computational cognitive neuroscience. First, I will review recent community efforts that aim to ease data sharing and analytical reproducibility, such as the reports of the OHBM Committees on Best Practice in Data Analysis and Sharing (COBIDAS) and the Brain Imaging Data Structures (BIDS). Second, I will discuss neuroimaging data sharing strategies in the light of ethical and legal constraints, such as the European General Data Protection Regulation (GDPR). Finally, I will discuss some common-sense guidelines for day-to-day research practice that aim to maximize the societal impact of computational cognitive neuroscience

Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics

We introduce a dynamic neural algorithm called Dynamic Neural (DN) SARSA(\lambda) for learning a behavioral sequence from delayed reward. DN-SARSA(\lambda) combines Dynamic Field Theory models of behavioral sequence representation, classical reinforcement learning, and a computational neuroscience model of working memory, called Item and Order working memory, which serves as an eligibility trace. DN-SARSA(\lambda) is implemented on both a simulated and real robot that must learn a specific rewarding sequence of elementary behaviors from exploration. Results show DN-SARSA(\lambda) performs on the level of the discrete SARSA(\lambda), validating the feasibility of general reinforcement learning without compromising neural dynamics.Comment: Sohrob Kazerounian, Matthew Luciw are Joint first author