Welcome to the Convergent Science Network Podcast!
During the BCBT Summerschools (2010 , 2011 and 2012) hosting professors Paul Verschure and Tony Prescott interviewed several of the speakers after their lectures. But also on other occasions interviews with various scientist in converging fields are conducted.
About the hosts: Paul Verschure is ICREA Professor at SPECS, Universitat Pompeu Fabra, Barcelona; Tony Prescott is Professor of Cognitive Neuroscience, University of Sheffield.
The Convergent Science Network of biomimetic and biohybrid systems (CSN, www.csnetwork.eu) is a coordination action for the development of future real-world technologies. CSN is supported through the Future and Emerging Technology programs (FET) of the Information and Communication Technologies (ICT) work programme of Framework Programme 7 of the European Commission.
Audio (post-)production: Sytse Wierenga. Podcast site: Alberto Betella.
Thoughts, discussions, and achievements in neurobiology, biomimetic and biohybrid systems
We can learn a lot from brains and bodies when making machines and robots. But reversely, building complex machine systems can also give ideas about how brains and bodies have implemented their functioning over the evolution of ages. This podcast discusses various themes and aspects in-between robotics, neuroscience, cognitive science, artificial intelligence, biology, and technology.
Duration: 43:33 m - Filetype: mp3 - Bitrate: 160 KBPS - Frequency: 44100 HZ
Interview with Xiao-Jing Wang
This post-lecture interview was conducted during the BCBT Summerschool held at the Universitat Pompeu Fabra, Barcelona, september 2010.
When you have the capability to hold something in your mind without input from the world, then you are free from immediacy, and free to develop a flexible response. This fundamental role of memory circuitry forms the core interest of Xiao-Jing Wang's research. With Paul Verschure he discusses how memory systems differ between sensory, motor, and cortical processing areas that are involved with decision making. Coming from a background in physics, Xiao-Jing Wang (Yale University School of Medicine, USA) has assessed neural structure modeling using attractor network dynamics, in order to describe mathematically a system that can be in a number of different persistent states. Besides fast switches, also slow transients can be found in circuitries that integrate information in decision making over time. Experimental paradigms are discussed to test Wang's model with respect to recurrent neuro-circuitry, coherence of stimuli, and reward dependent plasticity, as well as the connection to what is observed at the behavioral level.
About the lecturer
He is a professor at the Department of Neurobiology and Kavli Institute for Neuroscience, Yale University School of Medicine. His interests are in dynamics, computation and memory in cortical neural circuits, with an emphasis on working memory, decision making and the role of prefrontal cortex in cognition.