Maths
maths
Eugenio Piasini
SISSA is an elite postgraduate research institution for Maths, Physics and Neuroscience, located in Trieste, Italy. The Cognitive Neuroscience Department hosts 7 research labs that study the neuronal bases of time and magnitude processing, visual perception, motivation and intelligence, language and reading, tactile perception and learning, and neural computation. The Department is highly interdisciplinary; our approaches include behavioural, psychophysics, and neurophysiological experiments with humans and animals, as well as computational, statistical and mathematical models.
Mathew Diamond
Up to 2 PhD positions in Cognitive Neuroscience are available at SISSA, Trieste, starting October 2024. SISSA is an elite postgraduate research institution for Maths, Physics and Neuroscience, located in Trieste, Italy. SISSA operates in English, and its faculty and student community is diverse and strongly international. The Cognitive Neuroscience group (https://phdcns.sissa.it/) hosts 6 research labs that study the neuronal bases of time and magnitude processing, visual perception, motivation and intelligence, language, tactile perception and learning, and neural computation. Our research is highly interdisciplinary; our approaches include behavioural, psychophysics, and neurophysiological experiments with humans and animals, as well as computational, statistical and mathematical models. Students from a broad range of backgrounds (physics, maths, medicine, psychology, biology) are encouraged to apply. The selection procedure is now open. The application deadline is 27 August 2024. Please apply here (https://www.sissa.it/bandi/ammissione-ai-corsi-di-philosophiae-doctor-posizioni-cofinanziate-dal-fondo-sociale-europeo), and see the admission procedure page (https://phdcns.sissa.it/admission-procedure) for more information. Note that the positions available for current admission round are those funded by the 'Fondo Sociale Europeo Plus', accessible through the first link above.
Mathew Diamond
Up to 6 PhD positions in Cognitive Neuroscience are available at SISSA, Trieste, starting October 2025. SISSA is an elite postgraduate research institution for Maths, Physics and Neuroscience, located in Trieste, Italy. SISSA operates in English, and its faculty and student community is diverse and strongly international. The Cognitive Neuroscience group (https://phdcns.sissa.it/) hosts 6 research labs that study the neuronal bases of time and magnitude processing, neuronal foundations of perceptual experience and learning in various sensory modalities, motivation and intelligence, language, and neural computation. Our research is highly interdisciplinary; our approaches include behavioral, psychophysics, and neurophysiological experiments with humans and animals, as well as computational, statistical and mathematical models. Students from a broad range of backgrounds (physics, maths, medicine, psychology, biology) are encouraged to apply.
Fidelity and Replication: Modelling the Impact of Protocol Deviations on Effect Size
Cognitive science and cognitive neuroscience researchers have agreed that the replication of findings is important for establishing which ideas (or theories) are integral to the study of cognition across the lifespan. Recently, high-profile papers have called into question findings that were once thought to be unassailable. Much attention has been paid to how p-hacking, publication bias, and sample size are responsible for failed replications. However, much less attention has been paid to the fidelity by which researchers enact study protocols. Researchers conducting education or clinical trials are aware of the importance in fidelity – or the extent to which the protocols are delivered in the same way across participants. Nevertheless, this idea has not been applied to cognitive contexts. This seminar discusses factors that impact the replicability of findings alongside recent models suggesting that even small fidelity deviations have real impacts on the data collected.
Maths, AI and Neuroscience Meeting Stockholm
To understand brain function and develop artificial general intelligence it has become abundantly clear that there should be a close interaction among Neuroscience, machine learning and mathematics. There is a general hope that understanding the brain function will provide us with more powerful machine learning algorithms. On the other hand advances in machine learning are now providing the much needed tools to not only analyse brain activity data but also to design better experiments to expose brain function. Both neuroscience and machine learning explicitly or implicitly deal with high dimensional data and systems. Mathematics can provide powerful new tools to understand and quantify the dynamics of biological and artificial systems as they generate behavior that may be perceived as intelligent.
Maths, AI and Neuroscience meeting
To understand brain function and develop artificial general intelligence it has become abundantly clear that there should be a close interaction among Neuroscience, machine learning and mathematics. There is a general hope that understanding the brain function will provide us with more powerful machine learning algorithms. On the other hand advances in machine learning are now providing the much needed tools to not only analyse brain activity data but also to design better experiments to expose brain function. Both neuroscience and machine learning explicitly or implicitly deal with high dimensional data and systems. Mathematics can provide powerful new tools to understand and quantify the dynamics of biological and artificial systems as they generate behavior that may be perceived as intelligent. In this meeting we bring together experts from Mathematics, Artificial Intelligence and Neuroscience for a three day long hybrid meeting. We will have talks on mathematical tools in particular Topology to understand high dimensional data, explainable AI, how AI can help neuroscience and to what extent the brain may be using algorithms similar to the ones used in modern machine learning. Finally we will wrap up with a discussion on some aspects of neural hardware that may not have been considered in machine learning.