Job ID: 99260

Fully funded PhD fellowship (4 years) in computational and systems neuroscience

Position: Ph.D. Student

Deadline: 31 December 2022

Employment Start Date: 1 July 2023

Contract Length: 4 years

City: Barcelona

Country: Spain

Institution: Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)



Project “The neural basis of working memory history biases as components of statistical learning” funded by the Spanish Agency for Research (AEI).

The candidate should have a Master’s Degree in Neuroscience, Cognitive Science, Data Science or similar, have good programming skills, be fluent in English and have a genuine interest for cognitive research from a quantitative biological approach.

The project investigates the neural basis of history biases in working memory and their relation to sequence prediction using combined computational modeling, analysis of neurophysiological data from monkey experiments, and EEG and intracranial recording experiments in humans. See project summary below.

We offer a full time four-year position with a competitive PhD salary. Starting date is negotiable, but should be before September 2023.

Our lab offers an interdisciplinary stimulating environment, that values rigor, collaboration, close mentoring and critical but positive thinking. We are also a active member of the Barcelona Cognitive Computational and Systems Neuroscience community ( which connects a large number of groups in Barcelona with common interests and complementary approaches.

“The neural basis of working memory history biases as components of statistical learning”:

Our brain excels at finding structure and meaning in incoming streams of sensory information without any explicit teaching signal. Such statistical learning underlies language and other high-order functions and has been shown to be defective in various mental disorders (e.g. autism, schizophrenia), but little progress has been made in understanding its neural network basis. This project aims to provide a neural network understanding of statistical learning by defining recently described attractive history biases in working memory as a proxy of statistical learning. By harnessing the strong theoretical basis of working memory, we will combine experiments in humans, data analysis of neural spiking data and computational modeling to test the hypotheses that short-term working memory history biases are supported by synaptic plasticity mechanisms operating in the local prefrontal circuit, while longer-term working memory history biases depend on cortico-cortical interactions with other brain areas, and these mechanisms contribute to statistical learning. To test this, we will pursue two aims: (1) define neural network dynamics supporting working memory history biases using computational models constrained by human electrophysiological and magnetic resonance imaging recordings; and (2) test the association of working memory history biases with sequence prediction in healthy statistical learning. By integrating data from brain structure, neural function and behaviour in humans and monkeys, we will deliver mechanistically consistent biophysical network models of statistical learning. This knowledge will foster understanding of the biology of sequential cognitive processing, and provide tools to advance towards mechanistically informative behavioural biomarkers that allow stratification of persons with deficits in statistical learning.