Large-scale recordings of neurons to reveal mechanisms of learning and memory

In this project, the candidate will join an interdisciplinary team of experimental and computational experts to decipher neural fingerprints of memory processing.

Animals will be trained in a memory tasks in tasks which combine virtual reality with analog cues. Neuron activity will be measured during all stages of memory processing. 

The ability to form and store memories is essential to define one-self, relate to others and to make decisions based on previous experiences. Encoding, consolidation and storage of memories cross many time scales from millisecond precision to long-term memories and are performed by an intermingled network of inhibitory and excitatory neurons. How these processes are synchronized and how neuron representations change over time are not known. In our laboratory, we use novel large-scale recording techniques to simultaneously record from hundreds to several thousand neurons in behaving rodents.

The data obtained holds unprecedented temporal and spatial resolution enabling deciphering population dynamics. However, with increasing complexity and high dimensionality of inputs, single neuron responses become meaningless and population responses are necessary to understand underlying computations. This calls for novel analysis tools integrating models into the analyses to predict neural responses and thereby find novel representations.

In this project, the candidate will join an interdisciplinary team of experimental and computational experts to decipher neural fingerprints of memory processing. With a computational proficiency, the candidate will analyse data and develop and explore theoretical models to generate predictions that will be tested in the laboratory. Lack of knowledge of contributing elements to memory processing often limits the biological relevance of theoretical models. Here, the experimental data will give unprecedented resolution which will be directly explored in the models. This close and integrated experimental and computational approach will give an unique opportunity for deep insight into brain function.

The project include collaborations with leading groups at Harvard University and the University of California San Diego.

Requirements

  • You must have a master degree in physics or neuroscience.
  • Documented experience from computational modeling in statistical physics, scientific programming or developing and implementing advanced analysis on experimental data is an advantage.

Supervisors

Professor Marianne Fyhn

Professor Anders Malthe-Sørsenssen

Call 1: Project start autumn 2021

This project is in call 1, starting autumn 2021. Read about how to apply

Published Aug. 17, 2020 4:52 PM - Last modified Oct. 14, 2020 10:16 AM