진학프로석·박사 채용 접수 플랫폼

The research group of Systems Neuroscience of IMBB under the European Program ERC-2022-STG - NEURACT entitled “Untangling population representations of objects. A closed loop approach to link neural activity to mouse behavior” (Program Coordinator Prof. Froudarakis) invites applications for one (1) part-time Postdoctoral researcher to assist with the understanding of the algorithm that the brain uses to identify objects in our visual environment under the aforementioned EU-funded ERC Research Program.

About the lab:

Our lab investigates how cortical circuits interact to form transformation-invariant object representations that can guide behavior. Natural environment contains a large number of objects with overlapping sensory input, and our brain is capable of using information from different sensory modalities to extract their identities with ease. Yet, despite extensive research in the last few decades, we are still far from having a complete understanding of how the brain creates untangled object representations. If we understood how the cortex achieves this extraordinary ability at the algorithmic level, this would represent a significant advance in our understanding of brain computation in general. To address this question, we combine advanced imaging techniques for recording neural activity with high-throughput behavioral training and computational modeling to study how the activity of large neuronal populations across different cortical regions enables behaving animals to identify and isolate objects in different contexts.

About the project:

A paramount component of intelligence is our ability to extract useful information in the world through our sensory observations. Object recognition is a fundamental problem in visual perception: every day we depend on our ability to identify objects in our visual environment, and our brain is capable of accomplishing it effortlessly and in a fraction of a second, in spite of immense variation in the sensory information that arrives in our retinas. Understanding the algorithm that the brain uses to do this complex task is a decisive conquest in neuroscience but in order to understand ethologically relevant visual processing, we need to understand how it drives behavior. Despite significant progress characterizing visual processing, we do not understand how the visual system solves visual inference problems in natural environments and we are still far from having a complete understanding of how the brain creates untangled transformation-invariant object representations in the perceptual/visual domain, that can subsequently be used to guide behavior.

Job Description:

Object recognition is a fundamental problem in visual perception and our brain is capable of accomplishing it effortlessly and in a fraction of a second, in spite of immense variation in the sensory information that arrives in our retinas. The proposed research effort aims to (i) create a state-of-the-art behavioral virtual navigation system for mice, (ii) combine it with recent advanced functional brain recording techniques and sophisticated neural data analysis to study how objects are represented in the activity of large populations of neurons across the visual hierarchy and beyond and (iii) causally relate these representations to the behavior of the animal.

Using electrophysiology, virtual reality, computational modeling, and optogenetics, the candidate will explore how visual object information is encoded across cortical areas, how these representations evolve with learning, and how real-time manipulation of neural activity affects perception and behavior.

ㆍCharacterize neural codes for visual object features (identity, position, scale, orientation) and their relation to behavioral performance.

ㆍTrack representational reorganization during learning of new objects through chronic multi-area recordings.

ㆍDevelop and apply online neural decoders to manipulate neural activity in real time within a closed-loop behavioral setup.

ㆍUse optogenetic perturbations to test the causal role of specific neural representations in object recognition.