Project: Discovering the cost function optimized by the primate visual system's deep hierarchical architecture
The visual cortex has a distinctive deep hierarchical organization as a result of ontogenetic and phylogenetic optimization. It is unclear what the factors are that shape this particular hierarchical organization. One factor is the compositional and hierarchical nature of our world’s appearance, which may be optimally processed by a hierarchical visual system. Another factor is the need for space and energy efficiency, which constrains the number of neurons and connections. The project will employ computational modeling to understand the contribution of these constraints to shaping the combination of breadth, depth, and skipping connections employed by primate visual cortex.
The project involves developing and training over-parameterized deep neural network architectures and studying the emergence of hierarchical or non-hierarchical connectivity as a consequence of optimization on different visual tasks under different regularization constraints.
This project is NOT accepting applications.
- Professor Nikolaus Kriegeskorte
- Department/School: Psychology, Neuroscience, Electrical Engineering
- Location: Zuckerman Mind Brain Behavior Institute
- We develop neural network models of how visual inference is performed by biological brains, test such models in terms of their ability to predict detailed representations in human and primate brains measured with functional imaging and cell recording, and develop statistical analysis and visualization methods.
- Earliest starting date: 03/01/2019
- End date: 08/31/2019
- Number of hours per week of research expected during Spring 2019: ~36
- Number of hours per week of research expected during Summer 2019: ~36
- Skill sets: computer science, deep neural network modeling, PyTorch or Tensorflow.
- Student eligibility (as of Spring 2019):
freshman, sophomore, junior, senior, master’s
- International students on F1 or J1 visa: eligible