Supervisor: Mark van Rossum
School of Psychology and Mathematical Sciences

Description:

Both biological and artificial neural networks learn by changing the strength of connections between their neurons. Recently, it has emerged that in biology this modification of connections is an energy-costly process (Mery et al). As energy constraints are believed to have shaped many aspects of the brain’s design (see refs), this raises the question: how can the brain learn while being frugal with the modifications?
In this project we will use analytical calculations and simulations to see how different network architectures require different amount of energy to learn to perform well on standard tasks. Next we will examine how different modification rules (learning rules) affect energy consumption.
We hope that the outcomes of this research will lead to deeper insight in how the brain implements learning; at the same time we hope that the findings will inspire machine learning.

Requirements:

• Candidates are expected to have a strong quantitative background and an affinity to biology and machine learning/AI. Python/matlab skills desirable.
• No prior biology knowledge required.

Reading:

• Lennie, Peter. “The cost of cortical computation.” Current biology 13.6 (2003): 493-497.
• Laughlin, Simon B. “Energy as a constraint on the coding and processing of sensory information.” Current opinion in neurobiology 11.4 (2001): 475-480.
• Mery, F., & Kawecki, T. J. (2005). A cost of long-term memory in Drosophila. Science, 308(5725), 1148-1148

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