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Laura Bella Naumann

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🏠 MAR 5.015
📞 +49 30 314 29873
🐦 @LBNaumann

Laura Naumann studied Mathematics in Medicine and Life Science at Universität zu Lübeck. After finishing her bachelor’s degree in 2014 she joined the master program Computational Neuroscience at the Bernstein Center in Berlin. During her studies she visited the group for a lab rotation and later master thesis exploring the computational consequences of different inhibitory circuits. Following the master’s degree Laura began her doctoral studies in April 2017. She is investigating the functional implications of presynaptic inhibition in recurrent networks using mathematical analyses and numerical simulations.

Laura's Research

Context-invariance by feedback-driven modulation

in cooperation with Joram Keijser

Context-invariance in sensory processing is often believed to arise from hierarchical feedforward processing. However, sensory processing is also affected by feedback projections, even at the early stages. In this project we explore the hypotehsis that feedback-driven modulation of feedforward processing can enable context-invariant perception. By training network models with feedback modulation, we investigate whether the required spatial and temporal scale of the suggested mechanism is compatible with biological neuromodulation. The goal of this project is to propose an active role of feedback modulation in sensory processing and determine how the mechanism could be dissociated in the brain.

Presynaptic Inhibition as a rapid compensatory process

Presynaptic GABA(B) receptors have been shown to modulate the activity of neural circuits by inhibiting calcium channels which causes a reduction in neurotransmitter release. Activation of presynaptic GABA(B) receptors at excitatory synapses occurs as a result of GABA spillover from nearby inhibitory synapses. Therefore presynaptic inhibition is a compensatory process that multiplicatively alters effective synaptic strength. We investigate the functional implications of such a mechanism using simulations of biologically inspired spiking networks and mathematical analyses of simplified phenomenological models.