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Joram Keijser

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🏠 MAR 5.015
📞 +49 30 314 29873

Joram Keijser is a PhD student at lab, funded by the Einstein Center for Neurosciences (ECN). He is currently investigating why inhibitory neurons display such a great diversity. Before starting his PhD, Joram obtained a bachelor’s and master’s diploma in mathematics from Leiden University. Please have a look at his personal webpage for more info.

Joram's Research

Learning Interneuron Diversity

Inhibitory interneurons show great diversity in their intrinsic properties, connectivity patterns and morphology. Modern experimental techniques have led to rapid progress in characterizing these properties, but the functional reasons for this diversity are not well understood. In this project we hypothesize that the diversity of interneurons arises as a solution to particular problems neural circuits have to solve. We investigate this hypothesis by optimizing a range of interneuron properites using advances in training spiking neural networks. Subsequent comparison of the optimal properties with experimental data can elucidate the extent to which aspects of interneuron diversity can be explained.

Perceptual Decision Making with a Continuum of Alternatives

Traditionally, the field of perceptual decision making has used relatively simple tasks involving two alternatives. While this has led to important insights into the basic neuroscience and psychology of decision making, animals often need to make more complicated decisions. We therefore collaborated with the Hayneslab (Charite Berlin) to investigate how humans make decisions when faced with a continuum of alternatives.

Context-invariance by feedback-driven modulation

in cooperation with Laura Bella Naumann

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.