Martin Haesemeyer

Martin Haesemeyer, PhD

Martin Haesemeyer

Assistant Professor, Department of Neuroscience

(614) 292-5113

190 Rightmire Hall
1060 Carmack Road
Columbus, OH 43210

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Areas of Expertise

  • Cognitive and Computational Neuroscience/Imaging
  • Molecular and Cellular Neuroscience
  • Systems Neuroscience


  • PhD: IMP, Vienna, Austria
  • Postdoctoral Training: Harvard University

Current Research Description

Thermoregulation is a homeostatic process that is critical for the survival of all animals. Its deregulation after injury or as side-effects of psychiatric disorders such as Schizophrenia has severe adverse effects. We know from our own experience, that being too hot or too cold is decidedly uncomfortable. We therefore actively seek and create environments of comfortable temperature. On a basic level, this involves detecting environmental temperature, processing this information and eliciting appropriate thermoregulatory behaviors. In spite of its importance, we know very little about how brains control behavioral thermoregulation.

To address this question, we perform research in the small vertebrate larval zebrafish. Zebrafish have an archetypical vertebrate brain in which subcortical structures that are implicated in mammalian thermoregulation are highly conserved. Importantly, larval zebrafish has a critical advantage: Due to its transparency and small size, we can investigate information processing throughout the entire brain at cellular resolution via calcium imaging in a behaving animal. This gives us unprecedented insight into the question of how brains process temperature stimuli to thermoregulate. We currently investigate how neurons interact in circuits to allow zebrafish to seek out a preferred temperature. Like us, inflammation triggers behavioral fever responses in zebrafish: They actively seek out warmer temperatures. We use this model to investigate how changes in internal states affect homeostatic processing.

Our research involves gathering large behavioral and neural activity datasets in behaving larval zebrafish through calcium imaging and electrophysiology. Using machine learning techniques, we use these data to create quantitative models that explain stimulus processing and how this processing is influenced by internal states.

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