Kitanishi Lab

Graduate School of Arts and Sciences, The University of Tokyo

Circuit Mechanisms of Navigation and Memory 

We investigate the neural circuit mechanisms that support spatial navigation and memory, focusing on the hippocampus and associated brain areas. Specifically, we perform high-density, multi-area electrophysiological recordings of neuronal activity in behaving rodents to elucidate neuronal population dynamics. We also utilize/develop optogenetics, transsynaptic viral vectors, and machine learning to intervene in and decode neural circuits with high spatiotemporal precision. Through these studies, we explore the principles of information processing in the brain.


Mechanisms for generating spatial representations

Spatial recognition, such as where you are and where you are going, is an important brain function for animals to survive. The hippocampus, entorhinal cortex, and other related areas contain neurons that carry various spatial information, such as position, head direction, and speed. These cells are thought to be involved in spatial recognition and memory. However, little is known about the molecular, cellular, and circuit mechanisms that generate the information held by these neurons. We address this question using large-scale measurements of neuronal activity and viral vector-based genetic techniques in rodents (Kitanishi et al., Neuron, 2015).

Mechanism for generating hippocampal place cells

Hippocampal - parahippocampal communication

The hippocampus is thought to transmit information with various brain regions mainly via the subiculum and the entorhinal cortex. However, the mechanisms still need to be clarified since the interaction between the hippocampus and the external regions involves extensive, complex neural networks. We found how the diverse spatial information in the hippocampus is distributed to multiple downstream regions via the subiculum (Kitanishi et al., Sci Adv, 2021), which is the first study to identify information flows from the subiculum to external regions. We also found that the claustrum densely projects to the medial entorhinal cortex and regulates memory acquisition (Kitanishi et al., J Neurosci, 2017).

Information outflow from the hippocampus

Synaptic Plasticity

Where is information stored in the brain when we learn? A prevailing hypothesis is that structural changes in synapses are involved in the storage of information. If this hypothesis is correct, synaptic structure should change rapidly during learning, but this remains to be demonstrated. We focused on the fact that only a subset of neurons in the hippocampus is highly active during learning, and found that synaptic structure changes rapidly in these specific cells (Kitanishi et al., Cereb Cortex, 2009). Such network-specific synaptic plasticity may underlie learning. We have also extended our study to synaptic pathology in animal models of dementia (Kitanishi et al., Genes Cells, 2010; Kitanishi et al., Eur J Neurosci, 2009; Chen et al., Neurobiol Learn Mem, 2007).

Hippocampal pyramidal cell


The main techniques used in our laboratory are large-scale extracellular recording in behaving rodents and optogenetics / genetic engineering with viral vectors. We also apply various approaches, including anatomical tracing, molecular biology, and machine learning. Further, we continuously develop novel techniques. So far, we have established methods for: comprehensive tracking of information distribution in the brain (Kitanishi et al., Sci Adv, 2021), gene transfer into neurons that integrate monosynaptic inputs (Kitanishi et al., Commun Biol, 2022), and pathway-selective optogenetics (Kitanishi et al., J Neurosci, 2017). More recently, we have also been working on virtual reality and brain-machine interfaces.

Large-scale electrophysiological recordings 

Principal Investigator

Takuma Kitanishi, Ph.D.

Associate Professor,
Komaba Institute for Science / Department of Life Sciences,
Graduate School of Arts and Sciences,
The University of Tokyo
E-mail: tkitanishi [@]
Researchmap | Google Scholar | ORCID | ResearchGate | Neurotree

Academic appointments / Education

Mar 2022, Associate Professor, Komaba Institute for Science / Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo
Oct 2018, PRESTO Researcher, Japan Science and Technology Agency
Jan 2016, Lecturer, Department of Physiology, Graduate School of Medicine, Osaka City University
Dec 2014, Department of Molecular and Behavioral Neuroscience, Graduate School of Medicine, Osaka University
April 2013, Career-Path Promotion Unit for Young Life Scientists, Kyoto University
April 2013, JSPS research fellow (SPD)
April 2009, Post-doc, Tashiro group, Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Norwegian University of Science and Technology
Mar 2009, Ph.D. in pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo
Mar 2006, M.Sc. in pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo
Mar 2004, B.S. in pharmacology, Faculty of Pharmaceutical Sciences, The University of Tokyo


Associate Professor
Takuma Kitanishi

Assistant Professor
Izumi Iida-Watanabe

Postdoc (JSPS Research Fellow PD)
Takahiro Aimi

Lab manager
Keiko Karasawa
Kaori  Hamamura

Graduate students
[M2] Tingyu Wang
[M2] Yingwen Nong
[M1] Koharu Izumi
[M1] Tetsu Endo
[M1] Ayuna Miura

[B4] Rintaro Kishimoto
[B4] Toshihiko Shimura

Tomoko Sato (Lab manager, Aug. 2022 - April 2024)
Ryo Sato (Intern, B2, Pomona College, May 2023 - Aug. 2023)
Sho Yoshimatsu (Postdoc, Sept. 2022 - Mar. 2023)

April 2024

April 2023



We are always looking for motivated undergraduates, graduate students and postdocs. Please feel free to contact us if you are interested.


Komaba Institute for Science
Department of Life Sciences
Graduate School of Arts and Sciences
The University of Tokyo
3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
E-mail: tkitanishi [@]

Office: #007, Bldg. 3, Komaba I Campus
Tel: +81-3-5465-7563 (Ext.: 47563)
Access: A few minutes' walk from Komaba-todaimae Sta., Keio Inokashira Line