Research Program Overview

Our unique memories make us who we are. Representations of the external world are stored in the brain by billions of neurons with diverse morphologies, molecular compositions and physiological roles. Each of these neurons forms up to several thousand synaptic contacts that transmit signals with remarkable speed and precision. While the basic principles of synaptic communication are well-established, it is still unclear how multiplex neural circuits assemble and support cognitive tasks.

Why do we want to know? Aside from natural curiosity to decipher the most mysterious organ in the human body, we and many other neuroscientists are driven by the realization that comprehensive knowledge about brain wiring is essential for designing effective strategies to treat neurological disorders. Imagine that you need to fix a broken car but no one in the world has a clue how engines are built …

The Maximov laboratory seeks to uncover the mechanisms that regulate neuronal connectivity at molecular, cellular and circuit levels. We primarily focus on the hippocampus, a laminated structure within the limbic system of the brain that is critical for memory, emotions and navigation. We study the architecture and function of hippocampal circuits by using a variety of genetic, biophysical and behavioral techniques. We are particularly interested in understanding how synaptic networks of specific excitatory and inhibitory neuron subtypes are reorganized during learning, and how these experience-dependent events contribute to memory coding.

We heavily rely on optical imaging, serial electron microscopy and computational tools to define the role of experience in wiring of the hippocampal pathway at scales ranging from global to subcellular. Much of this work is done in collaboration with Dr. Mark Ellisman and his colleagues at the UCSD Department of Neurosciences and National Center for Microscopy and Imaging Research.

In parallel, we elucidate the molecular bases of network plasticity and information storage by combining human genetics data, unbiased screens, and analysis of candidate genes in mouse models.  As a part of this research program, we also design new tools to manipulate genetically-defined and behaviorally-relevant neural ensembles with small molecules.