Hsu, Ching-Lung - IBMS, Sinica

Spatial Navigation, Memory & Computing Mechanisms of Neurons

A fundamental challenge in systems neuroscience is to understand how the properties of individual cells support complex brain functions during behavior. One most extraordinary feature of the higher cognitive functions lies in our ability to achieve goals while flexibly and rapidly adapting to behavioral variables (or “contexts”) in highly dynamic environments.

How does the brain achieve it? And how is that based on experiences (i.e., learning and memory)? I propose to approach this problem by focusing on the dorsal hippocampus, one most well studied system crucial for goal-oriented spatial behavior and memory. Because of the wealthy understanding of the basic cellular physiology and behavioral correlates, the problem to deconstruct dynamic circuit computations in terms of biophysical neuron properties becomes more tractable.

For instance, hippocampal pyramidal cells are one of the few cell types in which the remarkable nonlinear input integration with subcellular precision (dendritic spikes) and their functional consequences are best understood (see Research Highlights as examples). They provide an important foundation for the proposed study.

Central problem: cellular mechanisms supporting memory and spatial navigation

How do rapidly dynamic, context-dependent circuit functions during memory-guided navigational behavior arise as consequences of electrophysiological and biophysical properties of neurons in the hippocampus?

How do the spatiotemporal structure of inputs, including microcircuit architecture (subcellular connectivities with excitatory and local inhibitory neurons) and their time-varying synaptic properties, shape the output of principal pyramidal cells during behavior?

Approaches

To resolve input-output transformation of neurons, the approaches adopted in the lab require the abilities to precisely characterizing properties of synaptic inputs, including their kinetics and spatial locations in the dendrites of the postsynaptic cell, as well as the underlying biophysical mechanisms (e.g., ion channels) and the computational properties they can confer. These are to be considered in the behavioral context or/and directly tested during behavior.

Methodologies

My lab will apply patch-clamp (intracellular) recording in acute brain slices and in awake, behaving mice, combined and complemented with an array of techniques including mouse behavior in virtual reality and real-world environments, virus-assisted neural tracing, computational modeling and random-access two-photon imaging with synaptic resolution.

Specific projects

We are now working along a few exciting directions:

1. Anterograde and retrograde transsynaptic mapping of the hippocampus: a goal is to build a constrained model with cell-type specificity to explain how the higher-dimensional selectivity of place cells may arise from microcircuit architectures

2. Integration of proprioceptive information to the spatial navigation system: in collaboration with Dr. Chih-Cheng Chen at the IBMS, we are using their transgenic tools to specifically assess how the sense of body/limb position and kinematics is used in spatial navigation and the associated neural mechanisms, taking advantage of our complementary real-world and virtual-reality approach

3. Principles of learning induced by nonlinear dendritic processing: with anatomical and electrophysiological methods, the goal is to reveal the mechanistic and computational perspectives of behaviorally relevant cellular learning rules underlying choice-dependent spatial coding in the brain