We design a novel planar microelectrode array (MEA) system that can be used to grow live neurons and record neural signals from multiple neurons simultaneously. Electrical and optical stimulation and recording techniques are combined with the MEA platform to investigate neural circuits in vitro. 60 to 256 channel MEAs have been successfully designed with nanostructure microelectrode electrodes (e.g. conductive polymer PEDOT:PSS).
We want to build live biological neuronal networks in vitro with ordered structures. We believe that the cultured neuronal network is a good model system to study basic principles of learning and memory at a network level. Micro-contact printing technique is the major method to print biomolecules that can either promote or prohibit neuronal growth. Surface chemical cues and topographical cues are the design rules for the guided neural growth in vitro. We are interested in discovering new physical and chemical guidance cues that lead to neuronal polarization and migration.
We use nano-transducers (e.g. gold nanorods) to design photothermal neural interfaces for controlling neural spiking activity. Photothermal conversion was successfully implemented to induce reversible inhibition of spiking activity for more than tens of minutes. Using the novel inhibition effect, the regulation of spiking activity will be possible for hyperactive neural circuits in the future.
In order to grow neurons in vitro, we design the cell-surface interface using chemical functionalization approaches and surface micro patterning. In addition, we are actively searching for a simple and effective cell-adhesive or -repellent substrates. So far, we have pioneered in using a mussel-inspired polymer coating ('polydopamine') in MEA for covalently linking cell-adhesive biomolecules on metal and insulator.
We are interested in the relation between network structures and information processing in neuronal network. Microelectrode array and optical imaging are two major platforms to access multiple neurons simultaneously. Recently, we have developed a large-scale optophysiological data processing software, NeuroCa (link), that can efficiently handle a big data problem in neuroinformatics.