headerphoto

Past Research in Chung-Chuan Lo Lab

  1. Signal propagation in neural networks

    2. Neural networks are characterized by their ability of processing large amount of input in parallel. Typical analyses of the network architecture focus on the cluster coefficient (local communication) and shortest path length (long-range signal propagation). The approach treats every nodes (neurons) equally and do not consider any particular direction of functional information flow in the networks. However, the approach may not fully characterize the architecture of neural networks as they are distinct from other types of networks in at least two ways:
    1) A neural network has a specific direction of information flow: signals enter the network from a specific set of input neurons, processed by local neurons and leave the network from output neurons.
    2) The neural pathways via multiple synaptic connections may be functionally more important than the direct synaptic connections (shortest pathways).
    In this project, we propose a novel analysis which characterizes information flows in a neural network. Specifically, we measures two quantities: 1) processing speed: how quickly all vertical information pathways are established between input and output nodes and 2) information sharing: how far information entering a given input node travels horizontally to different output nodes. Comparing to the small-world netowrks and random networks, we found that neural networks are characterized by the fast processing speed and broad information sharing.

  2. SPIN: Skeleton-based Polarity Identification for Neurons

    3. SPIN (SPIN: Skeleton-based Polarity Identification for Neurons) is a method designed to identify polarity of substructures of neurons solely based on their skeletons (tracing lines). The method has been tested on Drosophila neurons based on data available from Flycircuit database (http://www.flycircuit.tw/). Our test showed that SPIN can reach ~85%-95% accuracy at the terminal levels under various testing conditions.
    Check out "SPIN" for more information.

  3. Hanitu system

    4. Hanitu is a simulation platform in which the users design neural circuits for virtual worms, allowing them to move, to navigate, to forage for foods and to avoid hazards in a competing virtual world. The world “Hanitu” (pronounced as ha`nI-tu) comes from the religion of the Bunun, a tribe of Taiwanese aborigines. Hanitu is the spirit of any living creature or natural object in this world.
    Check out "Hanitu" for more information.

  4. Adaptive behavior in perceptual decision

    5. How do we find the best strategy when making decisions and how do we change our strategy in response to a changing environment? To address the questions, we study the neuronal mechanisms of speed-accuracy tradeoff in perceptual decision. In one project, we study how a top-down input with balanced excitation and inhibition to a decision neural circuit can rapidly shift the system between the strategies that emphasize fast or accuracy.

    Energy Landscape Vb=-52.5
    Performance

    In another project, we build a doapmine-modulated synaptic plasticity system in a large scale neural networks and study what neuronal parameters are critical to the adaptation to environmental change during a decision process.

    Decision Optimization
    DA dependent STDP affects the Cxe-CD synaptic efficiency.

  5. Neuronal plasticity in transcranial magnetic stimulation (TMS)

    6. TMS use a coil to produce eddy current which is able to stimulate the central nervous pathways. With the advantage as a non-invasive method, TMS is widely used in various fields from probing brain functions to the therapies of neurological disorders. Using different patterns of stimulation, TMS can facilitate or depress neural signal transmisison. However, the underlying neuronal mechanism is still unclear.

    To address the issues, we designed a model of cortico-basal ganglia circuit to simulate the response of TMS in the brain. We also propose a integrated model of synaptic plasticity which can reproduce the plastic response to TMS of different stimulation patterns.
    Motor Cortex Neuron Network Model Simulation Result of Motor Cortex Network Respond to Single TMS Pulse

Join the lab

Ever wondering how complicated cognitive behavior is formed from interactions between neurons? Join our lab and we will help you to find out how. Please contact () for more details.