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Laboratory

Computational Neuroscience Laboratory


Principle Investigator:
Yuguo Yu
Wenlian Lu

 

Research Directions:
Neural circuits organise and transform sensory signals from the external physical world into signals in the brain. The biophysical mechanisms, computational rules and algorithms associated with single neurons and neural circuits are big mysteries that are unsolved. Our research goals include, but are not limited to the following topics.


1. Energy efficient brain signalling and the brain' s energy budget.
Brain signalling is metabolically expensive. Energy expenditure not only constrains the size and architecture of the brain, which limits its computational power, but is critical to the interpretation of functional brain imaging signals through related metabolic mechanisms (e.g. oxygen consumption and blood flow). Comprising only about 2% of the body’s mass, the mammalian brain consumes about 20% of its energy. A unique feature of mammals is the warm body temperature they evolved (about 35-39 0C). How has the warm body temperature affected brain signalling and the brain energy budget? The answer is largely unknown and the main aim of this topic is to systematically study the answer to this question. We will adopt a combination of experimental and computational approaches which will shed light on the mechanisms of how mammalian cortical neurons and circuits maintain normal physiological functions at warm temperatures, and give insight into how hypothermia or hyperthermia may induce abnormal brain function disorders.


2. Neural information encoding/decoding and adaptation:
The link between sensory world and brain response can be studied from two opposite points of view. Neural encoding refers to the map from the signal to the response. The main focus is to understand how neurons respond to a wide variety of stimuli, and to accurately construct models that attempt to predict responses to other stimuli. Neural decoding refers to the reverse map, from response to stimulus, and the challenge is to reconstruct a stimulus, or certain aspects of that stimulus, from the spike sequences it evokes. Some issues include: (i) how the single neuron or neural circuits adapt their function to encode information from signals with different statistical features, (ii) what type of principle components or features of a stimulus are encoded in neural activity patterns, (iii) what aspects of the neural activity patterns encode this information (iv) what are the algorithms through which information is encoded and decoded from ensemble activity patterns. The main purpose of this topic is thus to study the coding scheme associated with cortical neurons encoding and decoding information.


3. Neuron modelling of single neurons and neural circuits
Each individual cortical neuron is composed of three main parts: cell body, dendrite tree and axon arbor. The complex morphology of dendrites and axon arbor of each type of cortical neurons may reflect specific computing roles in information processing. In addition, the intrinsic voltage-gated ionic channels distributed in different locations of dendrites/axons can produce a variety of complex spiking patterns in space and time, resulting in patterns with time delay, branch-point failures and reflected propagation. These patterns may reflect some forms of soma-axon computation that translate the synaptic inputs into more complex messages for communicating target neurons. We aim to build up more realistic models of single neurons, and neural circuits, in order to understand the functions of complex morphology and structure of the network in the information encoding and cognitive computing. In addition, we are also interested in the way damage in ionic channels or neural connections can result in brain disorders and mental disease.


4. Learning and memory formation.
Cortical circuits transform learnt information into long-term memories and hence store the information. How is this done? To understand this we are active in close collaborations with experimental scientists to study how the learning process affects the balance of excitatory and inhibitory synaptic transmission, and how the dynamics of ionic channels, which are intrinsic at the single neuron level, are affected by this process. We plan to investigate how the network dynamics changes before and after memory formation.

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