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 ismetabolically 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.