10 research outputs found
Facilitatory neural dynamics for predictive extrapolation
Neural conduction delay is a serious issue for organisms that need to act in real
time. Perceptual phenomena such as the flash-lag effect (FLE, where the position of
a moving object is perceived to be ahead of a brief flash when they are actually colocalized)
suggest that the nervous system may perform extrapolation to compensate
for delay. However, the precise neural mechanism for extrapolation has not been fully
investigated.
The main hypothesis of this dissertation is that facilitating synapses, with their
dynamic sensitivity to the rate of change in the input, can serve as a neural basis for
extrapolation. To test this hypothesis, computational and biologically inspired models
are proposed in this dissertation. (1) The facilitatory activation model (FAM) was
derived and tested in the motion FLE domain, showing that FAM with smoothing
can account for human data. (2) FAM was given a neurophysiological ground by
incorporating a spike-based model of facilitating synapses. The spike-based FAM was
tested in the luminance FLE domain, successfully explaining extrapolation in both
increasing and decreasing luminance conditions. Also, inhibitory backward masking
was suggested as a potential cellular mechanism accounting for the smoothing effect.
(3) The spike-based FAM was extended by combining it with spike-timing-dependent
plasticity (STDP), which allows facilitation to go across multiple neurons. Through STDP, facilitation can selectively propagate to a specific direction, which enables the
multi-neuron FAM to express behavior consistent with orientation FLE. (4) FAM
was applied to a modified 2D pole-balancing problem to test whether the biologically
inspired delay compensation model can be utilized in engineering domains. Experimental
results suggest that facilitating activity greatly enhances real time control
performance under various forms of input delay as well as under increasing delay and
input blank-out conditions.
The main contribution of this dissertation is that it shows an intimate link between
the organism-level problem of delay compensation, perceptual phenomenon of
FLE, computational function of extrapolation, and neurophysiological mechanisms
of facilitating synapses (and STDP). The results are expected to shed new light on
real-time and predictive processing in the brain, and help understand specific neural
processes such as facilitating synapses
Acquisition and Mining of the Whole Mouse Brain Microstructure
Charting out the complete brain microstructure of a mammalian species is a
grand challenge. Recent advances in serial sectioning microscopy such as the Knife-
Edge Scanning Microscopy (KESM), a high-throughput and high-resolution physical
sectioning technique, have the potential to finally address this challenge. Nevertheless,
there still are several obstacles remaining to be overcome. First, many of
these serial sectioning microscopy methods are still experimental and are not fully
automated. Second, even when the full raw data have been obtained, morphological
reconstruction, visualization/editing, statistics gathering, connectivity inference, and
network analysis remain tough problems due to the unprecedented amounts of data.
I designed a general data acquisition and analysis framework to overcome these
challenges with a focus on data from the C57BL/6 mouse brain. Since there has been
no such complete microstructure data from any mammalian species, the sheer amount of data can overwhelm researchers. To address the problems, I constructed a general
software framework for automated data acquisition and computational analysis of the
KESM data, and conducted two scientific case studies to discuss how the mouse brain
microstructure from the KESM can be utilized.
I expect the data, tools, and studies resulting from this dissertation research to
greatly contribute to computational neuroanatomy and computational neuroscience
A Unified Cognitive Model of Visual Filling-In Based on an Emergic Network Architecture
The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well).
ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact to raise additional emergent behaviours via cognitive re-use, hence the Emergic prefix throughout. Nevertheless, the model is robust and parameter free. Differential re-use occurs in the nature of model interaction with a particular testing paradigm.
ECM has a novel decomposition due to the requirements of handling motion and of supporting unified modelling via finer functional grains. The breadth of phenomenal behaviour covered is largely to lend credence to our novel decomposition.
The Emergic Network architecture is a hybrid between classical connectionism and classical computationalism that facilitates the construction of unified cognitive models. It helps cutting up of functionalism into finer-grains distributed over space (by harnessing massive recurrence) and over time (by harnessing continuous change), yet simplifies by using standard computer code to focus on the interaction of information flows. Thus while the structure of the network looks neurocentric, the dynamics are best understood in flowcentric terms. Surprisingly, dynamic system analysis (as usually understood) is not involved. An Emergic Network is engineered much like straightforward software or hardware systems that deal with continuously varying inputs. Ultimately, this thesis addresses the problem of reduction and induction over complex systems, and the Emergic Network architecture is merely a tool to assist in this epistemic endeavour.
ECM is strictly a sensory model and apart from perception, yet it is informed by phenomenology. It addresses the attribution problem of how much of a phenomenon is best explained at a sensory level of analysis, rather than at a perceptual one. As the causal information flows are stable under eye movement, we hypothesize that they are the locus of consciousness, howsoever it is ultimately realized