Quantitative Biology

We propose and demonstrate the use of a minimal 2 θ model for endogenous bursters coupled in 3-cell neural circuits. This 2 θ model offers the benefit of simplicity of designing larger neural networks along with an acute reduction on the computation cost.

Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. However, they differ in several important respects, including holistic versus complementary roles of the cerebellum, pattern recognition versus control as computational objectives, potentiation versus depression of synaptic plasticity, teaching signals versus error signals transmitted by climbing-fibers, sparse expansion coding by granule cells, and cerebellar internal models. In this review, we evaluate the different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum. That is, hierarchical reinforcement learning with multiple internal models.

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden state or an abstract rule has to be learned dynamically. Although performance in such tasks is regularly considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the established Wisconsin Card Sorting Test. We embed the new model within the mathematical framework of Bayesian Brain Theory, according to which beliefs about the hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We further apply the model to real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. Practical and theoretical implications of our computational modeling approach for clinical and cognitive neuroscience research are finally discussed, as well as potential future improvements.

Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. The ability of emotion identification begins in infancy and continues to develop throughout childhood and adolescence. Understanding the development of brain's emotion circuitry may help us explain the emotional changes observed during adolescence. Our previous study delineated the trajectory of brain functional connectivity (FC) from late childhood to early adulthood during emotion identification tasks. In this work, we endeavour to deepen our understanding from association to causation. We proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM), which incorporated our previous association model into the prior estimation pipeline. In particular, it can jointly estimate multiple directed acyclic graphs (DAGs) for multiple age groups at different developmental stages. Simulation results indicated more stable and accurate performance over various settings, especially when the sample size was small (high-dimensional cases). We then applied to the analysis of real data from the Philadelphia Neurodevelopmental Cohort (PNC). This included 855 individuals aged 8-22 years who were divided into five different adolescent stages. Our network analysis revealed the development of emotion-related intra- and inter- modular connectivity and pinpointed several emotion-related hubs. We further categorized the hubs into two types: in-hubs and out-hubs, as the center of receiving and distributing information. Several unique developmental hub structures and group-specific patterns were also discovered. Our findings help provide a causal understanding of emotion development in the human brain.

Scientific studies of consciousness rely on objects whose existence is assumed to be independent of any consciousness. On the contrary, we assume consciousness to be fundamental, and that one of the main features of consciousness is characterized as being other-dependent. We set up a framework which naturally subsumes this feature by defining a compact closed category where morphisms represent conscious processes. These morphisms are a composition of a set of generators, each being specified by their relations with other generators, and therefore co-dependent. The framework is general enough and fits well into a compositional model of consciousness. Interestingly, we also show how our proposal may become a step towards avoiding the hard problem of consciousness, and thereby address the combination problem of conscious experiences.

Parkinson's disease (PD) is caused by the progressive loss of dopaminergic cells in substantia nigra pars compacta (SNc). The root cause of this cell loss in PD is still not decisively elucidated. A recent line of thinking traces the cause of PD neurodegeneration to metabolic deficiency. Due to exceptionally high energy demand, SNc neurons exhibit a higher basal metabolic rate and higher oxygen consumption rate, which results in oxidative stress. Recently, we have suggested that the excitotoxic loss of SNc cells might be due to energy deficiency occurring at different levels of neural hierarchy. Levodopa (LDOPA), a precursor of dopamine, which is used as a symptom-relieving treatment for PD, leads to outcomes that are both positive and negative. Several researchers suggested that LDOPA might be harmful to SNc cells due to oxidative stress. The role of LDOPA in the course of PD pathogenesis is still debatable. We hypothesize that energy deficiency can lead to LDOPA-induced toxicity (LIT) in two ways: by promoting dopamine-induced oxidative stress and by exacerbating excitotoxicity in SNc. We present a multiscale computational model of SNc-striatum system, which will help us in understanding the mechanism behind neurodegeneration postulated above and provides insights for developing disease-modifying therapeutics. It was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During LDOPA therapy, it was observed that higher LDOPA dosage results in increased loss of somas and terminals in SNc. It was also observed that co-administration of LDOPA and glutathione (antioxidant) evades LDOPA-induced toxicity in SNc neurons. We show that our proposed model was able to capture LDOPA-induced toxicity in SNc, caused by energy deficiency.

The receptive fields of simple cells in the visual cortex can be understood as linear filters. These filters can be modelled by Gabor functions, or by Gaussian derivatives. Gabor functions can also be combined in an `energy model' of the complex cell response. This paper proposes an alternative model of the complex cell, based on Gaussian derivatives. It is most important to account for the insensitivity of the complex response to small shifts of the image. The new model uses a linear combination of the first few derivative filters, at a single position, to approximate the first derivative filter, at a series of adjacent positions. The maximum response, over all positions, gives a signal that is insensitive to small shifts of the image. This model, unlike previous approaches, is based on the scale space theory of visual processing. In particular, the complex cell is built from filters that respond to the \twod\ differential structure of the image. The computational aspects of the new model are studied in one and two dimensions, using the steerability of the Gaussian derivatives. The response of the model to basic images, such as edges and gratings, is derived formally. The response to natural images is also evaluated, using statistical measures of shift insensitivity. The relevance of the new model to the cortical image representation is discussed.

This thesis is designed to be a self-contained exposition of the neurobiological and mathematical aspects of sensory perception, memory, and learning with a bias towards olfaction. The final chapters introduce a new approach to modeling focusing more on the geometry of the system as opposed to element wise dynamics. Additionally, we construct an organism independent model for olfactory processing: something which is currently missing from the literature.

Addressing the question of visualising human mind could help us to find regions that are associated with observed cognition and responsible for expressing the elusive mental image, leading to a better understanding of cognitive function. The traditional approach treats brain decoding as a classification problem, reading the mind through statistical analysis of brain activity. However, human thought is rich and varied, that it is often influenced by more of a combination of object features than a specific type of category. For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment. To find the correspondence from brain signal features to image features, we embedded them into two latent spaces with modality-specific encoders and then aligned the two spaces by minimising the distance between paired latent representations. The proposed framework was trained by simultaneous electroencephalogram and functional MRI data, which were recorded when the subjects were viewing or imagining a set of image stimuli. In this paper, we focused on implementing the fMRI experiment. Our experimental results demonstrated the feasibility of translating brain activity to an image. The reconstructed image matches image stimuli approximate in both shape and colour. Our framework provides a promising direction for building a direct visualisation to reveal human mind.

Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivity patterns at different regions and can be used as input to traditional classifiers for various downstream graph analytic tasks, such as AD early stage prediction, and statistical evaluation of between-group significant alterations across brain regions. We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.