神经元与认知
When listening to connected speech, human brain can extract multiple levels of linguistic units, such as syllables, words, and sentences. It has been hypothesized that the time scale of cortical activity encoding each linguistic unit is…
We present a generalized theoretical framework for olfactory representation and plasticity, using the theory of smooth manifolds and sheaves to depict categorical odor learning via distributed neural computation. Beginning with the space of…
Recurrent neural networks (RNNs) trained on compositional tasks can exhibit functional modularity, in which neurons can be clustered by activity similarity and participation in shared computational subtasks. Unlike brains, these RNNs do not…
The perception and appreciation of beauty in a musical composition appears as being related to the amount of memory and balance between expectation and surprise. In this study, we use a formal computing tool derived from quantum mechanics…
The brain's biological age has been considered as a promising candidate for a neurologically significant biomarker. However, recent results based on longitudinal magnetic resonance imaging data have raised questions on its interpretation. A…
This paper examines the relationship between sparse random network architectures and neural network stability by examining the eigenvalue spectral distribution. Specifically, we generalise classical eigenspectral results to sparse…
The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which…
Identifying abnormal electroencephalographic activity is crucial in diagnosis and treatment of epilepsy. Recent studies showed that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies)…
Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in fields ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms…
In this work, we investigate the phenomenon of transverse resonance and transverse standing waves that occur within the cochlea of living organisms. It is demonstrated that the predisposing factor for their occurrence is the cochlear shape,…
We consider a large network of Izhikevich neurons. Each neuron has a quadratic integrate-and-fire type model with a recovery variable modelling spike frequency adaptation (SFA). We introduce a biologically motivated synaptic current…
In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and…
Musical large-scale form is investigated using an Electronic Dance Music (EDM) piece fed into a Finite-Difference Time Domain (FDTD) physical model of the cochlear which again inputs into an Impulse-Pattern Formulation (IPF) brain model. In…
Present day artificial neural architecture search (NAS) strategies are essentially prediction-error-optimized. That holds true for AI functions in general. From the developmental neuroscience perspective, I present evidence for the central…
The quest to comprehend the origins of intelligence raises intriguing questions about the evolution of learning abilities in natural systems. Why do living organisms possess an inherent drive to acquire knowledge of the unknown? Is this…
A computational model incorporating insights from quantum theory is proposed to describe and explain synaptic message transmission. We propose that together, neurotransmitters and their corresponding receptors, function as a physical…
Brain-computer interfaces (BCI) are presented as a solution for people with global paralysis, also known as locked-in syndrome (LIS). The targeted population includes the most severe patients, with no residual eye movements, who cannot use…
In the face of the stupefying complexity of the human brain, network analysis is a most useful tool that allows one to greatly simplify the problem, typically by approximating the billions of neurons comprising the brain by means of a…
Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the performance of frequency…
Uniquely among primates, humans possess a remarkable capacity to recognize and manipulate abstract structure in the service of task goals across a broad range of behaviors. One illustration of this is in the visual perception of geometric…