Biology subjects

Wong, E. C.

Publications and source records attributed to Wong, E. C..

5 recordsLinked to original sources

bioRxiv2023

A mathematical model for velocity-selective arterial spin labeling

PurposeTo present a theoretical framework that rigorously defines and analyzes key concepts and quantities for velocity selective arterial spin labeling (VSASL). Theory and MethodsAn expression for the VSASL arterial delivery function is derived based on (1) labeling and saturation profiles as a function of velocity and (2) physiologically plausible approximations of changes in acceleration and velocity across the vascular system. The dependence of labeling efficiency on the amplitude and effective bolus width of the arterial delivery function is defined. Factors that affect the effective bolus width are examined, and timing requirements to minimize quantitation errors are derived. ResultsThe model predicts that a flow-dependent negative bias in the effective bolus width can occur when velocity selective inversion (VSI) is used for the labeling module and velocity selective saturation (VSS) is used for the vascular crushing module. The bias can be minimized by choosing a nominal labeling cutoff velocity that is lower than the nominal cutoff velocity of the vascular crushing module. ConclusionThe elements of the model are specified in a general fashion such that future advances can be readily integrated. The model can facilitate further efforts to understand and characterize the performance of VSASL and provide critical theoretical insights that can be used to design future experiments and develop novel VSASL approaches.

bioengineering↗

bioRxiv2023

Metabolic energetics underlying attractors in neural models

Neural population modeling, including the role of neural attractors, is a promising tool for understanding many aspects of brain function. We propose a modeling framework to connect the abstract variables used in modeling to recent cellular level estimates of the bioenergetic costs of different aspects of neural activity, measured in ATP consumed per second per neuron. Based on recent work, an empirical reference for brain ATP use for the awake resting brain was estimated as [~]2x109 ATP/s-neuron across several mammalian species. The energetics framework was applied to the Wilson-Cowan (WC) model of two interacting populations of neurons, one excitatory (E) and one inhibitory (I). Attractors were considered exhibiting steady-state behavior and limit cycle behavior, both of which end when the excitatory stimulus ends, and sustained activity that persists after the stimulus ends. The general finding was that the energy cost essentially follows the firing rate (average spikes/s) of the E-population, and firing rates of 8-10 spikes/s are in good agreement with the empirical reference value of ATP use. Self-sustained firing driven by recurrent excitation, though, involves higher firing rates and a higher energy cost. By considering a network in which each node is a WC model, we found that a combination of three nodes can serve as a circuit element that turns on when input passes a threshold and then persists after the input ends (an on-switch), with moderate overall ATP use. The proposed framework can serve as a guide for anchoring neural population models to plausible bioenergetics requirements.

neuroscience↗

bioRxiv2022

Hippocampus Modulates Natural Sound Processing at Early Auditory Centers

Despite its prominence in learning and memory, hippocampal influence in early auditory processing centers remains unknown. Here, we examined how hippocampal activity modulates sound-evoked responses in the auditory midbrain and thalamus using optogenetics and functional MRI (fMRI) in rodents. Ventral hippocampus (vHP) excitatory neuron stimulation at 5 Hz evoked robust hippocampal activity that propagates to the primary auditory cortex. We then tested 5Hz vHP stimulation paired with either natural vocalizations or artificial/noise acoustic stimuli. vHP stimulation enhanced auditory responses to vocalizations (with a negative or positive valence) in the inferior colliculus, medial geniculate body, and auditory cortex, but not to their temporally reversed counterparts (artificial sounds) or broadband noise. Meanwhile, pharmacological vHP inactivation diminished response selectivity to vocalizations. These results directly reveal the large-scale hippocampal participation in natural sound processing at early centers of the ascending auditory pathway. They expand our present understanding of hippocampus in global auditory networks.

neuroscience↗

bioRxiv2020

Distributed Phase Oscillatory Excitation Efficiently Produces Attractors Using Spike Timing Dependent Plasticity

The brain is thought to represent information in the form of activity in distributed groups of neurons known as attractors, but it is not clear how attractors are formed or used in processing. We show here that in a randomly connected network of simulated spiking neurons, periodic stimulation of neurons with distributed phase offsets, along with standard spike timing dependent plasticity (STDP), efficiently creates distributed attractors. These attractors may have a consistent ordered firing pattern, or become disordered, depending on the conditions. We also show that when two such attractors are stimulated in sequence, the same STDP mechanism can create a directed association between them, forming the basis of an associative network. We find that for an STDP time constant of 20ms, the dependence of the efficiency of attractor creation on the driving frequency has a broad peak centered around 8Hz. Upon restimulation, the attractors selfoscillate, but with an oscillation frequency that is higher than the driving frequency, ranging from 10-100Hz.

neuroscience↗

bioRxiv2019

Example Based Hebbian Learning may be sufficient to support Human Intelligence

In this hypothesis paper we argue that when driven by example behavior, a simple Hebbian learning mechanism can form the core of a computational theory of learning that can support both low level learning and the development of human level intelligence. We show that when driven by example behavior Hebbian learning rules can support semantic, episodic and procedural memory. For humans, we hypothesize that the abilities to manipulate an off-line world model and to abstract using language allow for the generation and communication of rich example behavior, and thereby support human learning and a gradual increase of collective human intelligence across generations. We also compare the properties of Example Based Hebbian (EBH) learning with those of backpropagation-based learning and argue that the EBH mechanism is more consistent with observed characteristics of human learning.

neuroscience↗