Biology subjects

Young, L.-S.

Publications and source records attributed to Young, L.-S..

6 recordsLinked to original sources

bioRxiv2026

Mostly-monocular responses and other visual functions in a multiscale network model of Macaque V1

Visual signals from the two eyes merge gradually as they pass through the primary visual cortex (V1). Here we use a computational model of Macaque V1 to study the first stage of this integration along the magnocellular pathway, in layer 4C, aiming to infer neuroanatomical origins of binocular response. It is known that neurons in layer 4C are predominantly monocular, though some do exhibit varying degrees of binocularity. We find (1) the emergence of narrow binocular strips along borders of ocular dominance columns (ODC), a finding that aligns with experiments; (2) most consistent with data is when 10 - 30% of interactions near ODC boundaries are cross-columnar; and (3) feedback from layer 6 is largely monocular. These results were obtained through systematic hypothesis testing using a multiscale model that is orders of magnitude faster than its biologically-detailed predecessors. We propose that multiscale modeling can be an effective tool for bridging anatomy and function.

neuroscience↗

bioRxiv2021

A data-informed mean-field approach to mapping of cortical parameter landscapes

Constraining the many biological parameters that govern cortical dynamics is computa-tionally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a "biologically plausible" region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space. Author SummaryCortical circuits are characterized by a high degree of structural and dynamical complexity, and this biological reality is reflected in the large number of parameters in even semi-realistic cortical models. A fundamental task of computational neuroscience is to understand how these parameters govern network dynamics. While some neuronal parameters can be measured in vivo, many remain poorly constrained due to limitations of available experimental techniques. Computational models can address this problem by relating difficult-to-measure parameters to observable quantities, but to do so one must overcome two challenges: (1) the computational expense of mapping a high dimensional parameter space, and (2) extracting biological insights from such a map. This study aims to address these challenges in the following ways: First, we propose a parsimonious data-informed algorithm that efficiently predicts spontaneous cortical activity, thereby speeding up the mapping of parameter landscapes. Second, we show that lateral inhibition provides a basis for conceptualizing cortical parameter space, enabling us to begin to make sense of its geometric structure and attendant scaling relations. We illustrate our approach on a biologically realistic model of the monkey primary visual cortex.

neuroscience↗

bioRxiv2021

A computational model of direction selectivity in Macaque V1 cortex based on dynamic differences between ON and OFF pathways

This paper is about neural mechanisms of direction selectivity (DS) in Macaque primary visual cortex, V1. DS arises in V1 layer 4C which receives afferent input from the Magnocellular division of the Lateral Geniculate Nucleus (LGN). LGN itself, however, is not direction-selective. To understand the mechanisms of DS, we built a new computational model (DSV1) of 4C. DSV1 is a realistic, large-scale mechanistic model that simulates many V1 properties: orientation selectivity, spatial and temporal tuning, contrast response, and DS. In the model, DS is initiated by the dynamic difference of OFF and ON Magnocellular cell activity that excites the models layer 4C the recurrent network has no intra-cortical direction-specific connections. In experiments - and in DSV1 -- most 4C Simple cells were highly direction-selective but few 4C Complex cells had high DS. Furthermore, the preferred directions of the models direction-selective Simple cells were invariant with spatial and temporal frequency, in this way emulating the experimental data. The distribution of DS across the models population of cells was very close to that found in experiments. Analyzing DSV1, we found that the dynamic interaction of feedforward and intra-cortical synaptic currents led to cortical enhancement of DS for a majority of cells. In view of the strong quantitative agreement between DS in data and in model simulations, the neural mechanisms of DS in DSV1 may be indicative of those in the real visual cortex. Significance StatementMotion perception is a vital part of our visual experience of the world. In monkeys, whose vision resembles that of humans, the neural computation of the direction of a moving target starts in the primary visual cortex, V1, in layer 4C that receives input from the eye through the Lateral Geniculate Nucleus (LGN). How Direction-Selectivity (DS) is generated in layer 4C is an outstanding unsolved problem in theoretical neuroscience. In this paper, we offer a solution based on plausible biological mechanisms: We present a new large-scale circuit model in which DS originates from slightly different LGN ON/OFF response time-courses and is enhanced in cortex without the need for direction-specific intra-cortical connections. The models DS is in quantitative agreement with experiments.

neuroscience↗

bioRxiv2021

A theory of direction selectivity for Macaque primary visual cortex

This paper offers a new theory for the origin of direction selectivity in the Macaque primary visual cortex, V1. Direction selectivity (DS) is essential for the perception of motion and control of pursuit eye movements. In the Macaque visual pathway, DS neurons first appear in V1, in the Simple cell population of the Magnocellular input layer 4C. The LGN cells that project to these cortical neurons, however, are not direction-selective. We hypothesize that DS is initiated in feedforward LGN input, in the summed responses of LGN cells afferent to a cortical cell, and it is achieved through the interplay of (a) different visual response dynamics of ON and OFF LGN cells, and (b) the wiring of ON and OFF LGN neurons to cortex. We identify specific temporal differences in the ON/OFF pathways that together with (b) produce distinct response time-courses in separated subregions; analysis and simulations confirm the efficacy of the mechanisms proposed. To constrain the theory, we present data on Simple cells in layer 4C in response to drifting gratings. About half of the cells were found to have high DS, and the DS was broad-band in spatial and temporal frequency (SF and TF). The proposed theory includes a complete analysis of how stimulus features such as SF and TF interact with ON/OFF dynamics and LGN-to-cortex wiring to determine the preferred direction and magnitude of DS. Significance StatementMotion perception is important for primates, and direction selectivity (DS), the ability to perceive the direction a target is moving, is an essential part of motion perception. Yet no satisfactory mechanistic explanation has been proposed for the origin of DS in primate visual cortex up until now. In this paper, we hypothesize that DS is initiated in feedforward LGN input as a result of the dynamic differences between the ON/OFF pathways. The mechanisms we propose are biology-based, and our theory explains experimental data for all spatial and temporal frequencies in visual stimuli. Exploiting temporal biases in parallel pathways is relevant beyond visual neuroscience; similar ideas likely apply to other types of neural signal processing.g

neuroscience↗

bioRxiv2020

Origin of exponential growth in nonlinear reaction networks

Exponentially growing systems are prevalent in nature, spanning all scales from biochemical reaction networks in single cells to food webs of ecosystems. How exponential growth emerges in nonlinear systems is mathematically unclear. Here, we describe a general theoretical framework that reveals underlying principles of long-term growth: scalability of flux functions and ergodicity of the rescaled systems. Our theory shows that nonlinear fluxes can generate not only balanced growth, but also oscillatory or chaotic growth modalities, explaining non-equilibrium dynamics observed in cell cycles and ecosystems. Our mathematical framework is broadly useful in predicting long-term growth rates from natural and synthetic networks, analyzing the effects of system noise and perturbations, validating empirical and phenomenological laws on growth rate, and studying autocatalysis and network evolution. SignificanceNatural systems (e.g., cells, ecosystems) generally consist of reaction networks (e.g., metabolic networks, food webs) with nonlinear flux functions (e.g., Michaelis-Menten kinetics, density-dependent selection). Despite their complex nonlinearities, these systems often exhibit simple exponential growth in the long term. How exponential growth emerges from nonlinear networks remains elusive. Our work demonstrates mathematically how two principles, multivariate scalability of flux functions and ergodicity of the rescaled system, guarantee a well-defined rate of growth. By connecting ergodic theory, a powerful branch of mathematics, to the study of growth in biology, our theoretical framework can recapitulate various growth modalities (from balanced growth to periodic, quasi-periodic or even chaotic behaviors), greatly expanding the types of growing systems that can be studied.

systems biology↗

bioRxiv2019

Correlations in population dynamics in multi-component networks

This is a theoretical study of correlations in spiking activity between neuronal populations. We focus on the spike firing of entire local populations without regard to the identities of the neurons that fire the spikes, and show that such a population-level metric is more robust than correlations between pairs of neurons. Between any source and target populations, there is an intrinsic response time characterized by the phase-shift that maximizes the correlation between their spiking. We find that the alignment of gamma-band rhythms contributes significantly to the positive correlations between populations. Hence, the correlation metric sheds light on the transference of gamma rhythms between populations; the effectiveness of such transference has been hypothesized to be connected to communication between brain regions. We investigate the dependence of correlations on connectivity and degree of synchrony, and consider multi-component network motifs with configurations known to occur in real cortex, studying the correlations between components that are directly or indirectly connected, by single or multiple pathways, with or without feedback. Mechanistic explanations are offered for many of the phenomena observed.

neuroscience↗