An analogy can be made with the general methodologies
developed for numerically solving differential equations, for example,
the finite difference, finite element, finite volume, and spectral methods. These different but also closely related methodologies serve
as guidelines for designing numerical methods for specific
applications. In the multiscale approach, one uses a variety of models at different
levels of resolution and complexity to study one system. For example, one may study the mechanical behavior of
solids using both the atomistic and continuum models at the same time,
with the constitutive relations needed in the continuum model computed
from the atomistic model.
The pre-processing component includes two methods to extract patches from several magnification levels. The scale detector component includes a CNN allowing to regress the magnification level of a patch. The CNN obtains high performance in patches that come from the colon (the tissue used to train it) and it reaches good performance on other tissues such as prostate and lung too. Two multi-scale CNN architectures are developed for fully-supervised classification. The first one combines features from multi-scale branches, while the second one combines predictions from multi-scale branches. The first architecture obtains better performance and outperforms the model trained with patches from only one magnification level.
One major challenge is the need for multi-scale modeling and analytical methods to provide fundamental infrastructure for understanding neurological processes, disease, cognition, and behavior. New, versatile frameworks to elucidate multi-scale activity at a mechanistic level will enable this challenge to be addressed. Another major challenge is the need for harmonious technologies to enable cross-scale interrogation of neural activity. Although significant insights can be made with data that is not acquired synchronously, neurotechnologies that allow activity recorded using different modalities simultaneously will serve to broaden our toolkit for making consequential advancements. Traditional CFD methods solve the Navier-Stokes equations in either full or reduced form, whereas in contrast kinetic methods generally solve some form of the Liouville equation.
Based on the above review, we asked how well the spatial consistency of different methods used in ENs establishment are intended to support conservation, and how well their cross-scale applicability is? Other challenges include (a) the need for MR-compatible electrodes which do not cause significant artifacts in MR images and (b) the appropriateness for both modalities in a given subject. To the first point, there is active work to develop extracellular recording methods to acquire LFP, spikes, or both, that minimize susceptibility artifacts during fMRI.
Sequential multiscale modeling
The basic object of interest is a
dynamical system for the effective model in which the time parameter
is replaced by scale. Therefore this dynamical system describes how
the effective model changes as the scale changes. Classically this is a way of
solving the system of algebraic equations that arise from discretizing
differential equations by simultaneously using different levels https://wizardsdev.com/en/news/multiscale-analysis/ of
grids. In this way, one can more efficiently eliminate the errors on
different scales using different grids. In particular, it is
typically much more efficient to eliminate large scale (or smooth)
component of the errors using coarse grids. The first type are
problems where some interesting events, such as chemical reactions,
singularities or defects, are happening locally.
This is done by introducing fast-scale and slow-scale variables for an independent variable, and subsequently treating these variables, fast and slow, as if they are independent. In the solution process of the perturbation problem thereafter, the resulting additional freedom – introduced by the new independent variables – is used to remove (unwanted) secular terms. The latter puts constraints on the approximate solution, which are called solvability conditions. The prostate dataset is a subset of the publicly available database offered by The Cancer Genome Atlas (TCGA-PRAD), that includes 20 WSIs, stained with H&E. The images come from several sources and are digitized with different scanners, with a spatial resolution of 0.25 μm per pixel (40x).
Scales of neural interrogation and decoding
Data-driven machine learning algorithms like recurrent neural networks  and unsupervised hierarchical clustering of neural dynamics  have proven valuable for decoding high-dimensional data. Another strategy is to leverage low-dimensional projections to capture spatiotemporal dynamics. Low-dimensional embedding has enabled development of state-space based decoding algorithms for ECoG [112, 113] and may be particularly useful for decoding ECoG population dynamics on a single-trial basis . The pre-processing component allows to extract a large amount of patches from multiple magnification levels, guaranteeing scalable performance.
The Boltzmann equation is derived from the Liouville equation and is concerned with the temporal evolution of a single-particle distribution function fx→ξ→t, where x→ denotes the particle location, ξ→ the particle velocity, and t time. The fact that the Boltzmann equation is an integral-differential equation means it is extremely complicated to solve either analytically or numerically. The Boltzmann equation can be simplified through the implementation of various model equations that represent its binary collision integral. One of the most popular collision models is that of Bhatnagar-Gross-Krook (BGK) , which is a single-time relaxation model sufficient to capture a wide range of hydrodynamic flows.
3 Multi-Scale CNN for Classification Assessment
However, fMRI and PET are both sensitive to movement, which introduces additional challenges with these functional imaging methods in awake animals. For this reason, many fMRI-spike and PET-spike studies are performed on anesthetized animals (e.g. [225, 227–230]), although some studies utilize specialized MR-compatible restraint systems to limit movement during imaging (e.g. [223, 231]). A standard analysis used to characterize interactions between spikes and LFPs is the spike-triggered average of the LFP (figure 2).
- The scale detector makes reasonably good scale estimations also on the prostate data, in both the regression and the classification metrics, and in lung dataset, where the performance is the lowest though.
- Scholars have developed diverse methods/models to improve identification accuracy (Cook, 2002, Peng et al., 2017, Choe et al., 2021), providing beneficial preparations for incorporating ENs into landscape planning.
- Similarly, a spike-triggered spectrum of LFP activity can be used to determine how power in different frequency bands relates to local spiking activity.
- The decoding accuracy using the LFP and MUA outperformed models that relied solely on either LFP or MUA.
- The growth of multiscale modeling in the industrial sector was primarily due to financial motivations.
- However, in common with other morphological techniques, their extension to color and other multichannel images is not straightforward because of the absence of an unambiguous ordering.
For this reason, the CNNs show high performance with patches from magnification 5/10x, while including patches from 20x decreases the performance. The fact that the discriminant characteristics are identified in a range of scales may explain why the combination of the features shows higher performance than the combination of the predictions. In this paper, we describe typical methodologies used for characterizing neural activity at different temporal and spatial scales. For each method, we describe the state-of-the-art analysis that can be applied to this data type.
Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment. We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes. We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies.
For example, humans with higher capacity to synthesize dopamine in the dorsal striatum are more willing to exert cognitive effort than humans with lower synthesis capacity . Moreover, patients with schizophrenia who respond to treatment show a negative correlation between prefrontal cortex grey matter volume and the striatally measured capacity to synthesize dopamine . More generally, PET may contribute to tailoring therapeutic interventions to subtypes of patients and improving clinical outcomes. Relatively recently developed multivariate methods provide insights not only on patterns of local activation but also on patterns of connectivity between regions and networks . One variant of this approach uses condition-specific connectivity estimates from psychophysiological interaction models to train cross-validated SVMs.