Life Size Medical Brain Model - Human Brain Model - Realistic Brain Anatomy Display, Science Classroom Demonstration Tools (A)

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Cutsuridis, V., Cobb, S. & Graham, B. P. Encoding and retrieval in a model of the hippocampal CA1 microcircuit. Hippocampus 20(3), 423–446. https://doi.org/10.1002/hipo.20661 (2010). The 21 st century has been the bursting era of large-scale brain initiatives. The objective of the simulation partly justifies this multitude. As it was previously mentioned, the notion simulation is highly versatile in meaning depending on the goal of the project ( de Garis et al., 2010), i.e., where it sits on the Figure 3. Some of the projects of this spectrum are listed below. Hill, S. & Tononi, G. Modeling sleep and wakefulness in the thalamocortical system. J. Neurophysiol. 93, 1671–1698. https://doi.org/10.1152/jn.00915.2004 (2005).

An insightful interplay of function vs. structure is observed along the biologically plausible line of work by Deco and Jirsa (2012). They reconstructed the emergence of equilibrium states around multistable attractors and characteristic critical behavior like scaling-law distribution of inter-brain pair correlations as a function of global coupling parameters. Furthermore, new studies show that synchrony not only depends on the topology of the graph but also on its hysteresis ( Qian et al., 2020).DCM is, in fact, a method for testing hypotheses and guiding experiments, not a predictive or generative model by itself. Models of the intra-connected regions can be built based on the earlier subsections, e.g., neural mass model, neural fields, or conductance-based models. For a review of such hybrid approaches, see Moran et al. (2013).

Breakspear, M. Dynamic models of large-scale brain activity. Nat. Neurosci. 20(3), 340–352. https://doi.org/10.1038/nn.4497 (2017). Wilson-Cowan is a large-scale model of the collective activity of a neural population based on mean-field approximation (see Section 1.3.1). Seemingly the most influential model in computational neuroscience after Hodgkin-Huxley ( Hodgkin and Huxley, 1952) is Wilson-Cowan ( Wilson and Cowan, 1972) with presently over 3,000 mentions in the literature. D’Angelo, E. et al. Modeling the cerebellar microcircuit: New strategies for a long-standing issue. Fr. Cell Neurosci. 10, 176. https://doi.org/10.3389/fncel.2016.00176 (2016).

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These neurons included Ivy and Bistratified cells whose cell bodies are mainly located in the SR and diffusely project an axonal cloud from SLM to SO isotropically 39. Similarly, dendrites are preferentially oriented in the direction going from the SO to the SLM and tend to be confined inside the axonal cloud. In our model, axons and dendrites where both designed as single ellipsoids (Fig. 5). Akram, M. A., Nanda, S., Maraver, P., Armananzas, R. & Ascoli, G. A. An open repository for single-cell reconstructions of the brain forest. Sci. Data 5, 180006. https://doi.org/10.1038/sdata.2018.6 (2018). Bonifazi, P. et al. GABAergic hub neurons orchestrate synchrony in developing hippocampal networks 2009. Science 326(5958), 1419–1424. https://doi.org/10.1126/science.1175509 (2009). According to the heterogeneity of shapes and orientations of inhibitory interneurons, we have identified 11 classes of cells which were grouped into 7 different shapes generated through combinations of axonal and dendritic probability:

Combining ordinary differential equations (ODEs) with deep neural networks has recently emerged as a feasible method of incorporating differentiable physics into machine learning. A Neural Ordinary Differential Equation (Neural ODE) ( Chen et al., 2018) uses a parametric model as the differential function in an ODE. This architecture can learn the dynamics of a process without explicitly stating the differential function, as has been done previously in different fields. Instead, standard deep learning optimization techniques could be used to train a parameterized differential function that can accurately describe the dynamics of a system. In the recent past, this has been used to infer the dynamics of various time-varying signals with practical applications ( Chen et al., 2018; Jia and Benson, 2019; Kanaa et al., 2019; Rubanova et al., 2019; Yildiz et al., 2019; Kidger et al., 2020; Li et al., 2020; Liu et al., 2020). 3.2.2.2. Latent ODE Arszovszki, A., Borhegyi, Z. & Klausberger, T. Three axonal projection routes of individual pyramidal cells in the ventral CA1 hippocampus. Fr. Neuroanat. 8, 53. https://doi.org/10.3389/fnana.2014.00053 (2014). An integrative example of the implementation discussed above is NeuCube. NueCube is a 3D SNN with plasticity that learns the connections among populations from various STBD modulations such as EEG, fMRI, genetic, DTI, MEG, and NIRS. Gene regulatory networks can be incorporated as well if available. Finally, This implementation reproduces trajectories of neural activity. It has more robustness to noise and higher accuracy in classifying STBD than standard machine learning methods such as SVM ( Kasabov, 2014). Ferguson, K. A. et al. Network models provide insights into how oriens-lacunosum-moleculare and bistratified cell interactions influence the power of local hippocampal CA1 theta oscillations. Fr. Syst. Neurosci. 9, 110. https://doi.org/10.3389/fnsys.2015.00110 (2015). In this section, we review brain models across different scales that are faithful to biological constraints. We focus primarily on the first column from the left in Figure 3, starting from the realistic models with mesoscopic details to more coarse-grained frameworks. 1.1. Modeling at the Synaptic LevelBocchio, M. et al. Hippocampal hub neurons maintain distinct connectivity throughout their lifetime. Nat. commun. 11, 4559. https://doi.org/10.1038/s41467-020-18432-6 (2020). Scaling compute power does not suffice for leveling up to the whole-brain models. Another challenge is the integration of time delays that become significant at the whole-brain level. In local connections, the time delays are small enough to be ignored ( Jirsa et al., 2010) the transmission happens in a variety of finite speeds from 1 to 10 m per second. As a result of this variation, time delays between different brain parts are no longer negligible. Additional spatial features emerge by the implementation of this heterogeneity ( Jirsa and Kelso, 2000; Petkoski and Jirsa, 2019). Our focus is on generative models. Generative modeling can, in the current context, be distinguished from discriminative or classification modeling; in the sense that there is a probabilistic model of how observable data is generated by unobservable latent states. Almost invariably, generative models in imaging neuroscience are state space or dynamic models based upon differential equations or density dynamics (in continuous or discrete state spaces). Generative models can be used in one of two ways: first, they can be used to simulate or generate plausible neuronal dynamics (at multiple scales), with an emphasis on reproducing emergent phenomena of the sort seen in real brains. Second, the generative model can be inverted, given some empirical data, to make inferences about the functional form and architecture of distributed neuronal processing. In this use, the generative model is used as an observation model and is optimized to best explain some data. Crucially, this optimization entails identifying both the parameters of the generative model and its structure, via the process of model inversion and selection, respectively. When applied in this context, generative modeling is usually deployed to test hypotheses about functional brain architecture is (or neuronal circuits) using (Bayesian) model selection. In other words, comparing the evidence (a.k.a. marginal likelihood) for one model against some others. A reservoir computer (RC) ( Maass et al., 2002) is an RNN with a reservoir of interconnected spiking neurons. Broadly speaking, the distinction of RC among RNNs, in general, is the absence of granular layers between input and output. RCs themselves are dynamical systems that help learn the dynamics of data. Traditionally, the units of a reservoir have nonlinear activation functions that allow them to be universal approximators. Gauthier et al. (2021) show that this nonlinearity can be consolidated in an equivalent nonlinear vector autoregressor. With the nonlinear activation function out of the picture, the required computation, data, and metaparameter optimization complexity are significantly reduced, the interpretability is consequently improved while the performance stays the same. 3.1.2.4. Liquid State Machine Schneider, C. J., Cuntz, H. & Soltesz, I. Linking macroscopic with microscopic neuroanatomy using synthetic neuronal populations. PLoS Comput. Biol. 10(10), e1003921. https://doi.org/10.1371/journal.pcbi.1003921 (2014).