Guide Computational neuroscience: realistic modeling for experimentalists

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  3. Erik De Schutter
  4. Computational neuroscience : realistic modeling for experimentalists - Semantic Scholar

The book describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. It is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but it will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book.

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The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists. The chapters offer comprehensive coverage with little overlap and extensive cross-references, moving from basic building blocks to more complex applications. Huguenard, William R.

Neuroscientists with a computational background will benefit most from this book, and will find it a comprehensive source of information on how to build and critically assess neuron models at many levels of description. Terrence J. Wolf Singer , Terrence J. Sejnowski , and Pasko Rakic. Paul Miller. Michael A.

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  • Computational Modeling Methods for Neuroscientists.
  • Computational Modeling Methods for Neuroscientists.

Arbib and James J. Computational models have become an important tool in the study of the nervous system and are commonly used in the simulation of specific aspects of physiology and pathology at various levels in addition to teaching neuroscience; however, their correspondence to complex biological reality of nervous system remains under question. The large number of variables and factors affecting the structure and function of the nervous system makes it almost impossible for them to be considered in a single model.

Therefore, computational models, mostly consider few specific biological variables leading to their limited applicability. This paper provides some of the fundamental characteristics of the nervous system, including different cell types and their ratio, neurotransmitters and their receptors, synaptic plasticity and gene expressions to illustrate the gap between modeling studies and biological reality.

Nevertheless, computational models have their own advantages that make them almost an irreplaceable tool in modern neuroscience. Specially, the integrative power of these models in unifying biological observations ranging from sub-cellular to the whole organism, have been attracting a lot of interest. Within this context, we critically review the biological correspondence of computational models and suggest multi-level modeling as an effective approach for enhancing the applicability of computational modeling in neuroscience.

Computer modeling of neuronal, glial, synaptic and network function has become increasingly popular during the last few decades and has greatly contributed to the understanding of various aspects of the nervous system in health and disease [].

Models of neurons and glia exist that not only simulate selected output of neuronal function, but also predict cell function in different states. They also contribute in teaching various fields of neuroscience such as computational neuroscience, cognitive neuroscience, neural and cortical microcircuits, circuits and networks []. However, given the complexity of the nervous system, these models, especially the neuronal models have inherent deficiencies and are often criticized for their oversimplification and limited applicability []. This paper provides a critical overview and assessment of some of the inherent limitations of modeling studies of the nervous system from neurobiology perspective.

After considering these limitations, a multi-level approach is suggested for bringing these models near to what exists in reality at cellular, receptor, genetic, molecular and network level. Additionally, for optimizing the effectiveness and application of these computational models, integration of experimental observations at different levels from cell to whole organism is suggested.

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  4. As we will discuss, this across-scale models are not possible unless scientists from different fields and relevant disciplines including neurophysiologists and computational neuroscientists work in close collaboration. This will fill the gaps created by the lack of knowledge, diversity and can accurately connect the various levels of the hierarchy from single neuron to whole brain which is a common problem in models that are currently available. For this purpose, the remainder of the paper is divided into two main parts.

    First, some basic features of the nervous system such as cell types and their physiological aspects, synaptic plasticity and genetic considerations are discussed. Specifically, the gap between biological reality and modeling studies is explained; however, since this is not an exhaustive review, for each feature some examples of modeling studies pointing to their deficiency are provided for illustrative purposes. In the second part, from a modeling perspective, two main levels of modeling and their advantages and disadvantages are discussed and it is also explained how the multi-level approach may bring the advantages of both abstraction levels together in a hierarchical framework.

    Additionally, general requirements of any modeling study are suggested. The paper ends providing a general perspective based on the material presented in these two sections. There are several studies that have modeled neuronal function from various aspects and aimed to better predict the neuronal behavior under diverse conditions that would mimic a real neuron.

    However, while most of the investigators assert that their model is technically better than the others, these models have their own limitations as they consider some specific aspects of neuronal function [11]. To illustrate further, every modeling study usually focuses on one, two or few main variables and considers the effect of their variations on neuronal function as a change in the behavior of whole neuron or network of neurons and interprets relevant data as such.

    As we will discuss later, the possible number of variables that determine neuronal behavior under physiological and pathological conditions is too large and no single model can employ all these variables and their interactions. Such a model would require a huge computational effort as well as diversity of multi-disciplinary expertise to be constructed and analyzed.

    Therefore, from neuroscience view, the interpretation of the results of any such modeling study, especially when making general assessment, requires caution and has limited application.

    In this section, some of the main physiological characteristics of the nervous system that are usually not addressed adequately in modeling studies, are investigated in order to point out the gap between complex biological reality and computational models and therefore the limited applicability of these models Figure 1. To address this important problem, three specific considerations regarding the nervous system, namely, different cell types and their ratio, synapses and neurotransmitters and genes will be presented and few related modeling studies will be discussed.

    Figure 1: A schematic diagram of some of the important aspects of the nervous system that have been considered in modeling, however not addressed properly including different cell types neuronal and glial and their numbers in different areas of the brain, diversity of neurotransmitters and their transporters and receptors and various gene expressions in neurons.

    Cells: Numbers, Types, and Ratios.

    Erik De Schutter

    The nervous system is a huge complex network comprising of different cells, with different shapes and sizes that play diverse roles in its function. The human brain is unique in its several characteristics and comprises of over 86 billion neurons with approximately the same number of non-neuronal cells, called glia [].

    These neurons are connected to each other through to trillion synapses [17].

    Computational Models of Cognition: Part 1

    Modeling studies have an obvious limitation as data from individual cells and also different cell types of human brain is not available due to experimental limitations even if it was available, we would not know how to process and interpret this huge amount of data. On the other hand, in large network models of different brain areas, these studies drastically downscale the size of the network due to computational limitations which may affect the reliability of the results of such models.

    For example, the downscaling has been mentioned to be of great importance in interpreting the results of computational models of neurogenesis and its role in learning and memory as the size of the network number of computational units has a major effect on network capacity in learning and memory [19].

    Computational neuroscience : realistic modeling for experimentalists - Semantic Scholar

    Additionally, models of the nervous system usually consider a single type of cell or interactions of two or three relevant types of cells. However, as mentioned earlier, in reality, the nervous system comprised of extensive and complex networks of various types of neurons, interneurons and glial cells. Especially, in this regard, considering the active role of astroglia in various neuronal functions and synaptic plasticity, neuronal models without considering neuronglia interactions pose major limitations in understanding the neurobiological facts at the cellular level [1,20,21].

    Interestingly, employing astrocytes, even in Artificial Neural Network, or Artificial Neuron- Glia Networks, improves the performance of the network [21]. There are interspecies variations of neuronal and glial number, function and structure [22]. For instance, the ratio of cells, such as neuron verses glia, differs in a high proportion across species [23].

    In Caenorhabditis elegans there are neurons and only 56 glial and associated support cells [24], while in leech, a typical ganglion is composed of 25 to 30 neurons and only one astrocyte [23]. In addition, astrocytes and neurons are represented in a ratio of in the cortex of lower mammals such as rats and mice while in the human cerebral cortex, there are 1. Modeling studies usually fail to address these differences which are of great importance due to lack of a systematic approach for correlating data from different species [13].


    Additionally, the ratio of different cell types and their connections are different in, and specific to various regions of the brain. In the human brain, the glia to neuron ratio in subcortical nuclei can be as high as in the thalamus [26] compared to the relatively small combined number of glial cells reported for the cerebral [27] and cerebellar cortices [28]. These differences in the ratio of neuron and glia in different regions of the brain have also been reported in animals such as mice [29] and rats [30].

    However, modeling studies sometimes do not specify the part or specific region of the brain to which the model with the fixed ratios applies. For instance, some studies proposed a model for neuronastrocyte network [31,32], however, failed to explain which part of human brain they were modeled, although the ratio of Neuron verses Glia is different in various regions of brain [33].

    Neural networks are complex structures including a large number of synapses. For example, a CA1 pyramidal cell in rat hippocampus receives around 30, excitatory and 1, inhibitory inputs [34] with different ratios of excitatory versus inhibitory synapses in different areas of soma, axon and dendrites [35]. These structural differences are thought to affect the functional aspects of pyramidal neurons, especially the synaptic integration [35]. Moreover, the number of these connections is not invariant and changes due to neural branching and synaptogenesis [].

    Also, variable is the strength of these synapses because of variations in the level of plasticity at individual synapses.