Research Topic

Computing with cell types

About this Research Topic

The nervous system of virtually every animal is made up of a vast array of distinct cell types working in harmony to perform the computations necessary for the organism’s survival. The pioneering theoretical work by McCulloch and Pitts demonstrated how linking various cell types together can increase the computational repertoire and has served as a basis for understanding the evolutionary advantage of cellular heterogeneity in the nervous system.
Recently, a large body of neuroscientific research has focused on deep cellular phenotyping as a means of characterizing and annotating the cell diversity found in the brain to form coherent taxonomies. Specifically, the advent of new technologies, both at the molecular level (such as the development of transgenic mouse models and single-cell RNA sequencing) and at the morphological level (for instance, serial block-face electron microscopy and array tomography) has further enabled the discovery of novel cell types. Despite the abundance of modern data modalities about distinct cell types, a mechanistic understanding of their role within the computational logic of the brain is lagging behind.
The aim of this Research Topic is to serve as a platform to discuss leading-edge research on the computational advantages endowed by such a rich variety of cell types for neural systems. The range of works covered by this Research Topic is broad and encompasses any research or review paper that aims to elucidate how different cell types contribute in novel ways to neural computation. This approach necessarily spans several levels of biological complexity: from the single-cell level (including sub-cellular compartments such as synapses and dendrites), to microcircuits, and network simulations where the collective action of neural assemblies can generate local field potential dynamics and exhibit emergent properties. Importantly, the term “computation” is to be intended in its broadest possible sense: it can range from the computational capabilities of a single cell or its compartments to a task performed by a network composed of several thousand units, in which cell type heterogeneity impacts or enhances network function. In this sense, recent developments in deep learning approaches employing biologically-inspired network topology and cell diversity to execute a particular computation are an excellent fit for this Research Topic. Given that cell heterogeneity is an organizational principle shared by nervous systems of varying complexity, this Research Topic also welcomes studies investigating the computations performed in various parts of the nervous system, ranging from the sensory periphery to higher-order areas found in the brain of mammals and other animal species.


Keywords: Cell type, neuronal computation, biologically-inspired deep learning, spiking neural network, subcellular modelling, dendritic integration, recurrent neural network, attractor dynamics, excitation-inhibition balance.


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The nervous system of virtually every animal is made up of a vast array of distinct cell types working in harmony to perform the computations necessary for the organism’s survival. The pioneering theoretical work by McCulloch and Pitts demonstrated how linking various cell types together can increase the computational repertoire and has served as a basis for understanding the evolutionary advantage of cellular heterogeneity in the nervous system.
Recently, a large body of neuroscientific research has focused on deep cellular phenotyping as a means of characterizing and annotating the cell diversity found in the brain to form coherent taxonomies. Specifically, the advent of new technologies, both at the molecular level (such as the development of transgenic mouse models and single-cell RNA sequencing) and at the morphological level (for instance, serial block-face electron microscopy and array tomography) has further enabled the discovery of novel cell types. Despite the abundance of modern data modalities about distinct cell types, a mechanistic understanding of their role within the computational logic of the brain is lagging behind.
The aim of this Research Topic is to serve as a platform to discuss leading-edge research on the computational advantages endowed by such a rich variety of cell types for neural systems. The range of works covered by this Research Topic is broad and encompasses any research or review paper that aims to elucidate how different cell types contribute in novel ways to neural computation. This approach necessarily spans several levels of biological complexity: from the single-cell level (including sub-cellular compartments such as synapses and dendrites), to microcircuits, and network simulations where the collective action of neural assemblies can generate local field potential dynamics and exhibit emergent properties. Importantly, the term “computation” is to be intended in its broadest possible sense: it can range from the computational capabilities of a single cell or its compartments to a task performed by a network composed of several thousand units, in which cell type heterogeneity impacts or enhances network function. In this sense, recent developments in deep learning approaches employing biologically-inspired network topology and cell diversity to execute a particular computation are an excellent fit for this Research Topic. Given that cell heterogeneity is an organizational principle shared by nervous systems of varying complexity, this Research Topic also welcomes studies investigating the computations performed in various parts of the nervous system, ranging from the sensory periphery to higher-order areas found in the brain of mammals and other animal species.


Keywords: Cell type, neuronal computation, biologically-inspired deep learning, spiking neural network, subcellular modelling, dendritic integration, recurrent neural network, attractor dynamics, excitation-inhibition balance.


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

29 May 2021 Abstract
26 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

29 May 2021 Abstract
26 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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