25th Annual Computational Neuroscience Meeting: CNS-2016 (2025)

Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types

For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literature- based database of electrophysiological properties (www.neuroelectro.org), to better understand neuronal diversity, both within and across neuron types, and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a sub- stantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across laboratories than previously appreciated. Using this normalized data- set, we find that neuron types throughout the brain cluster by bio- physical properties into six to nine superclasses. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at theta- band frequencies.

Neuronal subtype specification in the cerebral cortex

Nature Reviews Neuroscience, 2007

The mammalian neocortex is a complex, highly organized, six-layered structure that contains hundreds of different neuronal cell types and a diverse range of glia 1,2. It is the region of the brain responsible for cognitive function, sensory perception and consciousness, and as such it has undergone pronounced expansion and development during evolution 3. There are two broad classes of cortical neurons: interneurons, which make local connections; and projection neurons, which extend axons to distant intracortical, subcortical and subcerebral targets. Projection neurons are glutamatergic neurons characterized by a typical pyramidal morphology that transmit information between different regions of the neocortex and to other regions of the brain. During development, they are generated from progenitors of the neocortical germinal zone located in the dorsolateral wall of the telencephalon 4-8. By contrast, GABA (γ-aminobutyric acid)-containing interneurons and Cajal-Retzius cells are generated primarily from progenitors in the ventral telencephalon and cortical hem, respectively, and migrate long distances to their final locations within the neocortex 9 (BOX 1). In this manner, multiple progenitor zones contribute to the rich variety of neuronal types found in the neocortex. Within the mature neocortex, distinct populations of projection neurons are located in different cortical layers and areas, have unique morphological features, express different complements of transcription factors, and ultimately serve different functions. The complexity and diversity of projection neuron subtypes makes any classification scheme difficult, but the most accurate system probably extends beyond hodology (anatomical projections)

Artificial Neural Networks in Action for an Automated Cell-Type Classification of Biological Neural Networks

IEEE Transactions on Emerging Topics in Computational Intelligence

Identification of different neuronal cell types is critical for understanding their contribution to brain functions. Yet, automated and reliable classification of neurons remains a challenge, primarily because of their biological complexity. Typical approaches include laborious and expensive immunohistochemical analysis while feature extraction algorithms based on cellular characteristics have recently been proposed. The former rely on molecular markers, which are often expressed in many cell types, while the latter suffer from similar issues: finding features that are distinctive for each class has proven to be equally challenging. Moreover, both approaches are time consuming and demand a lot of human intervention. In this work we establish the first, automated cell-type classification method that relies on neuronal activity rather than molecular or cellular features. We test our method on a real-world dataset comprising of raw calcium activity signals for four neuronal types. We compare the performance of three different deep learning models and demonstrate that our method can achieve automated classification of neuronal cell types with unprecedented accuracy.

The neuron classification problem

Brain research reviews, 2007

A systematic account of neuron cell types is a basic prerequisite for determining the vertebrate nervous system global wiring diagram. With comprehensive lineage and phylogenetic information unavailable, a general ontology based on structure-function taxonomy is proposed and implemented in a knowledge management system, and a prototype analysis of select regions (including retina, cerebellum, and hypothalamus) presented. The supporting Brain Architecture Knowledge Management System (BAMS) Neuron ontology is online and its user interface allows queries about terms and their definitions, classification criteria based on the original literature and "Petilla Convention" guidelines, hierarchies, and relations-with annotations documenting each ontology entry. Combined with three BAMS modules for neural regions, connections between regions and neuron types, and molecules, the Neuron ontology provides a general framework for physical descriptions and computational modeling of neural systems. The knowledge management system interacts with other web resources, is accessible in both XML and RDF/OWL, is extendible to the whole body, and awaits large-scale data population requiring community participation for timely implementation.

Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks

Scientific Reports, 2021

The study of cellular complexity in the nervous system based on anatomy has shown more practical and objective advantages in morphology than other perspectives on molecular, physiological, and evolutionary aspects. However, morphology-based neuron type classification in the whole rat brain is challenging, given the significant number of neuron types, limited reconstructed neuron samples, and diverse data formats. Here, we report that different types of deep neural network modules may well process different kinds of features and that the integration of these submodules will show power on the representation and classification of neuron types. For SWC-format data, which are compressed but unstructured, we construct a tree-based recurrent neural network (Tree-RNN) module. For 2D or 3D slice-format data, which are structured but with large volumes of pixels, we construct a convolutional neural network (CNN) module. We also generate a virtually simulated dataset with two classes, reconstr...

Categorization in Neuroscience

Handbook of Categorization in Cognitive Science, 2005

Comparison between supervised and unsupervised classifications of neuronal cell types: A case study

Developmental Neurobiology, 2011

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a "benchmark," the test to automatically distinguish between pyramidal cells and interneurons, defining "ground truth" by the presence or absence of an api-Additional Supporting Information may be found in the online version of this article.