Biology subjects

Komendantov, A. O.

Publications and source records attributed to Komendantov, A. O..

3 recordsLinked to original sources

bioRxiv2023

Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

neuroscience↗

bioRxiv2019

Simple models of quantitative firing phenotypes in hippocampal neurons: comprehensive coverage of intrinsic diversity

Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with/without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively different responses, such as spiking and bursting under different input conditions, thus forming a complex phenotype of responses. In a previous work, Hippocampome.org, a comprehensive knowledgebase of hippocampal neuron types, systematically characterized various spike pattern phenotypes experimentally identified from 120 neuron types/subtypes. In this paper, we present a comprehensive set of simple phenomenological models that quantitatively reproduce the diverse and complex phenotypes of hippocampal neurons. In addition to point-neuron models, we created compact multi-compartment models with up to four compartments, which will allow spatial segregation of synaptic integration in network simulations. Electrotonic compartmentalization observed in our compact multi-compartment models is qualitatively consistent with experimental observations. Furthermore, we observed that adding dendritic compartments to point-neuron models, in general, allowed soma to reproduce features of bursting patterns and abrupt non-linearities in some frequency adapting patterns slightly more accurately. This work maps 120 neuron types/subtypes in the rodent hippocampus to a low-dimensional model space and adds another dimension to the knowledge accumulated in Hippocampome.org. Computationally efficient representations of intrinsic dynamics, along with other pieces of knowledge available in Hippocampome.org, provide a biologically realistic platform to explore the dynamical interactions of various types at the mesoscopic level.\n\nAuthor SummaryThe neurons in the hippocampus show enormous diversity in their intrinsic activity patterns. A comprehensive characterization of various intrinsic types using a neuronal modeling system is necessary to simulate biologically realistic networks of brain regions. Morphologically detailed neuronal modeling frameworks often limit the scalability of such network simulations due to the specification of hundreds of rules governing each neurons intrinsic dynamics. In this work, we have accomplished a comprehensive mapping of experimentally identified intrinsic dynamics in a simple modeling system with only two governing rules. We have created over a hundred point-neuron models that reflect the intrinsic differences among the hippocampal neuron types both qualitatively and quantitatively. In addition, we compactly extended our point-neurons to include up to four compartments, which will allow anatomically finer-grained connections among the neurons in a network. Our compact model representations, which are freely available in Hippocampome.org, will allow future researchers to investigate dynamical interactions among various intrinsic types and emergent integrative properties using scalable, yet biologically realistic network simulations.

neuroscience↗

bioRxiv2017

Firing pattern classification and phenotyping in a knowledge base of hippocampal neuron types

Systematically organizing the anatomical, molecular, and physiological properties of cortical neurons is important for understanding their computational functions. Hippocampome.org defines 122 neuron types in the rodent hippocampal formation (dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex) based on their somatic, axonal, and dendritic locations, putative excitatory/inhibitory outputs, molecular marker expression, and biophysical properties such as time constant and input resistance. Here we augment the electrophysiological data of this knowledge base by collecting, quantifying, and analyzing the firing responses to depolarizing current injections for every hippocampal neuron type from available published experiments. We designed and implemented objective protocols to classify firing patterns based on both transient and steady-state activity. Specifically, we identified 5 transients (delay, adapting spiking, rapidly adapting spiking, transient stuttering, and transient slow-wave bursting) and 4 steady states (non-adapting spiking, persistent stuttering, persistent slow-wave bursting, and silence). By characterizing the set of all firing responses reported for hippocampal neurons, this automated classification approach revealed 9 unique families of firing pattern phenotypes while distinguishing potential new neuronal subtypes. Several novel statistical associations also emerged between firing responses and other electrophysiological properties, morphological features, and molecular marker expression. The firing pattern parameters, complete experimental conditions (including solution and stimulus details), digitized spike times, exact reference to the original empirical evidence, and analysis scripts are released open-source through Hippocampome.org for all neuron types, greatly enhancing the existing search and browse capabilities. This information, collated online in human-and machine-accessible form, will help design and interpret both experiments and hippocampal model simulations.\n\nSignificance StatementComprehensive characterization of nerve cells is essential for understanding signal processing in biological neuronal networks. Firing patterns are important identification characteristics of neurons and play crucial roles in information coding in neural systems. Building upon the comprehensive knowledge base Hippocampome.org, we developed and implemented automated protocols to classify all known firing responses exhibited by each neuron type of the rodent hippocampus based on analysis of transient and steady-state activity. This approach identified the most distinguishing elements of every firing phenotype and revealed previously unnoticed statistical associations of firing responses with other electrophysiological, morphological, and molecular properties. The resulting data, freely released online, constitute a powerful resource for designing and interpreting experiments as well as developing and testing hippocampal models.

neuroscience↗