ArticlePLoS computational biology2016
The Computational Properties of a Simplified Cortical Column Model.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Layered structure of cortex explains reversal dynamics in bistable perception.Scientific reports · 2025Article
- Exploring the Architectural Biases of the Cortical Microcircuit.Neural computation · 2025Article
- Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons.Entropy (Basel, Switzerland) · 2024Article
- Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware.Frontiers in neuroscience · 2023Article
- Brain signal predictions from multi-scale networks using a linearized framework.PLoS computational biology · 2022Article
- A Unified Software/Hardware Scalable Architecture for Brain-Inspired Computing Based on Self-Organizing Neural Models.Frontiers in neuroscience · 2022Article
- NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model.Neural computation · 2021Article
- MIIND : A Model-Agnostic Simulator of Neural Populations.Frontiers in neuroinformatics · 2021Article
- Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits.PLoS computational biology · 2020Article
- The SONATA data format for efficient description of large-scale network models.PLoS computational biology · 2020Article
- Article
- Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits.PLoS computational biology · 2019Article
- Computational geometry for modeling neural populations: From visualization to simulation.PLoS computational biology · 2019Article
- Visual physiology of the layer 4 cortical circuit in silico.PLoS computational biology · 2018Article
- Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model.Frontiers in neuroscience · 2018Article
- Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience.Frontiers in neuroinformatics · 2018Review
- Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.PLoS computational biology · 2017Article
- Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The mammalian neocortex has a repetitious, laminar structure and performs functions integral to higher cognitive processes, including sensory perception, memory, and coordinated motor output. What computations does this circuitry subserve that link these unique structural elements to their function? Potjans and Diesmann (2014) parameterized a four-layer, two cell type (i.e. excitatory and inhibitory) model of a cortical column with homogeneous populations and cell type dependent connection probabilities. We implement a version of their model using a displacement integro-partial differential equation (DiPDE) population density model. This approach, exact in the limit of large homogeneous populations, provides a fast numerical method to solve equations describing the full probability density distribution of neuronal membrane potentials. It lends itself to quickly analyzing the mean response properties of population-scale firing rate dynamics. We use this strategy to examine the input-output relationship of the Potjans and Diesmann cortical column model to understand its computational properties. When inputs are constrained to jointly and equally target excitatory and inhibitory neurons, we find a large linear regime where the effect of a multi-layer input signal can be reduced to a linear combination of component signals. One of these, a simple subtractive operation, can act as an error signal passed between hierarchical processing stages.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.