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Learning to live with Dale’s principle: ANNs with separate excitatory and inhibitory units

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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The contribution of synaptic location to inhibitory gain control in pyramidal cells
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Group Normalization
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Hierarchical Neural Networks for Image Interpretation
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Modulation of Coordinated Activity across Cortical Layers by Plasticity of Inhibitory Synapses
inferredcites →https://doi.org/10.1016/j.neuron.2011.09.027openalex-snapshot
How Inhibition Shapes Cortical Activity
inferredcites →https://doi.org/10.1016/j.neuron.2011.12.013openalex-snapshot
Parvalbumin-Expressing Interneurons Linearly Transform Cortical Responses to Visual Stimuli
inferredcites →https://doi.org/10.1016/j.neuron.2012.07.030openalex-snapshot
Plasticity of Inhibition
inferredcites →https://doi.org/10.1016/j.neuron.2015.09.013openalex-snapshot
Inhibitory Actions Unified by Network Integration
inferredcites →https://doi.org/10.1016/j.neuron.2016.06.033openalex-snapshot
GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits
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A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy
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Two Distinct Neural Timescales for Predictive Speech Processing
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Theories of Error Back-Propagation in the Brain
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Glutamatergic synapses onto hippocampal interneurons: precision timing without lasting plasticity
inferredcites →https://doi.org/10.1038/nature11347openalex-snapshot
Division and subtraction by distinct cortical inhibitory networks in vivo
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Input normalization by global feedforward inhibition expands cortical dynamic range
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Mechanisms and functions of GABA co-release
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Long-term synaptic plasticity in hippocampal interneurons
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Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
inferredcites →https://doi.org/10.1073/pnas.1701812114openalex-snapshot
Inhibitory engrams in perception and memory
inferredcites →https://doi.org/10.1088/0305-4470/22/21/030openalex-snapshot
The interaction space of neural networks with sign-constrained synapses
inferredcites →https://doi.org/10.1093/acrefore/9780190264086.013.46openalex-snapshot
Deep Neural Networks in Computational Neuroscience
inferredcites →https://doi.org/10.1098/rsnr.1976.0015openalex-snapshot
From electrical to chemical transmission in the central nervous system: The closing address of the Sir Henry Dale Centennial Symposium Cambridge, 19 September 1975
inferredcites →https://doi.org/10.1101/133504openalex-snapshot
Deep Neural Networks in Computational Neuroscience
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Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?
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A modular neural network model of grasp movement generation
inferredcites →https://doi.org/10.1109/iccv.2015.123openalex-snapshot
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
inferredcites →https://doi.org/10.1126/science.1061395openalex-snapshot
Spike Transmission and Synchrony Detection in Networks of GABAergic Interneurons
inferredcites →https://doi.org/10.1126/science.1255263openalex-snapshot
Fast-spiking, parvalbumin + GABAergic interneurons: From cellular design to microcircuit function
inferredcites →https://doi.org/10.1162/neco.2008.07-06-295openalex-snapshot
Solving the Problem of Negative Synaptic Weights in Cortical Models
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Homeostatic plasticity in neural development
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Gain Control Network Conditions in Early Sensory Coding
inferredcites →https://doi.org/10.1371/journal.pcbi.1004792openalex-snapshot
Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework
inferredcites →https://doi.org/10.1371/journal.pone.0220547openalex-snapshot
Training dynamically balanced excitatory-inhibitory networks
inferredcites →https://doi.org/10.1523/jneurosci.20-16-06193.2000openalex-snapshot
Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models
inferredcites →https://doi.org/10.32470/ccn.2019.1265-0openalex-snapshot
Understanding the functional and structural differences across excitatory and inhibitory neurons
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1412.1193openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot
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Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
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inferredcites →https://doi.org/10.48550/arxiv.1807.00053openalex-snapshot
Task-Driven Convolutional Recurrent Models of the Visual System
inferredcites →https://doi.org/10.48550/arxiv.1807.04587openalex-snapshot
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
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Dendritic cortical microcircuits approximate the backpropagation algorithm
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Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-981-95-4381-6_5openalex-snapshot
Excitatory-Inhibitory Dynamics in Adaptive Decision-Making
inferred← citeshttps://doi.org/10.1007/s11571-023-09956-wopenalex-snapshot
Effect in the spectra of eigenvalues and dynamics of RNNs trained with excitatory–inhibitory constraint
inferred← citeshttps://doi.org/10.1016/j.neunet.2021.07.011openalex-snapshot
Bio-instantiated recurrent neural networks: Integrating neurobiology-based network topology in artificial networks
inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107628openalex-snapshot
Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1038/s42256-021-00430-yopenalex-snapshot
Neurons learn by predicting future activity
inferred← citeshttps://doi.org/10.1038/s42256-024-00845-3openalex-snapshot
Learning efficient backprojections across cortical hierarchies in real time
inferred← citeshttps://doi.org/10.1088/1751-8121/ac79e5openalex-snapshot
Satisfiability transition in asymmetric neural networks
inferred← citeshttps://doi.org/10.1101/2021.01.22.427744openalex-snapshot
Bio-instantiated recurrent neural networks
inferred← citeshttps://doi.org/10.1101/2021.06.21.449346openalex-snapshot
Heterogeneous ‘cell types’ can improve performance of deep neural networks
inferred← citeshttps://doi.org/10.1101/2022.05.03.490165openalex-snapshot
Organic Convolution in The Ventral Visual Pathway Can Explain the Variety of Shape Tuning in Area V4
inferred← citeshttps://doi.org/10.1101/2022.10.28.514169openalex-snapshot
Modelling the role of contour integration in visual inference
inferred← citeshttps://doi.org/10.1101/2023.01.02.522459openalex-snapshot
Learning Cortical Hierarchies with Temporal Hebbian Updates
inferred← citeshttps://doi.org/10.1101/2024.05.15.594236openalex-snapshot
Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks
inferred← citeshttps://doi.org/10.1101/2024.09.27.615483openalex-snapshot
The oneirogen hypothesis: modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms
inferred← citeshttps://doi.org/10.1101/2024.10.25.620272openalex-snapshot
Brain-like learning with exponentiated gradients
inferred← citeshttps://doi.org/10.1101/2025.01.09.632231openalex-snapshot
Constructing Biologically Constrained RNNs via Dale’s Backprop and Topologically-Informed Pruning
inferred← citeshttps://doi.org/10.1109/iccv51070.2023.02061openalex-snapshot
Why do networks have inhibitory/negative connections?
inferred← citeshttps://doi.org/10.1113/jp282755openalex-snapshot
How to incorporate biological insights into network models and why it matters
inferred← citeshttps://doi.org/10.1126/sciadv.adw4970openalex-snapshot
Constructing biologically constrained RNNs via Dale’s backpropagation and topologically informed pruning
inferred← citeshttps://doi.org/10.1146/annurev-vision-112823-030616openalex-snapshot
The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates
inferred← citeshttps://doi.org/10.1152/jn.00196.2026openalex-snapshot
What Dale never said: multiple incarnations of Dale’s law from the birth of pharmacology to modeling brain circuits
inferred← citeshttps://doi.org/10.1162/neco_a_01625openalex-snapshot
Modeling the Role of Contour Integration in Visual Inference
inferred← citeshttps://doi.org/10.1162/neco_a_01671openalex-snapshot
Desiderata for Normative Models of Synaptic Plasticity
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Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning
inferred← citeshttps://doi.org/10.21203/rs.3.rs-2099320/v1openalex-snapshot
An Organic Convolution Model of the Ventral Visual Pathway Can Explain the Curvature Selective Shape Tuning Found in Area V4
inferred← citeshttps://doi.org/10.3389/fcpxs.2025.1636222openalex-snapshot
Organizational regularities in recurrent neural networks
inferred← citeshttps://doi.org/10.3389/fncir.2025.1528309openalex-snapshot
Sparse connectivity enables efficient information processing in cortex-like artificial neural networks
inferred← citeshttps://doi.org/10.3389/fncom.2023.1136010openalex-snapshot
Learning cortical hierarchies with temporal Hebbian updates
inferred← citeshttps://doi.org/10.52202/075280-0045openalex-snapshot
Learning better with Dale’s Law: A Spectral Perspective
inferred← citeshttps://doi.org/10.64898/2026.06.16.732595openalex-snapshot
Cell-type-specific cortical feedback coordinates hierarchical credit assignment
inferred← citeshttps://doi.org/10.7554/elife.104101openalex-snapshot
Online reinforcement learning of state representation in recurrent network supported by the power of random feedback and biological constraints
inferred← citeshttps://doi.org/10.7554/elife.104101.1openalex-snapshot
Learning of state representation in recurrent network: the power of random feedback and biological constraints
inferred← citeshttps://doi.org/10.7554/elife.104101.2openalex-snapshot
Online reinforcement learning of state representation in recurrent network: the power of random feedback and biological constraints
inferred← citeshttps://doi.org/10.7554/elife.104101.3openalex-snapshot
Online reinforcement learning of state representation in recurrent network: the power of random feedback and biological constraints
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Biologically informed cortical models predict optogenetic perturbations
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Theoretical principles explain the structure of the insect head direction circuit
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