Neural Network Stability

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37 papers9 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Transmission Neural Networks: Inhibitory and Excitatory Connections
Apr 5, 2026Shuang Gao, Peter E. CainesNeural Network Stability Neural Processes
Department of Electrical Engineering, Polytechnique Montreal, GERAD (Group for Research in Decision Analysis), and UNIQUE (Unifying Neuroscience and Artificial Intelligence - Quebec), Montreal, QC, Canada · Department of Electrical and Computer Engineering, McGill University & GERAD, Montreal, QC, Canada
Product-Stability: Provable Convergence for Gradient Descent on the Edge of Stability
Apr 3, 2026Eric GanDeep Learning Optimization Edge of Stability
Independent Researcher.
Spectral Higher-Order Neural Networks
Mar 30, 2026Gianluca Peri, Timoteo Carletti, Duccio Fanelli +1Neural Network Stability Feedforward Neural Networks
University of Florence, Italy · University of Namur, Belgium
Stability-Preserving Online Adaptation of Neural Closed-loop Maps
Mar 23, 2026Danilo Saccani, Luca Furieri, Giancarlo Ferrari-TrecateFeedback Control Neural Network Stability
Institute of Mechanical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland. · Department of Engineering Science, University of Oxford, United Kingdom
Heterogeneous Time Constants Improve Stability in Equilibrium Propagation
Mar 3, 2026Yoshimasa Kubo, Suhani Pragnesh Modi, Smit PatelEquilibrium Propagation Neural Network Training Dynamics
Department of Computer Science, Lakehead University
Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
Feb 26, 2026Wenquan Ma, Yang Sui, Jiaye Teng +3Generalization Bounds Algorithmic Generalization
Dynamical stability for dense patterns in attractor neural networks
Jul 14, 2025Uri Cohen, Máté LengyelNeural Network Memorization Associative Memory
Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK · Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary

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Transmission Neural Networks: Inhibitory and Excitatory Connections

Shuang Gao, Peter E. Caines

Department of Electrical Engineering, Polytechnique Montreal, GERAD (Group for Research in Decision Analysis), and UNIQUE (Unifying Neuroscience and Artificial Intelligence - Quebec), Montreal, QC, Canada · Department of Electrical and Computer Engineering, McGill University & GERAD, Montreal, QC, Canada

Neural Network Stability Neural Processes

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This paper extends the Transmission Neural Network model proposed by Gao and Caines in [1]-[3] to incorporate inhibitory connections and neurotransmitter populations. The extended network model contains binary neuronal states, transmission dynamics, and inhibitory and excitatory connections. Under technical assumptions, we establish the characterization of the firing probabilities of neurons, and show that such a characterization considering inhibitions can be equivalently represented by a neural network where each neuron has a continuous state of dimension 2. Moreover, we incorporated neurotransmitter populations into the modeling and establish the limit network model when the number of neurotransmitters at all synaptic connections go to infinity. Finally, sufficient conditions for stability and contraction properties of the limit network model are established.