31 datasets found

The dataset used in this paper is a 40x40 neuron sheet with a grid-like firing pattern, which is used to simulate the behavior of grid cells in the medial entorhinal cortex.

The dataset used in the paper describes the dynamics of synaptic plasticity and learning in neural networks.

The dataset used in the paper describes the dynamics of metabolic resource transport across the network of glial cells, which can stabilize learning dynamics in neuronal networks.

The dataset used in this paper is a collection of spike trains from different neural motifs.

The dataset used in this paper is a collection of spike trains from leaky-integrate-and-fire (LIF) neurons.

The dataset used in this paper is a collection of spike trains from salamander retina.

The dataset used in this paper is a collection of neural abstractions for dynamical models. The dataset consists of neural networks with different activation functions...

The dataset is used to model the mammalian olfactory system and solve non-negative sparse approximation problems.

The dataset describes the coexistence of fast and slow gamma oscillations in a single inhibitory population of neurons.

The dataset used in the paper is a randomly-generated dataset of lattices, where each lattice is represented by a basis of n vectors in R^n.

The dataset used in the paper is a collection of time series data from a dynamical system, which is used to test the performance of the network-based LMMs for discovering...

The proposed method for learning control for an unknown nonlinear dynamical system by formulating a system identification task.

ExNN dataset used for credit default prediction

The dataset used in the paper is a discrete-time stochastic dynamical system with two novel aspects: (a) using ranking supermartingales (RSMs) to certify almost-sure asymptotic...

Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns...

Simulated neural networks and larval zebrafish imaging data

The dataset used in this paper is a binary neural network model.

A comprehensive Python framework for evaluating large-scale deep neural networks (DNN) on memristive and memcapacitive crossbar systems, addressing various non-idealities.

The dataset used in the paper is a collection of functions that can be represented by a deep network, but cannot be represented by a shallow network.

The dataset is used for learning Hamiltonian neural networks.