ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2006.01001
  4. Cited By
Artificial neural networks for neuroscientists: A primer

Artificial neural networks for neuroscientists: A primer

1 June 2020
G. R. Yang
Xiao-Jing Wang
ArXivPDFHTML

Papers citing "Artificial neural networks for neuroscientists: A primer"

7 / 7 papers shown
Title
Items or Relations -- what do Artificial Neural Networks learn?
Items or Relations -- what do Artificial Neural Networks learn?
Renate Krause
Stefan Reimann
NAI
14
0
0
15 Apr 2024
Temporal Conditioning Spiking Latent Variable Models of the Neural
  Response to Natural Visual Scenes
Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes
Gehua (Marcus) Ma
Runhao Jiang
Rui Yan
Huajin Tang
AI4TS
42
6
0
21 Jun 2023
Benchmarking the human brain against computational architectures
Benchmarking the human brain against computational architectures
Céline van Valkenhoef
Catherine D. Schuman
P. Walther
8
0
0
15 May 2023
Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis
  using MRI Images and Ensemble Bagging Classifier
Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier
P. Patro
Tripti Goel
S. A. VaraPrasad
Md. Iftekhar Tanveer
R. Murugan
24
4
0
05 Nov 2022
A Step Towards Uncovering The Structure of Multistable Neural Networks
A Step Towards Uncovering The Structure of Multistable Neural Networks
Magnus Tournoy
B. Doiron
6
1
0
06 Oct 2022
Long short-term memory and learning-to-learn in networks of spiking
  neurons
Long short-term memory and learning-to-learn in networks of spiking neurons
G. Bellec
Darjan Salaj
Anand Subramoney
Robert Legenstein
Wolfgang Maass
119
481
0
26 Mar 2018
Demixed principal component analysis of population activity in higher
  cortical areas reveals independent representation of task parameters
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters
D. Kobak
Wieland Brendel
C. Constantinidis
C. Feierstein
Adam Kepecs
Z. Mainen
R. Romo
Xue-Lian Qi
N. Uchida
C. Machens
42
464
0
22 Oct 2014
1