Data_Sheet_1_A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks.PDF by Yan Fang (443710)

Comparison of parameter ranges used for Latin hypercube sampling (LHS) in [29] and genetic algorithms (GA) in the present work. by Jyotika Bahuguna (729510)

Data_Sheet_1_Back-Propagation Learning in Deep Spike-By-Spike Networks.pdf by David Rotermund (7213793)

Data_Sheet_2_Back-Propagation Learning in Deep Spike-By-Spike Networks.ZIP by David Rotermund (7213793)

Graphical representation of the optimization process in machine learning, focusing on the adjustment of weights during gradient descent. by Liu Liu (512237)

Data_Sheet_1_ACE-SNN: Algorithm-Hardware Co-design of Energy-Efficient & Low-Latency Deep Spiking Neural Networks for 3D Image Recognition.PDF by Gourav Datta (12363145)

Firing patterns of a-thorny cells encode more information about the statistics of presynaptic inputs. by Daniele Linaro (227790)

Parameter fitting for input and sampler layers in the EVA model. by Quynh-Anh Nguyen (847240)

Data_Sheet_1_A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction.docx by Asha Vijayan (14181197)

“…Using abstractions from cerebellar connections and learning rules of deep learning network (DLN), patterns were discriminated within datasets, and the same algorithm was used for trajectory optimization. In the current work, a cerebellum-inspired spiking neural network with dynamics of cerebellar neurons and learning mechanisms attributed to the granular layer, Purkinje cell (PC) layer, and cerebellar nuclei interconnected by excitatory and inhibitory synapses was implemented. …”

Presentation_1_SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.pdf by Emanuele Gemo (7932317)

“…This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. …”

Multivariate Temporal Point Process Regression by Xiwei Tang (807045)

“…In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. …”

Data_Sheet_1_Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics.pdf by Lea Steffen (11040255)

“…The SNN used for this benchmark is modeled in the simulator-independent declarative language PyNN, which allows using the same model for different simulator backends. …”

Outline of OSCNN. by Reyhane Ahmadi (14857363)

“…In this work, we introduce the pioneering Free-space Optical Deep Spiking Convolutional Neural Network (OSCNN), a novel approach inspired by the computational model of the human eye. …”

OSCNN accuracy with different feature extractors. by Reyhane Ahmadi (14857363)

“…In this work, we introduce the pioneering Free-space Optical Deep Spiking Convolutional Neural Network (OSCNN), a novel approach inspired by the computational model of the human eye. …”

Data_Sheet_1_Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.pdf by Alper Yegenoglu (12659710)

“…This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. …”

Data_Sheet_1_Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.pdf by Alper Yegenoglu (12659710)

“…This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. …”

Data_Sheet_1_E-prop on SpiNNaker 2: Exploring online learning in spiking RNNs on neuromorphic hardware.PDF by Amirhossein Rostami (14181200)

“…Introduction<p>In recent years, the application of deep learning models at the edge has gained attention. Typically, artificial neural networks (ANNs) are trained on graphics processing units (GPUs) and optimized for efficient execution on edge devices. …”

Decision-making deconstructed. by Jyotika Bahuguna (729510)

Video1_Investigating visual navigation using spiking neural network models of the insect mushroom bodies.MP4 by Oluwaseyi Oladipupo Jesusanmi (18596578)

“…We embodied the mushroom body model and comparator visual navigation algorithms on a Quanser Q-car robot with all processing running on an Nvidia Jetson TX2. …”

Pre-Synaptic Pool Modification (PSPM): A supervised learning procedure for recurrent spiking neural networks - Fig 1 by Bryce Allen Bagley (8486247)