Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm DOI
Sai Sukruth Bezugam, Yihao Wu,

JaeBum Yoo

и другие.

Опубликована: Апрель 23, 2024

In this study, we propose the first hardware implementation of a context-based recurrent spiking neural network (RSNN) emphasizing on integrating dual information streams within neocortical pyramidal neurons specifically Context-Dependent Leaky Integrate and Fire (CLIF) neuron models, essential element in RSNN. We present quantized version CLIF (qCLIF), developed through hardware-software codesign approach utilizing sparse activity Implemented 45nm technology node, qCLIF is compact (900um²) achieves high accuracy 90% despite 8 bit quantization DVS gesture classification dataset. Our analysis spans configuration from 10 to 200 neurons, supporting up 82k synapses 1.86 mm² footprint, demonstrating scalability efficiency.

Язык: Английский

Molecular insights into diabetic wound healing: Focus on Wnt/β-catenin and MAPK/ERK signaling pathways DOI
Shivam Pandey,

Tushar Anshu,

Krushna Ch Maharana

и другие.

Cytokine, Год журнала: 2025, Номер 191, С. 156957 - 156957

Опубликована: Май 13, 2025

Язык: Английский

Процитировано

1
Advancing neural computation: experimental validation and optimization of dendritic learning in feedforward tree networks DOI
Seyed‐Ali Sadegh‐Zadeh,

Pooya Hazegh

American Journal of Neurodegenerative Disease, Год журнала: 2024, Номер 13(5), С. 49 - 69

Опубликована: Янв. 1, 2024

This study aims to explore the capabilities of dendritic learning within feedforward tree networks (FFTN) in comparison traditional synaptic plasticity models, particularly context digit recognition tasks using MNIST dataset. We employed FFTNs with nonlinear segment amplification and Hebbian rules enhance computational efficiency. The dataset, consisting 70,000 images handwritten digits, was used for training testing. Key performance metrics, including accuracy, precision, recall, F1-score, were analysed. models significantly outperformed plasticity-based across all metrics. Specifically, framework achieved a test accuracy 91%, compared 88% demonstrating superior classification. Dendritic offers more powerful by closely mimicking biological neural processes, providing enhanced efficiency scalability. These findings have important implications advancing both artificial intelligence systems neuroscience.

Язык: Английский

Процитировано

0
Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm DOI
Sai Sukruth Bezugam, Yihao Wu,

JaeBum Yoo

и другие.

Опубликована: Апрель 23, 2024

In this study, we propose the first hardware implementation of a context-based recurrent spiking neural network (RSNN) emphasizing on integrating dual information streams within neocortical pyramidal neurons specifically Context-Dependent Leaky Integrate and Fire (CLIF) neuron models, essential element in RSNN. We present quantized version CLIF (qCLIF), developed through hardware-software codesign approach utilizing sparse activity Implemented 45nm technology node, qCLIF is compact (900um²) achieves high accuracy 90% despite 8 bit quantization DVS gesture classification dataset. Our analysis spans configuration from 10 to 200 neurons, supporting up 82k synapses 1.86 mm² footprint, demonstrating scalability efficiency.

Язык: Английский

Процитировано

0