[HTML][HTML] Spiking neural networks and their applications: A review
The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …
domains. However, deep neural networks are very resource-intensive in terms of energy …
The neuroconnectionist research programme
A Doerig, RP Sommers, K Seeliger… - Nature Reviews …, 2023 - nature.com
Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …
Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …
chips with high energy efficiency by introducing neural dynamics and spike properties. As …
[HTML][HTML] Hybrid 2D–CMOS microchips for memristive applications
Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate
advanced electronic circuits is a major goal for the semiconductor industry,. However, most …
advanced electronic circuits is a major goal for the semiconductor industry,. However, most …
Advancing neuromorphic computing with loihi: A survey of results and outlook
Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
[HTML][HTML] Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks
As a key building block of biological cortex, neurons are powerful information processing
units and can achieve highly complex nonlinear computations even in individual cells …
units and can achieve highly complex nonlinear computations even in individual cells …
Intracellular chloride regulation mediates local sleep pressure in the cortex
Extended wakefulness is associated with reduced performance and the build-up of sleep
pressure. In the cortex, this manifests as changes in network activity. These changes show …
pressure. In the cortex, this manifests as changes in network activity. These changes show …
[HTML][HTML] Hardware implementation of memristor-based artificial neural networks
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …
techniques, which rely on networks of connected simple computing units operating in …
An increase of inhibition drives the developmental decorrelation of neural activity
Throughout development, the brain transits from early highly synchronous activity patterns to
a mature state with sparse and decorrelated neural activity, yet the mechanisms underlying …
a mature state with sparse and decorrelated neural activity, yet the mechanisms underlying …
Associative and predictive hippocampal codes support memory-guided behaviors
Episodic memory involves learning and recalling associations between items and their
spatiotemporal context. Those memories can be further used to generate internal models of …
spatiotemporal context. Those memories can be further used to generate internal models of …