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Found 12 papers, 3 papers with code
Brain-inspired Computational Modeling of Action Recognition with Recurrent Spiking Neural Networks Equipped with Reinforcement Delay Learning
In this article, we present an effective brain-inspired computational model for action recognition.
Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP
The human brain constantly learns and rapidly adapts to new situations by integrating acquired knowledge and experiences into memory.
Enhancing efficiency of object recognition in different categorization levels by reinforcement learning in modular spiking neural networks
The human visual system contains a hierarchical sequence of modules that take part in visual perception at superordinate, basic, and subordinate categorization levels.
Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks
The plasticity of the conduction delay between neurons plays a fundamental role in learning.
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron
Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient.
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks
We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lower layers and reward-modulated STDP (R-STDP) for the higher ones.
First-spike based visual categorization using reward-modulated STDP
In the highest layers, each neuron was assigned to an object category, and it was assumed that the stimulus category was the category of the first neuron to fire.
Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer
This means that, low frequency information is sufficient for superordinate level, but not for the basic and subordinate levels.
STDP-based spiking deep convolutional neural networks for object recognition
Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron.
Humans and deep networks largely agree on which kinds of variation make object recognition harder
This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best algorithms for object recognition in natural images.
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition
Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases.
Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition
Retinal image of surrounding objects varies tremendously due to the changes in position, size, pose, illumination condition, background context, occlusion, noise, and nonrigid deformations.
