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Found 5 papers, 1 papers with code
Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
We observe that the performance of this feed-forward Hebbian learning for fully test-time adaptation can be significantly improved by incorporating a feedback neuro-modulation layer.
Discrete Time Convolution for Fast Event-Based Stereo
Inspired by continuous dynamics of biological neuron models, we propose a novel encoding method for sparse events - continuous time convolution (CTC) - which learns to model the spatial feature of the data with intrinsic dynamics.
Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks
Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately.
Accelerated physical emulation of Bayesian inference in spiking neural networks
The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices.
Spiking neurons with short-term synaptic plasticity form superior generative networks
In this work, we use networks of leaky integrate-and-fire neurons that are trained to perform both discriminative and generative tasks in their forward and backward information processing paths, respectively.
