61 research outputs found
Protocol for dissecting cascade computational components in neural networks of a visual system
Finding the complete functional circuits of neurons is a challenging problem in brain research. Here, we present a protocol, based on visual stimuli and spikes, for obtaining the complete circuit of recorded neurons using spike-triggered nonnegative matrix factorization. We describe steps for data preprocessing, inferring the spatial receptive field of the subunits, and analyzing the module matrix. This approach identifies computational components of the feedforward network of retinal ganglion cells and dissects the network structure based on natural image stimuli.For complete details on the use and execution of this protocol, please refer to Jia et al. (2021).
Exploring Asymmetric Tunable Blind-Spots for Self-supervised Denoising in Real-World Scenarios
Self-supervised denoising has attracted widespread attention due to its
ability to train without clean images. However, noise in real-world scenarios
is often spatially correlated, which causes many self-supervised algorithms
based on the pixel-wise independent noise assumption to perform poorly on
real-world images. Recently, asymmetric pixel-shuffle downsampling (AP) has
been proposed to disrupt the spatial correlation of noise. However,
downsampling introduces aliasing effects, and the post-processing to eliminate
these effects can destroy the spatial structure and high-frequency details of
the image, in addition to being time-consuming. In this paper, we
systematically analyze downsampling-based methods and propose an Asymmetric
Tunable Blind-Spot Network (AT-BSN) to address these issues. We design a
blind-spot network with a freely tunable blind-spot size, using a large
blind-spot during training to suppress local spatially correlated noise while
minimizing damage to the global structure, and a small blind-spot during
inference to minimize information loss. Moreover, we propose blind-spot
self-ensemble and distillation of non-blind-spot network to further improve
performance and reduce computational complexity. Experimental results
demonstrate that our method achieves state-of-the-art results while
comprehensively outperforming other self-supervised methods in terms of image
texture maintaining, parameter count, computation cost, and inference time
Spike timing reshapes robustness against attacks in spiking neural networks
The success of deep learning in the past decade is partially shrouded in the
shadow of adversarial attacks. In contrast, the brain is far more robust at
complex cognitive tasks. Utilizing the advantage that neurons in the brain
communicate via spikes, spiking neural networks (SNNs) are emerging as a new
type of neural network model, boosting the frontier of theoretical
investigation and empirical application of artificial neural networks and deep
learning. Neuroscience research proposes that the precise timing of neural
spikes plays an important role in the information coding and sensory processing
of the biological brain. However, the role of spike timing in SNNs is less
considered and far from understood. Here we systematically explored the timing
mechanism of spike coding in SNNs, focusing on the robustness of the system
against various types of attacks. We found that SNNs can achieve higher
robustness improvement using the coding principle of precise spike timing in
neural encoding and decoding, facilitated by different learning rules. Our
results suggest that the utility of spike timing coding in SNNs could improve
the robustness against attacks, providing a new approach to reliable coding
principles for developing next-generation brain-inspired deep learning
One Forward is Enough for Neural Network Training via Likelihood Ratio Method
While backpropagation (BP) is the mainstream approach for gradient
computation in neural network training, its heavy reliance on the chain rule of
differentiation constrains the designing flexibility of network architecture
and training pipelines. We avoid the recursive computation in BP and develop a
unified likelihood ratio (ULR) method for gradient estimation with just one
forward propagation. Not only can ULR be extended to train a wide variety of
neural network architectures, but the computation flow in BP can also be
rearranged by ULR for better device adaptation. Moreover, we propose several
variance reduction techniques to further accelerate the training process. Our
experiments offer numerical results across diverse aspects, including various
neural network training scenarios, computation flow rearrangement, and
fine-tuning of pre-trained models. All findings demonstrate that ULR
effectively enhances the flexibility of neural network training by permitting
localized module training without compromising the global objective and
significantly boosts the network robustness
Neural System Identification with Spike-triggered Non-negative Matrix Factorization
Neuronal circuits formed in the brain are complex with intricate connection
patterns. Such complexity is also observed in the retina as a relatively simple
neuronal circuit. A retinal ganglion cell receives excitatory inputs from
neurons in previous layers as driving forces to fire spikes. Analytical methods
are required that can decipher these components in a systematic manner.
Recently a method termed spike-triggered non-negative matrix factorization
(STNMF) has been proposed for this purpose. In this study, we extend the scope
of the STNMF method. By using the retinal ganglion cell as a model system, we
show that STNMF can detect various computational properties of upstream bipolar
cells, including spatial receptive field, temporal filter, and transfer
nonlinearity. In addition, we recover synaptic connection strengths from the
weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of
a ganglion cell into a few subsets of spikes where each subset is contributed
by one presynaptic bipolar cell. Taken together, these results corroborate that
STNMF is a useful method for deciphering the structure of neuronal circuits.Comment: updated versio
A Novel Noise Injection-based Training Scheme for Better Model Robustness
Noise injection-based method has been shown to be able to improve the
robustness of artificial neural networks in previous work. In this work, we
propose a novel noise injection-based training scheme for better model
robustness. Specifically, we first develop a likelihood ratio method to
estimate the gradient with respect to both synaptic weights and noise levels
for stochastic gradient descent training. Then, we design an approximation for
the vanilla noise injection-based training method to reduce memory and improve
computational efficiency. Next, we apply our proposed scheme to spiking neural
networks and evaluate the performance of classification accuracy and robustness
on MNIST and Fashion-MNIST datasets. Experiment results show that our proposed
method achieves a much better performance on adversarial robustness and
slightly better performance on original accuracy, compared with the
conventional gradient-based training method
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