91 research outputs found
Backprop Diffusion is Biologically Plausible
The Backpropagation algorithm relies on the abstraction of using a neural
model that gets rid of the notion of time, since the input is mapped
instantaneously to the output. In this paper, we claim that this abstraction of
ignoring time, along with the abrupt input changes that occur when feeding the
training set, are in fact the reasons why, in some papers, Backprop biological
plausibility is regarded as an arguable issue. We show that as soon as a deep
feedforward network operates with neurons with time-delayed response, the
backprop weight update turns out to be the basic equation of a biologically
plausible diffusion process based on forward-backward waves. We also show that
such a process very well approximates the gradient for inputs that are not too
fast with respect to the depth of the network. These remarks somewhat disclose
the diffusion process behind the backprop equation and leads us to interpret
the corresponding algorithm as a degeneration of a more general diffusion
process that takes place also in neural networks with cyclic connections.Comment: 9 pages, 3 figures. arXiv admin note: text overlap with
arXiv:1907.0510
Brain-Inspired Computational Intelligence via Predictive Coding
Artificial intelligence (AI) is rapidly becoming one of the key technologies
of this century. The majority of results in AI thus far have been achieved
using deep neural networks trained with the error backpropagation learning
algorithm. However, the ubiquitous adoption of this approach has highlighted
some important limitations such as substantial computational cost, difficulty
in quantifying uncertainty, lack of robustness, unreliability, and biological
implausibility. It is possible that addressing these limitations may require
schemes that are inspired and guided by neuroscience theories. One such theory,
called predictive coding (PC), has shown promising performance in machine
intelligence tasks, exhibiting exciting properties that make it potentially
valuable for the machine learning community: PC can model information
processing in different brain areas, can be used in cognitive control and
robotics, and has a solid mathematical grounding in variational inference,
offering a powerful inversion scheme for a specific class of continuous-state
generative models. With the hope of foregrounding research in this direction,
we survey the literature that has contributed to this perspective, highlighting
the many ways that PC might play a role in the future of machine learning and
computational intelligence at large.Comment: 37 Pages, 9 Figure
Replacing Backpropagation with Biological Plausible Top-down Credit Assignment in Deep Neural Networks Training
Top-down connections in the biological brain has been shown to be important
in high cognitive functions. However, the function of this mechanism in machine
learning has not been defined clearly. In this study, we propose to lay out a
framework constituted by a bottom-up and a top-down network. Here, we use a
Top-down Credit Assignment Network (TDCA-network) to replace the loss function
and back propagation (BP) which serve as the feedback mechanism in traditional
bottom-up network training paradigm. Our results show that the credit given by
well-trained TDCA-network outperforms the gradient from backpropagation in
classification task under different settings on multiple datasets. In addition,
we successfully use a credit diffusing trick, which can keep training and
testing performance remain unchanged, to reduce parameter complexity of the
TDCA-network. More importantly, by comparing their trajectories in the
parameter landscape, we find that TDCA-network directly achieved a global
optimum, in contrast to that backpropagation only can gain a localized optimum.
Thus, our results demonstrate that TDCA-network not only provide a biological
plausible learning mechanism, but also has the potential to directly achieve
global optimum, indicating that top-down credit assignment can substitute
backpropagation, and provide a better learning framework for Deep Neural
Networks
Adaptive extreme edge computing for wearable devices
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions towards smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g. memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices
Training Spiking Neural Networks Using Lessons From Deep Learning
The brain is the perfect place to look for inspiration to develop more
efficient neural networks. The inner workings of our synapses and neurons
provide a glimpse at what the future of deep learning might look like. This
paper serves as a tutorial and perspective showing how to apply the lessons
learnt from several decades of research in deep learning, gradient descent,
backpropagation and neuroscience to biologically plausible spiking neural
neural networks. We also explore the delicate interplay between encoding data
as spikes and the learning process; the challenges and solutions of applying
gradient-based learning to spiking neural networks; the subtle link between
temporal backpropagation and spike timing dependent plasticity, and how deep
learning might move towards biologically plausible online learning. Some ideas
are well accepted and commonly used amongst the neuromorphic engineering
community, while others are presented or justified for the first time here. A
series of companion interactive tutorials complementary to this paper using our
Python package, snnTorch, are also made available:
https://snntorch.readthedocs.io/en/latest/tutorials/index.htm
Training a Hopfield Variational Autoencoder with Equilibrium Propagation
On dedicated analog hardware, equilibrium propagation is an energy-efficient
alternative to backpropagation. In spite of its theoretical guarantees, its
application in the AI domain remains limited to the discriminative setting.
Meanwhile, despite its high computational demands, generative AI is on the
rise. In this paper, we demonstrate the application of Equilibrium Propagation
in training a variational autoencoder (VAE) for generative modeling. Leveraging
the symmetric nature of Hopfield networks, we propose using a single model to
serve as both the encoder and decoder which could effectively halve the
required chip size for VAE implementations, paving the way for more efficient
analog hardware configurations.Comment: Associative Memory & Hopfield Networks in 2023 (NeurIPS 2023
workshop
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