22 research outputs found
Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images
In this work, we develop convolutional neural generative coding (Conv-NGC), a
generalization of predictive coding to the case of
convolution/deconvolution-based computation. Specifically, we concretely
implement a flexible neurobiologically-motivated algorithm that progressively
refines latent state maps in order to dynamically form a more accurate internal
representation/reconstruction model of natural images. The performance of the
resulting sensory processing system is evaluated on several benchmark datasets
such as Color-MNIST, CIFAR-10, and Street House View Numbers (SVHN). We study
the effectiveness of our brain-inspired neural system on the tasks of
reconstruction and image denoising and find that it is competitive with
convolutional auto-encoding systems trained by backpropagation of errors and
notably outperforms them with respect to out-of-distribution reconstruction
(including on the full 90k CINIC-10 test set)
The Predictive Forward-Forward Algorithm
We propose the predictive forward-forward (PFF) algorithm for conducting
credit assignment in neural systems. Specifically, we design a novel, dynamic
recurrent neural system that learns a directed generative circuit jointly and
simultaneously with a representation circuit. Notably, the system integrates
learnable lateral competition, noise injection, and elements of predictive
coding, an emerging and viable neurobiological process theory of cortical
function, with the forward-forward (FF) adaptation scheme. Furthermore, PFF
efficiently learns to propagate learning signals and updates synapses with
forward passes only, eliminating key structural and computational constraints
imposed by backpropagation-based schemes. Besides computational advantages, the
PFF process could prove useful for understanding the learning mechanisms behind
biological neurons that use local signals despite missing feedback connections.
We run experiments on image data and demonstrate that the PFF procedure works
as well as backpropagation, offering a promising brain-inspired algorithm for
classifying, reconstructing, and synthesizing data patterns.Comment: More revisions/edits, update to key diagram depicting PFF process,
link to algorithm / simulation code (repo) now include
Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems
In this article, we propose a backpropagation-free approach to robotic
control through the neuro-cognitive computational framework of neural
generative coding (NGC), designing an agent built completely from powerful
predictive coding/processing circuits that facilitate dynamic, online learning
from sparse rewards, embodying the principles of planning-as-inference.
Concretely, we craft an adaptive agent system, which we call active predictive
coding (ActPC), that balances an internally-generated epistemic signal (meant
to encourage intelligent exploration) with an internally-generated instrumental
signal (meant to encourage goal-seeking behavior) to ultimately learn how to
control various simulated robotic systems as well as a complex robotic arm
using a realistic robotics simulator, i.e., the Surreal Robotics Suite, for the
block lifting task and can pick-and-place problems. Notably, our experimental
results demonstrate that our proposed ActPC agent performs well in the face of
sparse (extrinsic) reward signals and is competitive with or outperforms
several powerful backprop-based RL approaches.Comment: Contains appendix with pseudocode and additional detail
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting
In lifelong learning systems, especially those based on artificial neural
networks, one of the biggest obstacles is the severe inability to retain old
knowledge as new information is encountered. This phenomenon is known as
catastrophic forgetting. In this article, we propose a new kind of
connectionist architecture, the Sequential Neural Coding Network, that is
robust to forgetting when learning from streams of data points and, unlike
networks of today, does not learn via the immensely popular back-propagation of
errors. Grounded in the neurocognitive theory of predictive processing, our
model adapts its synapses in a biologically-plausible fashion, while another,
complementary neural system rapidly learns to direct and control this
cortex-like structure by mimicking the task-executive control functionality of
the basal ganglia. In our experiments, we demonstrate that our self-organizing
system experiences significantly less forgetting as compared to standard neural
models and outperforms a wide swath of previously proposed methods even though
it is trained across task datasets in a stream-like fashion. The promising
performance of our complementary system on benchmarks, e.g., SplitMNIST, Split
Fashion MNIST, and Split NotMNIST, offers evidence that by incorporating
mechanisms prominent in real neuronal systems, such as competition, sparse
activation patterns, and iterative input processing, a new possibility for
tackling the grand challenge of lifelong machine learning opens up.Comment: Key updates including results on standard benchmarks, e.g., split
mnist/fmnist/not-mnist. Task selection/basal ganglia model has been
integrate
Provably Stable Interpretable Encodings of Context Free Grammars in RNNs with a Differentiable Stack
Given a collection of strings belonging to a context free grammar (CFG) and
another collection of strings not belonging to the CFG, how might one infer the
grammar? This is the problem of grammatical inference. Since CFGs are the
languages recognized by pushdown automata (PDA), it suffices to determine the
state transition rules and stack action rules of the corresponding PDA. An
approach would be to train a recurrent neural network (RNN) to classify the
sample data and attempt to extract these PDA rules. But neural networks are not
a priori aware of the structure of a PDA and would likely require many samples
to infer this structure. Furthermore, extracting the PDA rules from the RNN is
nontrivial. We build a RNN specifically structured like a PDA, where weights
correspond directly to the PDA rules. This requires a stack architecture that
is somehow differentiable (to enable gradient-based learning) and stable (an
unstable stack will show deteriorating performance with longer strings). We
propose a stack architecture that is differentiable and that provably exhibits
orbital stability. Using this stack, we construct a neural network that
provably approximates a PDA for strings of arbitrary length. Moreover, our
model and method of proof can easily be generalized to other state machines,
such as a Turing Machine.Comment: 20 pages, 2 figure
On the Tensor Representation and Algebraic Homomorphism of the Neural State Turing Machine
Recurrent neural networks (RNNs) and transformers have been shown to be
Turing-complete, but this result assumes infinite precision in their hidden
representations, positional encodings for transformers, and unbounded
computation time in general. In practical applications, however, it is crucial
to have real-time models that can recognize Turing complete grammars in a
single pass. To address this issue and to better understand the true
computational power of artificial neural networks (ANNs), we introduce a new
class of recurrent models called the neural state Turing machine (NSTM). The
NSTM has bounded weights and finite-precision connections and can simulate any
Turing Machine in real-time. In contrast to prior work that assumes unbounded
time and precision in weights, to demonstrate equivalence with TMs, we prove
that a -neuron bounded tensor RNN, coupled with third-order synapses, can
model any TM class in real-time. Furthermore, under the Markov assumption, we
provide a new theoretical bound for a non-recurrent network augmented with
memory, showing that a tensor feedforward network with th-order finite
precision weights is equivalent to a universal TM.Comment: 14 pages, 7 table