22 research outputs found
Effect of top-down connections in Hierarchical Sparse Coding
Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent
multi-dimensional, structured data such as images. The simplest solution to
solve this computationally hard problem is to decompose it into independent
layer-wise subproblems. However, neuroscientific evidence would suggest
inter-connecting these subproblems as in the Predictive Coding (PC) theory,
which adds top-down connections between consecutive layers. In this study, a
new model called 2-Layers Sparse Predictive Coding (2L-SPC) is introduced to
assess the impact of this inter-layer feedback connection. In particular, the
2L-SPC is compared with a Hierarchical Lasso (Hi-La) network made out of a
sequence of independent Lasso layers. The 2L-SPC and the 2-layers Hi-La
networks are trained on 4 different databases and with different sparsity
parameters on each layer. First, we show that the overall prediction error
generated by 2L-SPC is lower thanks to the feedback mechanism as it transfers
prediction error between layers. Second, we demonstrate that the inference
stage of the 2L-SPC is faster to converge than for the Hi-La model. Third, we
show that the 2L-SPC also accelerates the learning process. Finally, the
qualitative analysis of both models dictionaries, supported by their activation
probability, show that the 2L-SPC features are more generic and informative
Learning Spiking Neural Systems with the Event-Driven Forward-Forward Process
We develop a novel credit assignment algorithm for information processing
with spiking neurons without requiring feedback synapses. Specifically, we
propose an event-driven generalization of the forward-forward and the
predictive forward-forward learning processes for a spiking neural system that
iteratively processes sensory input over a stimulus window. As a result, the
recurrent circuit computes the membrane potential of each neuron in each layer
as a function of local bottom-up, top-down, and lateral signals, facilitating a
dynamic, layer-wise parallel form of neural computation. Unlike spiking neural
coding, which relies on feedback synapses to adjust neural electrical activity,
our model operates purely online and forward in time, offering a promising way
to learn distributed representations of sensory data patterns with temporal
spike signals. Notably, our experimental results on several pattern datasets
demonstrate that the even-driven forward-forward (ED-FF) framework works well
for training a dynamic recurrent spiking system capable of both classification
and reconstruction
Predictive coding in auditory perception: challenges and unresolved questions.
Predictive coding is arguably the currently dominant theoretical framework for the study of perception. It has been employed to explain important auditory perceptual phenomena, and it has inspired theoretical, experimental and computational modelling efforts aimed at describing how the auditory system parses the complex sound input into meaningful units (auditory scene analysis). These efforts have uncovered some vital questions, addressing which could help to further specify predictive coding and clarify some of its basic assumptions. The goal of the current review is to motivate these questions and show how unresolved issues in explaining some auditory phenomena lead to general questions of the theoretical framework. We focus on experimental and computational modelling issues related to sequential grouping in auditory scene analysis (auditory pattern detection and bistable perception), as we believe that this is the research topic where predictive coding has the highest potential for advancing our understanding. In addition to specific questions, our analysis led us to identify three more general questions that require further clarification: (1) What exactly is meant by prediction in predictive coding? (2) What governs which generative models make the predictions? and (3) What (if it exists) is the correlate of perceptual experience within the predictive coding framework
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)
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
Advances in reinforcement learning (RL) often rely on massive compute
resources and remain notoriously sample inefficient. In contrast, the human
brain is able to efficiently learn effective control strategies using limited
resources. This raises the question whether insights from neuroscience can be
used to improve current RL methods. Predictive processing is a popular
theoretical framework which maintains that the human brain is actively seeking
to minimize surprise. We show that recurrent neural networks which predict
their own sensory states can be leveraged to minimise surprise, yielding
substantial gains in cumulative reward. Specifically, we present the Predictive
Processing Proximal Policy Optimization (P4O) agent; an actor-critic
reinforcement learning agent that applies predictive processing to a recurrent
variant of the PPO algorithm by integrating a world model in its hidden state.
Even without hyperparameter tuning, P4O significantly outperforms a baseline
recurrent variant of the PPO algorithm on multiple Atari games using a single
GPU. It also outperforms other state-of-the-art agents given the same
wall-clock time and exceeds human gamer performance on multiple games including
Seaquest, which is a particularly challenging environment in the Atari domain.
Altogether, our work underscores how insights from the field of neuroscience
may support the development of more capable and efficient artificial agents.Comment: 24 pages, 8 figure
Echo State Property of Deep Reservoir Computing Networks
In the last years, the Reservoir Computing (RC) framework has emerged as a state of-the-art approach for efficient learning in temporal domains. Recently, within the RC context, deep Echo State Network (ESN) models have been proposed. Being composed of a stack of multiple non-linear reservoir layers, deep ESNs potentially allow to exploit the advantages of a hierarchical temporal feature representation at different levels of abstraction, at the same time preserving the training efficiency typical of the RC methodology. In this paper, we generalize to the case of deep architectures the fundamental RC conditions related to the Echo State Property (ESP), based on the study of stability and contractivity of the resulting dynamical system. Besides providing a necessary condition and a sufficient condition for the ESP of layered RC networks, the results of our analysis provide also insights on the nature of the state dynamics in hierarchically organized recurrent models. In particular, we find out that by adding layers to a deep reservoir architecture, the regime of network’s dynamics can only be driven towards (equally or) less stable behaviors. Moreover, our investigation shows the intrinsic ability of temporal dynamics differentiation at the different levels in a deep recurrent architecture, with higher layers in the stack characterized by less contractive dynamics. Such theoretical insights are further supported by experimental results that show the effect of layering in terms of a progressively increased short-term memory capacity of the recurrent models
PreCNet: Next Frame Video Prediction Based on Predictive Coding
Predictive coding, currently a highly influential theory in neuroscience, has
not been widely adopted in machine learning yet. In this work, we transform the
seminal model of Rao and Ballard (1999) into a modern deep learning framework
while remaining maximally faithful to the original schema. The resulting
network we propose (PreCNet) is tested on a widely used next frame video
prediction benchmark, which consists of images from an urban environment
recorded from a car-mounted camera. On this benchmark (training: 41k images
from KITTI dataset; testing: Caltech Pedestrian dataset), we achieve to our
knowledge the best performance to date when measured with the Structural
Similarity Index (SSIM). Performance on all measures was further improved when
a larger training set (2M images from BDD100k), pointing to the limitations of
the KITTI training set. This work demonstrates that an architecture carefully
based in a neuroscience model, without being explicitly tailored to the task at
hand, can exhibit unprecedented performance