1,968 research outputs found
A Deep Predictive Coding Network for Inferring Hierarchical Causes Underlying Sensory Inputs
Predictive coding has been argued as a mechanism underlying sensory processing in the brain. In computational models of predictive coding, the brain is described as a machine that constructs and continuously adapts a generative model based on the stimuli received from external environment. It uses this model to infer causes that generated the received stimuli. However, it is not clear how predictive coding can be used to construct deep neural network models of the brain while complying with the architectural constraints imposed by the brain. Here, we describe an algorithm to construct a deep generative model that can be used to infer causes behind the stimuli received from external environment. Specifically, we train a deep neural network on real-world images in an unsupervised learning paradigm. To understand the capacity of the network with regards to modeling the external environment, we studied the causes inferred using the trained model on images of objects that are not used in training. Despite the novel features of these objects the model is able to infer the causes for them. Furthermore, the reconstructions of the original images obtained from the generative model using these inferred causes preserve important details of these objects
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Predictive Coding Theories of Cortical Function
Predictive coding is a unifying framework for understanding perception,
action and neocortical organization. In predictive coding, different areas of
the neocortex implement a hierarchical generative model of the world that is
learned from sensory inputs. Cortical circuits are hypothesized to perform
Bayesian inference based on this generative model. Specifically, the
Rao-Ballard hierarchical predictive coding model assumes that the top-down
feedback connections from higher to lower order cortical areas convey
predictions of lower-level activities. The bottom-up, feedforward connections
in turn convey the errors between top-down predictions and actual activities.
These errors are used to correct current estimates of the state of the world
and generate new predictions. Through the objective of minimizing prediction
errors, predictive coding provides a functional explanation for a wide range of
neural responses and many aspects of brain organization
Uncertainty and stress: Why it causes diseases and how it is mastered by the brain
The term 'stress' - coined in 1936 - has many definitions, but until now has lacked a theoretical foundation. Here we present an information-theoretic approach - based on the 'free energy principle' - defining the essence of stress; namely, uncertainty. We address three questions: What is uncertainty? What does it do to us? What are our resources to master it? Mathematically speaking, uncertainty is entropy or 'expected surprise'. The 'free energy principle' rests upon the fact that self-organizing biological agents resist a tendency to disorder and must therefore minimize the entropy of their sensory states. Applied to our everyday life, this means that we feel uncertain, when we anticipate that outcomes will turn out to be something other than expected - and that we are unable to avoid surprise. As all cognitive systems strive to reduce their uncertainty about future outcomes, they face a critical constraint: Reducing uncertainty requires cerebral energy. The characteristic of the vertebrate brain to prioritize its own high energy is captured by the notion of the 'selfish brain'. Accordingly, in times of uncertainty, the selfish brain demands extra energy from the body. If, despite all this, the brain cannot reduce uncertainty, a persistent cerebral energy crisis may develop, burdening the individual by 'allostatic load' that contributes to systemic and brain malfunction (impaired memory, atherogenesis, diabetes and subsequent cardio- and cerebrovascular events). Based on the basic tenet that stress originates from uncertainty, we discuss the strategies our brain uses to avoid surprise and thereby resolve uncertainty
Active inference, communication and hermeneutics
AbstractHermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others β during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions β both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then β in principle β they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa
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
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