28 research outputs found
Predictive Coding as a Model of Biased Competition in Visual Attention
Attention acts, through cortical feedback pathways, to enhance the response of cells encoding expected or predicted information. Such observations are inconsistent with the predictive coding theory of cortical function which proposes that feedback acts to suppress information predicted by higher-level cortical regions. Despite this discrepancy, this article demonstrates that the predictive coding model can be used to simulate a number of the effects of attention. This is achieved via a simple mathematical rearrangement of the predictive coding model, which allows it to be interpreted as a form of biased competition model. Nonlinear extensions to the model are proposed that enable it to explain a wider range of data
Learning image components for object recognition
In order to perform object recognition it is necessary to learn representations of the underlying components of images. Such components correspond to objects, object-parts, or features. Non-negative matrix factorisation is a generative model that has been specifically proposed for finding such meaningful representations of image data, through the use of non-negativity constraints on the factors. This article reports on an empirical investigation of the performance of non-negative matrix factorisation algorithms. It is found that such algorithms need to impose additional constraints on the sparseness of the factors in order to successfully deal with occlusion. However, these constraints can themselves result in these algorithms failing to identify image components under certain conditions. In contrast, a recognition model (a competitive learning neural network algorithm) reliably and accurately learns representations of elementary image features without such constraints
A feedback model of perceptual learning and categorisation
Top-down, feedback, influences are known to have significant effects on visual information processing. Such influences are also likely to affect perceptual learning. This article employs a computational model of the cortical region interactions underlying visual perception to investigate possible influences of top-down information on learning. The results suggest that feedback could bias the way in which perceptual stimuli are categorised and could also facilitate the learning of sub-ordinate level representations suitable for object identification and perceptual expertise
Comprehensive Assessment of the Performance of Deep Learning Classifiers Reveals a Surprising Lack of Robustness
Reliable and robust evaluation methods are a necessary first step towards
developing machine learning models that are themselves robust and reliable.
Unfortunately, current evaluation protocols typically used to assess
classifiers fail to comprehensively evaluate performance as they tend to rely
on limited types of test data, and ignore others. For example, using the
standard test data fails to evaluate the predictions made by the classifier to
samples from classes it was not trained on. On the other hand, testing with
data containing samples from unknown classes fails to evaluate how well the
classifier can predict the labels for known classes. This article advocates
bench-marking performance using a wide range of different types of data and
using a single metric that can be applied to all such data types to produce a
consistent evaluation of performance. Using such a benchmark it is found that
current deep neural networks, including those trained with methods that are
believed to produce state-of-the-art robustness, are extremely vulnerable to
making mistakes on certain types of data. This means that such models will be
unreliable in real-world scenarios where they may encounter data from many
different domains, and that they are insecure as they can easily be fooled into
making the wrong decisions. It is hoped that these results will motivate the
wider adoption of more comprehensive testing methods that will, in turn, lead
to the development of more robust machine learning methods in the future.
Code is available at:
\url{https://codeberg.org/mwspratling/RobustnessEvaluation
Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical Function
The combination of two or more population-coded signals in a neural model of pre-dictive coding can give rise to multiplicative gain modulation in the response properties of individual neurons. Synaptic weights generating these multiplicative response properties can be learned using an unsupervised, Hebbian, learning rule. The behaviour of the model is compared to empirical data on gaze-dependent gain modulation of cortical cells, and found to be in good agreement with a range of physiological observations. Furthermore, it is demonstrated that the model can learn to represent a set of basis functions. The current paper thus connects an often-observed neurophysiological phenomenon and important neu-rocomputational principle (gain modulation) with an influential theory of brain operation (predictive coding).
A model of partial reference frame transforms through pooling of gain-modulated responses
In multimodal integration and sensorimotor transformation areas of the posterior parietal cortex (PPC), neural responses often appear encoded in spatial reference frames that are intermediate to the in-trinsic sensory reference frames, for example, eye-centered for visual or head-centered for auditory stimulation. Many sensory responses in these areas are also modulated by direction of gaze. We demonstrate that certain types of mixed-frame responses can be generated by pooling gain-modulated responses—similar to how complex cells in the visual cortex are thought to pool the responses of simple cells. The proposed model simulates 2 types of mixed-frame responses observed in the PPC: in particular, sensory responses that shift differentially with gaze in horizontal and verti-cal dimensions and sensory responses that shift differentially for different start and end points along a single dimension of gaze. We distinguish these 2 types of mixed-frame responses from a third type in which sensory responses shift a partial yet approximately equal amount with each gaze shift. We argue that the empirical data on mixed-frame responses may be caused by multiple mechan-isms, and we adapt existing reference-frame measures to dis-tinguish between the different types. Finally, we discuss how mixed-frame responses may be revealing of the local organization of presynaptic responses
A model of non-linear interactions between cortical top-down and horizontal connections explains the attentional gating of collinear facilitation
AbstractPast physiological and psychophysical experiments have shown that attention can modulate the effects of contextual information appearing outside the classical receptive field of a cortical neuron. Specifically, it has been suggested that attention, operating via cortical feedback connections, gates the effects of long-range horizontal connections underlying collinear facilitation in cortical area V1. This article proposes a novel mechanism, based on the computations performed within the dendrites of cortical pyramidal cells, that can account for these observations. Furthermore, it is shown that the top-down gating signal into V1 can result from a process of biased competition occurring in extrastriate cortex. A model based on these two assumptions is used to replicate the results of physiological and psychophysical experiments on collinear facilitation and attentional modulation
Distinguishing theory from implementation in predictive coding accounts of brain function
AbstractIt is often helpful to distinguish between a theory (Marr's computational level) and a specific implementation of that theory (Marr's physical level). However, in the target article, a single implementation of predictive coding is presented as if this were the theory of predictive coding itself. Other implementations of predictive coding have been formulated which can explain additional neurobiological phenomena.</jats:p