5 research outputs found
ACCURACY AND MULTI-CORE PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR HANDWRITTEN CHARACTER RECOGNITION
There have been considerable developments in the quest for intelligent machines since the beginning of the cybernetics revolution and the advent of computers. In the last two decades with the onset of the internet the developments have been extensive. This quest for building intelligent machines have led into research on the working of human brain, which has in turn led to the development of pattern recognition models which take inspiration in their structure and performance from biological neural networks. Research in creating intelligent systems poses two main problems. The first one is to develop algorithms which can generalize and predict accurately based on previous examples. The second one is to make these algorithms run fast enough to be able to do real time tasks. The aim of this thesis is to study and compare the accuracy and multi-core performance of some of the best learning algorithms to the task of handwritten character recognition. Seven algorithms are compared for their accuracy on the MNIST database, and the test set accuracy (generalization) for the different algorithms are compared. The second task is to implement and compare the performance of two of the hierarchical Bayesian based cortical algorithms, Hierarchical Temporal Memory (HTM) and Hierarchical Expectation Refinement Algorithm (HERA) on multi-core architectures. The results indicate that the HTM and HERA algorithms can make use of the parallelism in multi-core architectures
Deep Boltzmann Machines as Hierarchical Generative Models of Perceptual Inference in the Cortex
The mammalian neocortex is integral to all aspects of cognition, in particular perception
across all sensory modalities. Whether computational principles can be identified that
would explain why the cortex is so versatile and capable of adapting to various inputs
is not clear. One well-known hypothesis is that the cortex implements a generative
model, actively synthesising internal explanations of the sensory input. This ‘analysis
by synthesis’ could be instantiated in the top-down connections in the hierarchy of
cortical regions, and allow the cortex to evaluate its internal model and thus learn good
representations of sensory input over time. Few computational models however exist
that implement these principles.
In this thesis, we investigate the deep Boltzmann machine (DBM) as a model of
analysis by synthesis in the cortex, and demonstrate how three distinct perceptual phenomena
can be interpreted in this light: visual hallucinations, bistable perception, and
object-based attention. A common thread is that in all cases, the internally synthesised
explanations go beyond, or deviate from, what is in the visual input. The DBM was
recently introduced in machine learning, but combines several properties of interest
for biological application. It constitutes a hierarchical generative model and carries
both the semantics of a connectionist neural network and a probabilistic model. Thus,
we can consider neuronal mechanisms but also (approximate) probabilistic inference,
which has been proposed to underlie cortical processing, and contribute to the ongoing
discussion concerning probabilistic or Bayesian models of cognition.
Concretely, making use of the model’s capability to synthesise internal representations
of sensory input, we model complex visual hallucinations resulting from loss of
vision in Charles Bonnet syndrome.We demonstrate that homeostatic regulation of neuronal
firing could be the underlying cause, reproduce various aspects of the syndrome,
and examine a role for the neuromodulator acetylcholine. Next, we relate bistable perception
to approximate, sampling-based probabilistic inference, and show how neuronal
adaptation can be incorporated by providing a biological interpretation for a recently
developed sampling algorithm. Finally, we explore how analysis by synthesis could be
related to attentional feedback processing, employing the generative aspect of the DBM
to implement a form of object-based attention.
We thus present a model that uniquely combines several computational principles
(sampling, neural processing, unsupervised learning) and is general enough to uniquely
address a range of distinct perceptual phenomena. The connection to machine learning
ensures theoretical grounding and practical evaluation of the underlying principles. Our
results lend further credence to the hypothesis of a generative model in the brain, and
promise fruitful interaction between neuroscience and Deep Learning approaches
Learning invariant features using inertial priors
We address the technical challenges involved in combining key features from several theories of the visual cortex in a single coherent model. The resulting model is a hierarchical Bayesian network factored into modular component networks embedding variable-order Markov models. Each component network has an associated receptive field corresponding to components residing in the level directly below it in the hierarchy. The variable-order Markov models account for features that are invariant to naturally occurring transformations in their inputs. These invariant features give rise to increasingly stable, persistent representations as we ascend the hierarchy. The receptive fields of proximate components on the same level overlap to restore selectivity that might otherwise be lost to invariance