250 research outputs found
Reinforcement Learning on Slow Features of High-Dimensional Input Streams
Humans and animals are able to learn complex behaviors based on a massive stream of sensory information from different modalities. Early animal studies have identified learning mechanisms that are based on reward and punishment such that animals tend to avoid actions that lead to punishment whereas rewarded actions are reinforced. However, most algorithms for reward-based learning are only applicable if the dimensionality of the state-space is sufficiently small or its structure is sufficiently simple. Therefore, the question arises how the problem of learning on high-dimensional data is solved in the brain. In this article, we propose a biologically plausible generic two-stage learning system that can directly be applied to raw high-dimensional input streams. The system is composed of a hierarchical slow feature analysis (SFA) network for preprocessing and a simple neural network on top that is trained based on rewards. We demonstrate by computer simulations that this generic architecture is able to learn quite demanding reinforcement learning tasks on high-dimensional visual input streams in a time that is comparable to the time needed when an explicit highly informative low-dimensional state-space representation is given instead of the high-dimensional visual input. The learning speed of the proposed architecture in a task similar to the Morris water maze task is comparable to that found in experimental studies with rats. This study thus supports the hypothesis that slowness learning is one important unsupervised learning principle utilized in the brain to form efficient state representations for behavioral learning
Towards a learning-theoretic analysis of spike-timing dependent plasticity
This paper suggests a learning-theoretic perspective on how synaptic
plasticity benefits global brain functioning. We introduce a model, the
selectron, that (i) arises as the fast time constant limit of leaky
integrate-and-fire neurons equipped with spiking timing dependent plasticity
(STDP) and (ii) is amenable to theoretical analysis. We show that the selectron
encodes reward estimates into spikes and that an error bound on spikes is
controlled by a spiking margin and the sum of synaptic weights. Moreover, the
efficacy of spikes (their usefulness to other reward maximizing selectrons)
also depends on total synaptic strength. Finally, based on our analysis, we
propose a regularized version of STDP, and show the regularization improves the
robustness of neuronal learning when faced with multiple stimuli.Comment: To appear in Adv. Neural Inf. Proc. System
Single Biological Neurons as Temporally Precise Spatio-Temporal Pattern Recognizers
This PhD thesis is focused on the central idea that single neurons in the
brain should be regarded as temporally precise and highly complex
spatio-temporal pattern recognizers. This is opposed to the prevalent view of
biological neurons as simple and mainly spatial pattern recognizers by most
neuroscientists today. In this thesis, I will attempt to demonstrate that this
is an important distinction, predominantly because the above-mentioned
computational properties of single neurons have far-reaching implications with
respect to the various brain circuits that neurons compose, and on how
information is encoded by neuronal activity in the brain. Namely, that these
particular "low-level" details at the single neuron level have substantial
system-wide ramifications. In the introduction we will highlight the main
components that comprise a neural microcircuit that can perform useful
computations and illustrate the inter-dependence of these components from a
system perspective. In chapter 1 we discuss the great complexity of the
spatio-temporal input-output relationship of cortical neurons that are the
result of morphological structure and biophysical properties of the neuron. In
chapter 2 we demonstrate that single neurons can generate temporally precise
output patterns in response to specific spatio-temporal input patterns with a
very simple biologically plausible learning rule. In chapter 3, we use the
differentiable deep network analog of a realistic cortical neuron as a tool to
approximate the gradient of the output of the neuron with respect to its input
and use this capability in an attempt to teach the neuron to perform nonlinear
XOR operation. In chapter 4 we expand chapter 3 to describe extension of our
ideas to neuronal networks composed of many realistic biological spiking
neurons that represent either small microcircuits or entire brain regions
Independent Component Analysis in Spiking Neurons
Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition
Accelerating Pattern Recognition Algorithms On Parallel Computing Architectures
The move to more parallel computing architectures places more responsibility on the programmer to achieve greater performance. The programmer must now have a greater understanding of the underlying architecture and the inherent algorithmic parallelism. Using parallel computing architectures for exploiting algorithmic parallelism can be a complex task. This dissertation demonstrates various techniques for using parallel computing architectures to exploit algorithmic parallelism. Specifically, three pattern recognition (PR) approaches are examined for acceleration across multiple parallel computing architectures, namely field programmable gate arrays (FPGAs) and general purpose graphical processing units (GPGPUs). Phase-only filter correlation for fingerprint identification was studied as the first PR approach. This approach\u27s sensitivity to angular rotations, scaling, and missing data was surveyed. Additionally, a novel FPGA implementation of this algorithm was created using fixed point computations, deep pipelining, and four computation phases. Communication and computation were overlapped to efficiently process large fingerprint galleries. The FPGA implementation showed approximately a 47 times speedup over a central processing unit (CPU) implementation with negligible impact on precision. For the second PR approach, a spiking neural network (SNN) algorithm for a character recognition application was examined. A novel FPGA implementation of the approach was developed incorporating a scalable modular SNN processing element (PE) to efficiently perform neural computations. The modular SNN PE incorporated streaming memory, fixed point computation, and deep pipelining. This design showed speedups of approximately 3.3 and 8.5 times over CPU implementations for 624 and 9,264 sized neural networks, respectively. Results indicate that the PE design could scale to process larger sized networks easily. Finally for the third PR approach, cellular simultaneous recurrent networks (CSRNs) were investigated for GPGPU acceleration. Particularly, the applications of maze traversal and face recognition were studied. Novel GPGPU implementations were developed employing varying quantities of task-level, data-level, and instruction-level parallelism to achieve efficient runtime performance. Furthermore, the performance of the face recognition application was examined across a heterogeneous cluster of multi-core and GPGPU architectures. A combination of multi-core processors and GPGPUs achieved roughly a 996 times speedup over a single-core CPU implementation. From examining these PR approaches for acceleration, this dissertation presents useful techniques and insight applicable to other algorithms to improve performance when designing a parallel implementation
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
Neurally Plausible Model of Robot Reaching Inspired by Infant Motor Babbling
In this dissertation, we present an abstract model of infant reaching that is neurally-plausible. This model is grounded in embodied artificial intelligence, which emphasizes the importance of the sensorimotor interaction of an agent and the world. It includes both learning sensorimotor correlations through motor babbling and also arm motion planning using spreading activation. We introduce a mechanism called bundle formation as a way to generalize motions during the motor babbling stage.
We then offer a neural model for the abstract model, which is composed of three layers of neural maps with parallel structures representing the same sensorimotor space. The motor babbling period shapes the structure of the three neural maps as well as the connections within and between them; these connections encode trajectory bundles in the neural maps.
We then investigate an implementation of the neural model using a reaching task on a humanoid robot. Through a set of experiments, we were able to find the best way to implement different components of this model such as motor babbling, neural representation of sensorimotor space, dimension reduction, path planning, and path execution.
After the proper implementation had been found, we conducted another set of experiments to analyze the model and evaluate the planned motions. We evaluated unseen reaching motions using jerk, end effector error, and overshooting. In these experiments, we studied the effect of different dimensionalities of the reduced sensorimotor space, different bundle widths, and different bundle structures on the quality of arm motions.
We hypothesized a larger bundle width would allow the model to generalize better. The results confirmed that the larger bundles lead to a smaller error of end-effector position for testing targets. An experiment with the resolution of neural maps showed that a neural map with a coarse resolution produces less smooth motions compared to a neural map with a fine resolution. We also compared the unseen reaching motions under different dimensionalities of the reduced sensorimotor space. The results showed that a smaller dimension leads to less smooth and accurate movements
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