Antoni Oliver

STUDYING THE ROLE OF SYNCHRONIZED AND CHAOTIC SPIKING NEURAL ENSEMBLES IN NEURAL INFORMATION PROCESSING

The brain is characterized by performing many diverse processing tasks ranging from elaborate processes such as pattern recognition, memory or decision making to more simple functionalities such as linear filtering in image processing. Understanding the mechanisms by which the brain is able to produce such a different range of cortical operations remains a fundamental problem in neuroscience. Here we show a study about which processes are related to chaotic and synchronized states based on the study of in-silico implementation of Stochastic Spiking Neural Networks (SSNN). The measurements obtained reveal that chaotic neural ensembles are excellent transmission and convolution systems since mutual information between signals is minimized. At the same time, synchronized cells (that can be understood as ordered states of the brain) can be associated to more complex nonlinear computations. In this sense, we experimentally show that complex and quick pattern recognition processes arise when both synchronized and chaotic states are mixed. These measurements are in accordance with in vivo observations related to the role of neural synchrony in pattern recognition and to the speed of the real biological process. We also suggest that the high-level adaptive mechanisms of the brain that are the Hebbian and non-Hebbian learning rules can be understood as processes devoted to generate the appropriate clustering of both synchronized and chaotic ensembles. The measurements obtained from the hardware implementation of different types of neural systems suggest that the brain processing can be governed by the superposition of these two complementary states with complementary functionalities (nonlinear processing for synchronized states and information convolution and parallelization for chaotic).

Hardware implementation of stochastic spiking neural networks.

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

A New Stochastic Computing Methodology for Efficient Neural Network Implementation

This paper presents a new methodology for the hardware implementation of neural networks (NNs) based on probabilistic laws. The proposed encoding scheme circumvents the limitations of classical stochastic computing (based on unipolar or bipolar encoding) extending the representation range to any real number using the ratio of two bipolar-encoded pulsed signals. Furthermore, the novel approach presents practically a total noise-immunity capability due to its specific codification. We introduce different designs for building the fundamental blocks needed to implement NNs. The validity of the present approach is demonstrated through a regression and a pattern recognition task. The low cost of the methodology in terms of hardware, along with its capacity to implement complex mathematical functions (such as the hyperbolic tangent), allows its use for building highly reliable systems and parallel computing.

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting

Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.

Ultra-Fast Data-Mining Hardware Architecture Based on Stochastic Computing

Minimal hardware implementations able to cope with the processing of large amounts of data in reasonable times are highly desired in our information-driven society. In this work we review the application of stochastic computing to probabilistic-based pattern-recognition analysis of huge database sets. The proposed technique consists in the hardware implementation of a parallel architecture implementing a similarity search of data with respect to different pre-stored categories. We design pulse-based stochastic-logic blocks to obtain an efficient pattern recognition system. The proposed architecture speeds up the screening process of huge databases by a factor of 7 when compared to a conventional digital implementation using the same hardware area.

A Stochastic Spiking Neural Network for Virtual Screening

Virtual screening (VS) has become a key computational tool in early drug design and screening performance is of high relevance due to the large volume of data that must be processed to identify molecules with the sought activity-related pattern. At the same time, the hardware implementations of spiking neural networks (SNNs) arise as an emerging computing technique that can be applied to parallelize processes that normally present a high cost in terms of computing time and power. Consequently, SNN represents an attractive alternative to perform time-consuming processing tasks, such as VS. In this brief, we present a smart stochastic spiking neural architecture that implements the ultrafast shape recognition (USR) algorithm achieving two order of magnitude of speed improvement with respect to USR software implementations. The neural system is implemented in hardware using field-programmable gate arrays allowing a highly parallelized USR implementation. The results show that, due to the high parallelization of the system, millions of compounds can be checked in reasonable times. From these results, we can state that the proposed architecture arises as a feasible methodology to efficiently enhance time-consuming data-mining processes such as 3-D molecular similarity search.

A Second-Screen Meets Hypervideo, Delivering Content Through HbbTV

In this paper two improvements for the Hypervideo platform, used to represent augmented reality on Interactive TVs thanks to the hypervideo concept, are presented: the introduction of a second-screen application to the platform, enabling the user to obtain the additional information on its handheld device and delivering the video track through the broadcast channel, thanks to the HbbTV capability.

Automatic collection of user behavior in 360° multimedia

The arrival of 360° video to the everyday life creates the necessity of assessing both the audiovisual production and the playback environment offered to the final user. Leveraging the standard Experience API (xAPI), that considers collecting micro-interactions with e-learning content, we propose a platform to automatically collect the users’ interaction with applications based on interactive 360° multimedia. To validate the platform, we introduce an example of educational activities based on interactive 360° videos and the tools used to first, annotate these videos and convert them into interactive activities; second, to perform said activity and collect the users’ behavior via xAPI statements; and finally, to convert these statements to meaningful information in the form of user metrics and charts, both at individual level and also aggregated by activity, creating the possibility of finding singular and group behavior. This work concludes that the presented platform helps to analyze how users behave with omnidirectional interactive productions, with the aim of validating and improving its usability, ending with the discussion of future work ideas.

A surface site interaction point methodology for macromolecules and huge molecular databases.

Determining the position and magnitude of Surface Site Interaction Points (SSIP) is a useful technique for understanding intermolecular interactions. SSIPs have been used for the prediction of solvation properties and for virtual co‐crystal screening. To determine the SSIPs for a molecule, the Molecular Electrostatic Potential Surface (MEPS) is first calculated using ab initio methods such as Density Functional Theory. This leads to a high cost in terms of computation time and is not compatible with the analysis of huge molecular databases. Herein, we present a method for the fast estimation of SSIPs, which is based on the MEPS calculated from MMFF94 atomic partial charges. The results show that this method can be used to calculate SSIPs for large molecular databases with a much higher speed than the original ab initio methodology.

Noise-robust hardware implementation of neural networks

Efficient hardware implementations of neural networks are of high interest. Stochastic computing is an alternative to conventional digital logic that allows to exploit the intrinsic parallelism of neural networks using few hardware resources. We present a new stochastic methodology that extends the capabilities of classical stochastic computing. In particular, the present approach exhibits practically total immunity to noise. This is demonstrated evaluating the influence of the noise on the systems performance for a mathematical regression task.