Neural Information Processing: between synchrony and chaos

The brain is characterized by performing many different 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. Some recent empirical and theoretical results support the notion that the brain is naturally poised between ordered and chaotic states. As the largest number of metastable states exists at a point near the transition, the brain therefore has access to a larger repertoire of behaviours. Consequently, it is of high interest to know which type of processing can be associated with both ordered and disordered states. Here we show an explanation of which processes are related to chaotic and synchronized states based on the study of in-silico implementation of biologically plausible neural systems. The measurements obtained reveal that synchronized cells (that can be understood as ordered states of the brain) are related to non-linear computations, while uncorrelated neural ensembles are excellent information transmission systems that are able to implement linear transformations (as the realization of convolution products) and to parallelize neural processes. From these results we propose a plausible meaning for Hebbian and non-Hebbian learning rules as those biophysical mechanisms by which the brain creates ordered or chaotic ensembles depending on the desired functionality. The measurements that we obtain from the hardware implementation of different neural systems endorse the fact that the brain is working with two different states, ordered and chaotic, with complementary functionalities that imply non-linear processing (synchronized states) and information transmission and convolution (chaotic states)

Stochastic-Based Pattern Recognition Analysis

In this work we review the basic principles of stochastic logic and propose its application to probabilistic-based pattern-recognition analysis. The proposed technique is intrinsically a parallel comparison of input data to various pre-stored categories using Bayesian techniques. We design smart pulse-based stochastic-logic blocks to provide an efficient pattern recognition analysis. The proposed rchitecture is applied to a specific navigation problem. The resulting system is orders of magnitude faster than processor-based solutions

A community-based geological reconstruction of Antarctic Ice Sheet deglaciation since the Last Glacial Maximum

A robust understanding of Antarctic Ice Sheet deglacial history since the Last Glacial Maximum is important in order to constrain ice sheet and glacial-isostatic adjustment models, and to explore the forcing mechanisms responsible for ice sheet retreat. Such understanding can be derived from a broad range of geological and glaciological datasets and recent decades have seen an upsurge in such data gathering around the continent and Sub-Antarctic islands. Here, we report a new synthesis of those datasets, based on an accompanying series of reviews of the geological data, organised by sector. We present a series of timeslice maps for 20ka, 15ka, 10ka and 5ka, including grounding line position and ice sheet thickness changes, along with a clear assessment of levels of confidence. The reconstruction shows that the Antarctic Ice sheet did not everywhere reach the continental shelf edge at its maximum, that initial retreat was asynchronous, and that the spatial pattern of deglaciation was highly variable, particularly on the inner shelf. The deglacial reconstruction is consistent with a moderate overall excess ice volume and with a relatively small Antarctic contribution to meltwater pulse 1a. We discuss key areas of uncertainty both around the continent and by time interval, and we highlight potential priorit. © 2014 The Authors

Coralline Algae in a Changing Mediterranean Sea: How Can We Predict Their Future, if We Do Not Know Their Present?

In this review we assess the state of knowledge for the coralline algae of the Mediterranean Sea, a group of calcareous seaweeds imperfectly known and considered highly vulnerable to long-term climate change. Corallines have occurred in the Mediterranean area for ∼140 My and are well-represented in the subsequent fossil record; for some species currently common the fossil documentation dates back to the Oligocene, with a major role in the sedimentary record of some areas. Some Mediterranean corallines are key ecosystem engineers that produce or consolidate biogenic habitats (e.g., coralligenous concretions, Lithophyllum byssoides rims, rims of articulated corallines, maerl/rhodolith beds). Although bioconstructions built by corallines exist virtually in every sea, in the Mediterranean they reach a particularly high spatial and bathymetric extent (coralligenous concretions alone are estimated to exceed 2,700 km2 in surface). Overall, composition, dynamics and responses to human disturbances of coralline-dominated communities have been well-studied; except for a few species, however, the biology of Mediterranean corallines is poorly known. In terms of diversity, 60 species of corallines are currently reported from the Mediterranean. This number, however, is based on morphological assessments and recent studies incorporating molecular data suggest that the correct estimate is probably much higher. The responses of Mediterranean corallines to climate change have been the subject of several recent studies that documented their tolerance/sensitivity to elevated temperatures and pCO2. These investigations have focused on a few species and should be extended to a wider taxonomic set

FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting