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)

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

<div><p>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.</p></div

Ultra-Fast Data-Mining Hardware Architecture Based on Stochastic Computing - Fig 3

<p>(a) Binary to pulse converter (B2P), an LFSR and a comparator are combined to obtain the pulsed signal. (b) Linear Feedback Shift Register (LFSR) used in the experiments.</p

Variation of query identification probability with respect to similarity.

<p>An arbitrary threshold of <i>s</i>_<i>min</i> = <i>0</i>.<i>2</i> is selected.</p

Number of positive identifications from a database with 2.56<i>•</i>10⁶ particles when <i>s</i>_<i>min</i> = 0.2.

<p>Number of positive identifications from a database with 2.56<i>•</i>10⁶ particles when <i>s</i>_<i>min</i> = 0.2.</p

Architecture used to estimate the closest category to the reference vector 'r'.

<p>Each comparator (CP) provides at its output a switching signal proportional to the similarity of the two vectors connected to it. The Winner-Take All selects the highest frequency signal.</p

Relationship between the k value and the precision of the system when fz = 1 and using smin = 0.5 (i.e. N = 2k).

<p>Relationship between the k value and the precision of the system when fz = 1 and using smin = 0.5 (i.e. N = 2k).</p

Level curves for the similarity metric used for the case of a two-dimensional space.

<p>Level curves for the similarity metric used for the case of a two-dimensional space.</p