5,160 research outputs found
Spiking Neural P Systems with Addition/Subtraction Computing on Synapses
Spiking neural P systems (SN P systems, for short) are a class of distributed
and parallel computing models inspired from biological spiking neurons. In this paper,
we introduce a variant called SN P systems with addition/subtraction computing on
synapses (CSSN P systems). CSSN P systems are inspired and motivated by the shunting
inhibition of biological synapses, while incorporating ideas from dynamic graphs and
networks. We consider addition and subtraction operations on synapses, and prove that
CSSN P systems are computationally universal as number generators, under a normal
form (i.e. a simplifying set of restrictions)
Simulating FRSN P Systems with Real Numbers in P-Lingua on sequential and CUDA platforms
Fuzzy Reasoning Spiking Neural P systems (FRSN P systems,
for short) is a variant of Spiking Neural P systems incorporating
fuzzy logic elements that make it suitable to model fuzzy diagnosis knowledge
and reasoning required for fault diagnosis applications. In this sense,
several FRSN P system variants have been proposed, dealing with real
numbers, trapezoidal numbers, weights, etc. The model incorporating
real numbers was the first introduced [13], presenting promising applications
in the field of fault diagnosis of electrical systems. For this variant,
a matrix-based algorithm was provided which, when executed on parallel
computing platforms, fully exploits the model maximally parallel
capacities. In this paper we introduce a P-Lingua framework extension
to parse and simulate FRSN P systems with real numbers. Two simulators,
implementing a variant of the original matrix-based simulation
algorithm, are provided: a sequential one (written in Java), intended to
run on traditional CPUs, and a parallel one, intended to run on CUDAenabled
devices.Ministerio de Economía y Competitividad TIN2012-3743
A unified approach to linking experimental, statistical and computational analysis of spike train data
A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.Published versio
Spiking Neural P Systems with Structural Plasticity: Attacking the Subset Sum Problem
Spiking neural P systems with structural plasticity (in short,
SNPSP systems) are models of computations inspired by the function and
structure of biological neurons. In SNPSP systems, neurons can create
or delete synapses using plasticity rules. We report two families of solutions:
a non-uniform and a uniform one, to the NP-complete problem
Subset Sum using SNPSP systems. Instead of the usual rule-level nondeterminism
(choosing which rule to apply) we use synapse-level nondeterminism
(choosing which synapses to create or delete). The nondeterminism
due to plasticity rules have the following improvements from a
previous solution: in our non-uniform solution, plasticity rules allowed
for a normal form to be used (i.e. without forgetting rules or rules with
delays, system is simple, only synapse-level nondeterminism); in our uniform
solution the number of neurons and the computation steps are
reduced.Ministerio de Economía y Competitividad TIN2012-3743
Dynamical principles in neuroscience
Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA
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