Membrane computing and brain modelling

We briefly discuss variants of (extended) spiking neural P systems that combine features from the areas of membrane computing and spiking neurons. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Action Recognition Using a Bio-Inspired Feedforward Spiking Network

We propose a bio-inspired feedforward spiking network modeling two brain areas dedicated to motion (V1 and MT), and we show how the spiking output can be exploited in a computer vision application: action recognition. In order to analyze spike trains, we consider two characteristics of the neural code: mean firing rate of each neuron and synchrony between neurons. Interestingly, we show that they carry some relevant information for the action recognition application. We compare our results to Jhuang et al. (Proceedings of the 11th international conference on computer vision, pp. 1–8, 2007) on the Weizmann database. As a conclusion, we are convinced that spiking networks represent a powerful alternative framework for real vision applications that will benefit from recent advances in computational neuroscience.     

On spiking neural P systems and partially blind counter machines

A k-output spiking neural P system (SNP) with output neurons, , generates a tuple of positive integers if, starting from the initial configuration, there is a sequence of steps such that during the computation, each O _i generates exactly two spikes aa (the times the pair aa are generated may be different for different output neurons) and the time interval between the first a and the second a is n _i . After the output neurons generate their pairs of spikes, the system eventually halts. We give characterizations of sets definable by partially blind multicounter machines in terms of k-output SNPs operating in a sequential mode. Slight variations of the models make them universal.     

Experiments on the reliability of stochastic spiking neural P systems

In the area of membrane computing, time-freeness has been defined as the ability for a timed membrane system to produce always the same result, independently of the execution times associated to the rules. In this paper, we use a similar idea in the framework of spiking neural P systems, a model inspired by the structure and the functioning of neural cells. In particular, we introduce stochastic spiking neural P systems where the time of firing for an enabled spiking rule is probabilistically chosen and we investigate when, and how, these probabilities can influence the ability of the systems to simulate, in a reliable way, universal machines, such as register machines.     

Integrated neural and robotic simulations. Simulation of cerebellar neurobiological substrate for an object-oriented dynamic model abstraction process

Experimental studies of the Central Nervous System (CNS) at multiple organization levels aim at understanding how information is represented and processed by the brain’s neurobiological substrate. The information processed within different neural subsystems is neurocomputed using distributed and dynamic patterns of neural activity. These emerging patterns can be hardly understood by merely taking into account individual cell activities. Studying how these patterns are elicited in the CNS under specific behavioral tasks has become a groundbreaking research topic in system neuroscience. This methodology of synthetic behavioral experimentation is also motivated by the concept of embodied neuroscience, according to which the primary goal of the CNS is to solve/facilitate the body–environment interaction.With the aim to bridge the gap between system neuroscience and biological control, this paper presents how the CNS neural structures can be connected/integrated within a body agent; in particular, an efficient neural simulator based on EDLUT (Ros et al., 2006) has been integrated within a simulated robotic environment to facilitate the implementation of object manipulating closed loop experiments (action–perception loop). This kind of experiment allows the study of the neural abstraction process of dynamic models that occurs within our neural structures when manipulating objects.The neural simulator, communication interfaces, and a robot platform have been efficiently integrated enabling real time simulations. The cerebellum is thought to play a crucial role in human-body interaction with a primary function related to motor control which makes it the perfect candidate to start building an embodied nervous system as illustrated in the simulations performed in this work.     

Advances in Design and Application of Spiking Neural Networks

This paper presents new findings in the design and application of biologically plausible neural networks based on spiking neuron models, which represent a more plausible model of real biological neurons where time is considered as an important feature for information encoding and processing in the brain. The design approach consists of an evolutionary strategy based supervised training algorithm, newly developed by the authors, and the use of different biologically plausible neuronal models. A dynamic synapse (DS) based neuron model, a biologically more detailed model, and the spike response model (SRM) are investigated in order to demonstrate the efficacy of the proposed approach and to further our understanding of the computing capabilities of the nervous system. Unlike the conventional synapse, represented as a static entity with a fixed weight, employed in conventional and SRM-based neural networks, a DS is weightless and its strength changes upon the arrival of incoming input spikes. Therefore its efficacy depends on the temporal structure of the impinging spike trains. In the proposed approach, the training of the network free parameters is achieved using an evolutionary strategy where, instead of binary encoding, real values are used to encode the static and DS parameters which underlie the learning process. The results show that spiking neural networks based on both types of synapse are capable of learning non-linearly separable data by means of spatio-temporal encoding. Furthermore, a comparison of the obtained performance with classical neural networks (multi-layer perceptrons) is presented.     

On the power of elementary features in spiking neural P systems

Since their first publication in 2006, spiking neural (SN) P systems have already attracted the attention of a lot of researchers. This might be owing to the fact that this abstract computing device follows basic principles known from spiking neural nets, but its implementation is discrete, using membrane computing background. Among the elementary properties which confer SN P systems their computational power one can count the unbounded fan-in (indegree) and fan-out (outdegree) of each “neuron”, synchronicity of the whole system, the possibility of delaying and/or removing spikes in neurons, the capability of evaluating arbitrary regular expressions in neurons in constant time and some others. In this paper we focus on the power of these elementary features. Particularly, we study the power of the model when some of these features are disabled. Rather surprisingly, even very restricted SN P systems keep their universal computational power. Certain important questions regarding this topic still remain open.     

Characterizations of some classes of spiking neural P systems

We look at the recently introduced neural-like systems, called SN P systems. These systems incorporate the ideas of spiking neurons into membrane computing. We study various classes and characterize their computing power and complexity. In particular, we analyze asynchronous and sequential SN P systems and present some conditions under which they become (non-)universal. The non-universal variants are characterized by monotonic counter machines and partially blind counter machines and, hence, have many decidable properties. We also investigate the language-generating capability of SN P systems.     

A biophysical implementation of a bidirectional graph search algorithm to solve multiple goal navigation tasks

The model presented here extends formal analysis (Hasselmo et al., Neural Networks, 15, pp. 689-707, 2002b) and abstract modelling (Gorchetchnikov and Hasselmo, Neurocomputing, 44-46, pp. 423-427, 2002a) of interactions within the hippocampal area (or other cortical areas), which can be flexibly used to navigate toward any arbitrary goal or multiple goals that change on a trial-by-trial basis. The algorithm is a version of a bidirectional breadth-first graph search implemented in simulated neurons using two flows of neural activity. The new model changes the continuous firing rate neuronal representations (Gorchetchnikov and Hasselmo 2002a) to more detailed compartmental versions with realistic parameters, while preserving the qualitative properties analysed previously (Hasselmo et al., 2002b, Gorchetchnikov and Hasselmo 2002a). The case of multiple goals being present in the environment is studied in this paper. The first set of simulations tests the algorithm in the selection of the closest goal. A small difference in distance between the simulated animal and different goals is sufficient for a correct selection. The second set of simulations studies the behaviour of the model when the goals have different saliences. A small salience-based difference between firing rates of the cells providing goal-related input to the model is sufficient for the selection of a more salient goal. This behaviour was tested in three types of environments: a linear track, a T-maze and an open field. Further investigation of quantitative properties of the model should allow it to handle cases when the exact location of the goal is uncertain.