14 research outputs found
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Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models
In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli
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Spatio-temporal dependencies in functional connectivity in rodent cortical cultures
Models of functional connectivity in cortical cultures on multi-electrodes arrays may aid in understanding how cognitive pathways form and improve techniques that aim to interface with neuronal systems. To enable research on such models, this study uses both data- and model-driven approaches to determine what dependencies are present in and between functional connectivity networks derived from bursts of extracellularly recorded activity. Properties of excitation in bursts were analysed using correlative techniques to assess the degree of linear dependence and then two parallel techniques were used to assess functional connectivity. Three models presenting increasing levels of spatio-temporal dependency were used to capture the dynamics of individual functional connections and their consistencies were verified using surrogate data. By comparing network-wide properties between model generated networks and functional networks from data, complex interdependencies were revealed. This indicates the persistent co-activation of neuronal pathways in spontaneous bursts, as can be found in whole brain structures
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Neural Stochastic Diffusion Search Network-a theoretical solution to the binding problem
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Attention through self-synchronisation in the spiking neuron stochastic diffusion network
The paper discusses ensemble behaviour in the Spiking Neuron Stochastic Diffusion Network, SNSDN, a novel network exploring biologically plausible information processing based on higher order temporal coding. SNSDN was proposed as an alternative solution to the binding problem [1]. SNSDN operation resembles Stochastic Diffusin on Search, SDS, a non-deterministic search algorithm able to rapidly locate the best instantiation of a target pattern within a noisy search space ([3], [5]). In SNSDN, relevant information is encoded in the length of interspike intervals. Although
every neuron operates in its own time, âattentionâ to a pattern in the search space results in self-synchronised activity of a large population of neurons. When multiple patterns are present in the search space, âswitching of at-
tentionâ results in a change of the synchronous activity. The qualitative effect of attention on the synchronicity of spiking behaviour in both time and frequency domain will be discussed
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Time complexity analysis of the stochastic diffusion search
The Stochastic Diffusion Search algorithm -an integral part of Stochastic Search Networks is
investigated. Stochastic Diffusion Search is an alternative solution for invariant pattern recognition and
focus of attention. It has been shown that the algorithm can be modelled as an ergodic, finite state Markov
Chain under some non-restrictive assumptions. Sub-linear time complexity for some settings of parameters
has been formulated and proved. Some properties of the algorithm are then characterised and numerical
examples illustrating some features of the algorithm are presented
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Low cost brain-computer interface: first results
Brain-Computer Interfacing (BCI) has been previously demonstrated to restore patient communication, meeting with varying degrees of success. Due to the nature of the equipment traditionally used in BCI experimentation (the electroencephalograph) it is mostly conned to clinical and research environments. The required medical safety standards, subsequent cost of equipment and its application/training times are all issues that need to be resolved if BCIs are to be taken out of the lab/clinic and delivered to the home market. The results in this paper demonstrate a system developed with a low cost medical grade EEG amplier unit in conjunction with the open source BCI2000 software suite thus constructing the cheapest
per electrode system available, meeting rigorous clinical safety standards. Discussion of the future of this technology and future work concerning this platform are also introduced
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Application of EMD as a novel technique for the study of tremor time series
This paper introduces the Hilbert Analysis (HA),
which is a novel digital signal processing technique, for the investigation of tremor. The HA is formed by two complementary tools, i.e. the Empirical Mode Decomposition (EMD) and the Hilbert Spectrum (HS). In this work we show that the EMD can automatically detect and isolate tremulous and voluntary movements from experimental signals collected from 31 patients with different conditions. Our results also suggest that the tremor may be described by a new class of mathematical functions defined in the HA framework. In a further study, the HS was employed for visualization of the energy activities of signals. This
tool introduces the concept of instantaneous frequency in the field of tremor. In addition, it could provide, in a time-frequency energy plot, a clear visualization of local activities of tremor energy over the time. The HA demonstrated to be very useful to perform objective measurements of any kind of tremor and can therefore be used to perform functional assessment
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A search for invariant relative satellite motion
This report presents the canonical Hamiltonian formulation of relative satellite motion. The unperturbed Hamiltonian model is shown to be equivalent to the well known Hill-Clohessy-Wilshire (HCW) linear formulation. The in°uence of perturbations of the nonlinear Gravitational potential and the oblateness of the Earth; J2 perturbations are also modelled within the Hamiltonian formulation. The modelling
incorporates eccentricity of the reference orbit. The corresponding Hamiltonian vector ÂŻelds are computed and implemented in Simulink. A numerical method is presented aimed at locating periodic or quasi-periodic relative satellite motion. The numerical method outlined in
this paper is applied to the Hamiltonian system. Although the orbits considered here are weakly unstable at best, in the case of eccentricity only, the method ÂŻnds exact periodic orbits. When other perturbations such as nonlinear gravitational terms are added, drift is signicantly reduced and in the case of the J2 perturbation with and without
the nonlinear gravitational potential term, bounded quasi-periodic solutions are found. Advantages of using Newton's method to search for periodic or quasi-periodic relative satellite motion include simplicity of implementation, repeatability of solutions due to its non-random nature, and fast convergence. Given that the use of bounded or drifting trajectories as control references carries practical di±culties over long-term missions, Principal Component Analysis (PCA) is applied to the quasi-periodic or
slowly drifting trajectories to help provide a closed reference trajectory for the implementation of closed loop control. In order to evaluate the eÂźect of the quality of the model used to generate the periodic reference trajectory, a study involving closed loop control of a simulated master/follower formation was performed.
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The results of the closed loop control study indicate that the quality of the model employed for generating the reference trajectory used for control purposes has an important in°uence on the resulting amount of fuel required to track the reference trajectory. The model used to
generate LQR controller gains also has an eŸect on the e±ciency of the controller