Neutral coding - A report based on an NRP work session

Neural coding by impulses and trains on single and multiple channels, and representation of information in nonimpulse carrier

Simulation of Abnormal/Normal Brain States Using the KIV Model

Recent studies have focused on the phenomena of abnormal electrical brain activity which may transition into a debilitating seizure state through the entrainment of large populations of neurons.Starting from the initial epileptogenisis of a small population of abnormally firing neurons, to the mobilization of mesoscopic neuron populations behaving in a synchronous manner, a model has been formulated that captures the initial epileptogenisis to the semi-periodic entrainment of distant neuron populations.The normal non-linear dynamic signal captured through EEG, moves into a semi-periodic state, which can be quantified as the seizure state.Capturing the asynchronous/synchronous behavior of the normal/pathological brain state will be discussed.This model will also demonstrate how electrical stimulation applied to the limbic system restores the seizure state of the brain back to its original normal condition.Human brain states are modeled using a biologically inspired neural network, the KIV model.The KIV model exhibits the noisy, chaotic attributes found in the limbic system of brains of higher forms of organisms, and in its normal basal state, represents the homogeneous activity of millions of neuron activations.The KIV can exhibit the ’unbalanced state’ of neural activity, whereas when a small cluster of abnormal firing neurons starts to exhibit periodic neural firings that eventually entrain all the neurons within the limbic system, the network has moved into the ‘seizure’ state.These attributes have been found in human EEG recordings and have been duplicated in this model of the brain.The discussion in this dissertation covers the attributes found in human EEG data and models these attributes.Additionally, this model proposes a methodology to restore the modeled ‘seizure’ state, and by doing so, proposes a manner for external electrical titration to restore the abnormal seizure state back to a normal chaotic EEG signal state.Quantification measurements of normal, abnormal, and restoration to normal brain states will be exhibited using the following approaches:Analysis of human EEG dataQuantification measurements of brain states.Development of models of the different brain states, i.e. fit parameters of the model on individual personal data/history.Implementation of quantitative measurements on “restored” simulated seizure state

Pulsatile electrical stimulation of auditory nerve fibres : a modelling approach

A stochastic leaky integrate-and-fire nerve model with a dynamical threshold (LIFDT) has been derived for the neural response to sinusoidal electrical stimulation. The LIFDT model incorporates both the refractory effects and the accommodation effects in the interpulse interactions. In this thesis, this phenomenological nerve model is extended for the neural response to pulsatile electrical stimulation, which is widely used in cochlear implants as it reduces inter channel interference. Neurophysiological data from adult guinea pigs were fitted to the LIFDT model. First, the parameters were constrained by the Input/output (I/O) curve analysis. Analysis of the data showed strong accommodation effects. The figures of I/O function for each pulse were plotted according to the physiological data. Fitting the I/O function of the data constrained the value of four variables of LIFDT model. The other five parameters were “optimised by eye”. Although the LIFDT is built with stimulus-dependent threshold, the response of short duration biphasic pulsatile stimuli exhibits weak accommodation effects. Then, in order to avoid the complication of full optimization, analytical approximation of the LIFDT model was derived for pulsatile electrical stimulation. It improves computational efficiency and provides information on how the parameters of the LIFDT model affect the accommodation effects. Theoretical predictions indicate that the LIFDT model could not capture the strong accommodation effects in the neurophysiological data due to structural problems. Alternatively, a Markov renewal process model was utilized to track the pulsetrain response. The stationary and non-stationary Markov renewal process models were fitted to the neurophysiological data. Both models can interpret the conventional PST histograms into conditional probabilities, which are directly related to the interpulse intervals. The consistent results from those two models provide a qualitative analysis of the accommodation characteristics

A Kalman filter model for signal estimation in the auditory system

Using a Kalman filter that contains a forward-predictive model of a relevant system, to predict the states of that system by means of an analysis-by-synthesis implementation in order to evade significant time delays incurred by feedback mechanisms was previously applied to the coordinated movement of limbs by means of the cerebellum. In this dissertation, the same concept was applied to the auditory system in order to investigate if such a concept is a universal neurophysiological method for correctly estimating a state in a quick and reliable way. To test this assumption an auditory system model and Kalman estimator were designed, where the Kalman filter contained a stochastically equivalent forward-predictive model of the complete auditory system model. The Kalman filter was used to estimate the power found in a particular band of the frequency spectrum and its performance in the mean-squared error sense was compared to that of a simple postsynaptic current decoding filter under various types of neural channel noise. It was shown that the Kalman filter, containing a biologically plausible internal model could estimate the power better than a postsynaptic current decoding filter, proposed in the literature. When the just-noticeable difference in intensity discrimination, as reported in the literature, was compared to model-predictions, it was shown that a smaller mean-squared error results in the case of the designed auditory system model and Kalman estimator. This suggests that the application of the Kalman filter concept is important as it provides a bridge between measured data and the auditory system model. It was concluded that a Kalman filter model containing a biologically plausible internal model can explain some characteristics of the signal processing of the auditory system. The research suggests that the principle of an estimator that contains an internal model could be a universal neurophysiological method for the correct estimation of a desired state.Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2006.Electrical, Electronic and Computer Engineeringunrestricte

NEUROMORPHIC VLSI REALIZATION OF THE HIPPOCAMPAL FORMATION AND THE LATERAL SUPERIOR OLIVE

In this work, the focus is on realizing the function of the hippocampal formation (HF) and the lateral superior olive (LSO) in electronic circuits. The first major contribution of this dissertation is to realize the function of the HF in silicon. This was based on the GRIDSmap model and the Bayesian integration. For this, two novel circuits were designed and integrated with others. The first circuit was that of a Bayesian integration synapse which can perform Bayesian integration at the single neuron level. The second circuit was that of a velocity integrator which is so compact that it can enable integration of the entire system on a single chip compared to its predecessors which would have needed 27 chips! However, since the computational neuroscience models of the hippocampal place cells do not explain all the characteristics observed empirically, a novel model for the place cells, based on the sensori-motor integration of inputs is proposed. This is the second major contribution of this thesis. The third major contribution is to demonstrate a VLSI system which can perform azimuthal localization based on population response of the LSO. This system was based on the Reed and Blum's model of the LSO. For this, a novel circuit of a second order synapse and that of a conductance neuron was designed and integrated with other circuits. This synapse circuit can produce an output current whose peak is delayed and is proportional to the number of inputs it receives. The HF is thought to aid in spatial navigation and the LSO is thought to be involved in azimuthal localization of sounds both of which are useful for autonomous robotic spatial navigation. Hence, silicon realization of these two will be useful in robotics which is an area of interest for the neuromorphic engineers

Dynamics of embodied dissociated cortical cultures for the control of hybrid biological robots.

The thesis presents a new paradigm for studying the importance of interactions between an organism and its environment using a combination of biology and technology: embodying cultured cortical neurons via robotics. From this platform, explanations of the emergent neural network properties leading to cognition are sought through detailed electrical observation of neural activity. By growing the networks of neurons and glia over multi-electrode arrays (MEA), which can be used to both stimulate and record the activity of multiple neurons in parallel over months, a long-term real-time 2-way communication with the neural network becomes possible. A better understanding of the processes leading to biological cognition can, in turn, facilitate progress in understanding neural pathologies, designing neural prosthetics, and creating fundamentally different types of artificial cognition. Here, methods were first developed to reliably induce and detect neural plasticity using MEAs. This knowledge was then applied to construct sensory-motor mappings and training algorithms that produced adaptive goal-directed behavior. To paraphrase the results, most any stimulation could induce neural plasticity, while the inclusion of temporal and/or spatial information about neural activity was needed to identify plasticity. Interestingly, the plasticity of action potential propagation in axons was observed. This is a notion counter to the dominant theories of neural plasticity that focus on synaptic efficacies and is suggestive of a vast and novel computational mechanism for learning and memory in the brain. Adaptive goal-directed behavior was achieved by using patterned training stimuli, contingent on behavioral performance, to sculpt the network into behaviorally appropriate functional states: network plasticity was not only induced, but could be customized. Clinically, understanding the relationships between electrical stimulation, neural activity, and the functional expression of neural plasticity could assist neuro-rehabilitation and the design of neuroprosthetics. In a broader context, the networks were also embodied with a robotic drawing machine exhibited in galleries throughout the world. This provided a forum to educate the public and critically discuss neuroscience, robotics, neural interfaces, cybernetics, bio-art, and the ethics of biotechnology.Ph.D.Committee Chair: Steve M. Potter; Committee Member: Eric Schumacher; Committee Member: Robert J. Butera; Committee Member: Stephan P. DeWeerth; Committee Member: Thomas D. DeMars