1,720,975 research outputs found
Redundant Input Cancellation by a Bursting Neural Network
No full textOne of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation.
In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs
Parameter Estimation, Optimal Control and Optimal Design in Stochastic Neural Models
No full textThis thesis solves estimation and control problems in computational\ud
neuroscience, mathematically dealing with the first-passage times of diffusion\ud
stochastic processes. We first derive estimation algorithms for model parameters\ud
from first-passage time observations, and then we derive algorithms for the\ud
control of first-passage times. Finally, we solve an optimal design\ud
problem which combines elements of the first two: we ask how to elicit\ud
first-passage times such as to facilitate model estimation based on said\ud
first-passage observations.\ud
The main mathematical tools used are the Fokker-Planck partial differential\ud
equation for evolution of probability densities, the Hamilton-Jacobi-Bellman\ud
equation of optimal control and the adjoint optimization principle from optimal\ud
control theory.\ud
The focus is on developing computational schemes for the\ud
solution of the problems. The schemes are implemented and are tested for a wide\ud
range of parameters
Parameter Estimation, Optimal Control and Optimal Design in Stochastic Neural Models
No full textThis thesis solves estimation and control problems in computational
neuroscience, mathematically dealing with the first-passage times of diffusion
stochastic processes. We first derive estimation algorithms for model parameters
from first-passage time observations, and then we derive algorithms for the
control of first-passage times. Finally, we solve an optimal design
problem which combines elements of the first two: we ask how to elicit
first-passage times such as to facilitate model estimation based on said
first-passage observations.
The main mathematical tools used are the Fokker-Planck partial differential
equation for evolution of probability densities, the Hamilton-Jacobi-Bellman
equation of optimal control and the adjoint optimization principle from optimal
control theory.
The focus is on developing computational schemes for the
solution of the problems. The schemes are implemented and are tested for a wide
range of parameters
Redundant Input Cancellation by a Bursting Neural Network
No full textOne of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation.
In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs
Neural dynamics leading to optimized information transfer
No full textNeural information processing by trains of action potentials is studied in the context of weakly electric fish electroreceptor neurons. A simple but accurate dynamical model for the firing activity of these neurons is presented and compared with experimental results. Dynamical analysis of the model reveals the mechanism by which it reproduces features present in experimental data, such as relative refractoriness and bursting behaviour. Approximations necessary for application of information theory to neural spike trains are presented and different measures are compared. Finally, the consequences of spike patterning caused by relative refractoriness and bursting on information transfer are investigated. It is found that relative refractoriness can increase information transfer while bursting provides a non-linear mechanism for encoding information that might be more efficient than firing of isolated spikes
Stimulus Coding and Synchrony in Stochastic Neuron Models
No full textA stochastic leaky integrate-and-fire neuron model was implemented in this study to simulate the spiking activity of the electrosensory "P-unit" receptor neurons of the weakly electric fish Apteronotus leptorhynchus. In the context of sensory coding, these cells have been previously shown to respond in experiment to natural random narrowband signals with either a linear or nonlinear coding scheme, depending on the intrinsic firing rate of the cell in the absence of external stimulation. It was hypothesised in this study that this duality is due to the relation of the stimulus to the neuron's excitation threshold. This hypothesis was validated with the model by lowering the threshold of the neuron or increasing its intrinsic noise, or randomness, either of which made the relation between firing rate and input strength more linear.
Furthermore, synchronous P-unit firing to a common input also plays a role in decoding the stimulus at deeper levels of the neural pathways. Synchronisation and desynchronisation between multiple model responses for different types of natural communication signals were shown to agree with experimental observations. A novel result of resonance-induced synchrony enhancement of P-units to certain communication frequencies was also found
Effective Stochastic Models of Neuroscientific Data with Application to Weakly Electric Fish
No full textNeural systems are often stochastic, non-linear, and non-autonomous. The complex manifestation of these aspects hinders the interpretation of neuroscientific data. Neuroscience
thus benefits from the inclusion of theoretical models in its methodology. Detailed biophysical models of neural systems, however, are often plagued by high-dimensional and poorly constrained parameter spaces. As an alternative, data-driven effective models can often explain the core dynamical features of a dataset with few underlying assumptions. By lumping high-dimensional fluctuations into low-dimensional stochastic terms, observed time-series can be well-represented by stochastic dynamical systems. Here, I apply this approach to two datasets from weakly electric fish. The rate of electrosensory sampling of freely behaving fish displays spontaneous transitions between two preferred values: an active exploratory state and a resting state. I show that, over a long timescale, this rate can be modelled with a stochastic double-well system where a slow external agent modulates the relative depth of the wells. On a shorter timescale, however, fish exhibit abrupt and transient increases in sampling rate not consistent with a diffusion process. I develop and apply a novel inference method to construct a jump-diffusion process that fits the observed fluctuations. This same technique is successfully applied to intrinsic membrane voltage noise in pyramidal neurons of the primary electrosensory processing area, which display abrupt depolarization events along with diffusive fluctuations. I then characterize a novel sensory acquisition strategy whereby fish adopt a rhythmic movement pattern coupled with stochastic oscillations of their sampling rate. Lastly, in the context of differentiating between self-generated and external electrosensory signals, I model the sensory signature of communication signals between fish. This analysis provides supporting evidence for the presence of a sensory ambiguity associated with these signals
Neural dynamics leading to optimized information transfer
No full textNeural information processing by trains of action potentials is studied in the context of weakly electric fish electroreceptor neurons. A simple but accurate dynamical model for the firing activity of these neurons is presented and compared with experimental results. Dynamical analysis of the model reveals the mechanism by which it reproduces features present in experimental data, such as relative refractoriness and bursting behaviour. Approximations necessary for application of information theory to neural spike trains are presented and different measures are compared. Finally, the consequences of spike patterning caused by relative refractoriness and bursting on information transfer are investigated. It is found that relative refractoriness can increase information transfer while bursting provides a non-linear mechanism for encoding information that might be more efficient than firing of isolated spikes
Using Machine Learning Techniques to Understand the Biophysics of Demyelination
No full textDemyelination is the process where the insulating layer of axons known as myelin is
damaged. This affects the propagation of action potentials along axons which can have deteriorating consequences on the motor activity of an organism. Thus it is important to understand the biophysical effects of demyelination to improve the diagnostics of its diseases. We trained a Convolutional Neural Network (CNN) on Coherent anti-Stokes Raman scattering (CARS) microscope images of mice spinal cord inflicted with the demyelinating disease Experimental Autoimmune Encephalomyelitis (EAE). Our CNN was able to classify the images reliably based on clinical scores assigned to the mice. We then synthesized our own images of the spinal cord regions using a 2D Biased Random Walk. These images are simplified versions of the original CARS images and show homogenously myelinated axons, unlike the heterogeneous nerve fibres found in real spinal cords. The images were fed into the trained CNN as an attempt to develop a clinical connection to the biophysical effects of demyelination. We found that the trained CNN was indeed able to capture structural features related to demyelination which can allow us to constrain demyelination models such that they include the simulated parameters of the synthesized images
Stimulus Coding and Synchrony in Stochastic Neuron Models
No full textA stochastic leaky integrate-and-fire neuron model was implemented in this study to simulate the spiking activity of the electrosensory "P-unit" receptor neurons of the weakly electric fish Apteronotus leptorhynchus. In the context of sensory coding, these cells have been previously shown to respond in experiment to natural random narrowband signals with either a linear or nonlinear coding scheme, depending on the intrinsic firing rate of the cell in the absence of external stimulation. It was hypothesised in this study that this duality is due to the relation of the stimulus to the neuron's excitation threshold. This hypothesis was validated with the model by lowering the threshold of the neuron or increasing its intrinsic noise, or randomness, either of which made the relation between firing rate and input strength more linear.
Furthermore, synchronous P-unit firing to a common input also plays a role in decoding the stimulus at deeper levels of the neural pathways. Synchronisation and desynchronisation between multiple model responses for different types of natural communication signals were shown to agree with experimental observations. A novel result of resonance-induced synchrony enhancement of P-units to certain communication frequencies was also found
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