Roderick Adams

STD-Dependent and Independent Encoding of Input Irregularity as Spike Rate in a Computational Model of a Cerebellar Nucleus Neuron

Neurons in the cerebellar nuclei (CN) receive inhibitory inputs from Purkinje cells in the cerebellar cortex and provide the major output from the cerebellum, but their computational function is not well understood. It has recently been shown that the spike activity of Purkinje cells is more regular than previously assumed and that this regularity can affect motor behaviour. We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. For high convergence ratios, the irregularity induced spike rate acceleration depends on short-term depression (STD) at the Purkinje cell synapses. At low convergence ratios, or for synchronised Purkinje cell input, the firing rate increase is independent of STD. The transformation of input irregularity into output spike rate occurs in response to artificial input spike trains as well as to spike trains recorded from Purkinje cells in tottering mice, which show highly irregular spiking patterns. Our results suggest that STD may contribute to the accelerated CN spike rate in tottering mice and they raise the possibility that the deficits in motor control in these mutants partly result as a pathological consequence of this natural form of plasticity.

Integrating binding site predictions using meta classification methods

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks and support vector machines on predictions from 12 key algorithms. Furthermore, we use a ‘window’ of consecutive results for the input vectors in order to contextualise the neighbouring results. Moreover, we improve the classification result with the aid of under- and over- sampling techniques. We find that by integrating 12 base algorithms, support vector machines and single layer networks can give better binding site predictions.

An Investigation into the Performance and Representations of a Stochastic, Evolutionary Neural Tree

The stochastic competitive evolutionary neural tree (SCENT) is a new unsupervised neural net that dynamically evolves a representational structure in response to its training data. Uniquely SCENT requires no initial parameter setting as it autonomously creates appropriate parameterisation at runtime. Pruning and convergence are stochastically controlled using locally calculated heuristics. A thorough investigation into the performance of SCENT is presented. The network is compared to other dynamic tree based models and to a high quality flat clusterer over a variety of data sets and runs.

Hierarchical classification with a stochastic competitive evolutionary neural tree

The stochastic competitive evolutionary neural tree (SCENT) is a dynamic tree structured network that is able to provide a hierarchical classification of unlabelled data sets. The SCENT is an extension of the competitive evolutionary neural tree (CENT) with the addition of temperature controlled stochastic noise to enable the network to provide solutions independent of initialisation conditions. The main advantage that the SCENT offers over other hierarchical competitive networks is its ability to self determine the number and structure of the competitive nodes in the network without the need for externally set parameters. The network produces stable classificatory structures by halting its growth using locally calculated heuristics. The results of network simulations are presented over Andersons IRIS data set.

Decoding of Purkinje cell pauses by deep cerebellar nucleus neurons

Recent work has shown that Purkinje cells can read out PF patterns that have been stored by PF LTD by using a novel neural code [4]. Computer simulations and electrophysiological recordings in slices and awake mice predicted that the presentation of patterns of synchronised PF activity results in a characteristic burst-pause sequence in Purkinje cell firing, with novel patterns giving rise to longer pauses than stored patterns. The duration of these pauses was the best criterion to distinguish Purkinje cell responses to stored and novel patterns.

Support vector regression to estimate the permeability enhancement of potential transdermal enhancers.

Searching for chemicals that will safely enhance transdermal drug delivery is a significant challenge. This study applies support vector regression (SVR) for the first time to estimating the optimal formulation design of transdermal hydrocortisone formulations.

The importance of hyperparameters selection within small datasets

Gaussian Process is a Machine Learning technique that has been applied to the analysis of percutaneous absorption of chemicals through human skin. The normal, automatic method of setting the hyperparameters associated with Gaussian Processes may not be suitable for small datasets. In this paper we investigate whether a handcrafted search method of determining these hyperparameters is better for such datasets.

Synaptic Plasticity and Pattern Recognition in Cerebellar Purkinje Cells

Many theories of cerebellar learning assume that long-term depression (LTD) of synapses between parallel fibres (PFs) and Purkinje cells is the basis for pattern recognition in the cerebellum. Here we describe a series of computer simulations that use a morphologically realistic conductance-based model of a cerebellar Purkinje cell to study pattern recognition based on PF LTD. Our simulation results, which are supported by electrophysiological recordings in vitro and in vivo, suggest that Purkinje cells can use a novel neural code that is based on the duration of silent periods in their activity. The simulations of the biologically detailed Purkinje cell model are compared with simulations of a corresponding artificial neural network (ANN) model. We find that the predictions of the two models differ to a large extent. The Purkinje cell model is very sensitive to the amount of LTD induced, whereas the ANN is not. Moreover, the pattern recognition performance of the ANN increases as the patterns become sparser, while the Purkinje cell model is unable to recognise very sparse patterns. These results highlight that it is important to choose a model at a level of biological detail that fits the research question that is being addressed.

The Application of Gaussian Processes in the Prediction of Permeability Across a Polydimethlysiloxane Membrane

Polydimethylsiloxane (PDMS) silicone membranes, such as Silastic®, have been used widely in place of mammalian tissue in the determination of percutaneous absorption. While many experiments have shown correlations between the permeability across both membranes, Moss et al. demonstrated in a systematic study that PDMS membranes tend to exhibit greater permeability than mammalian skin, and that the relationship between permeability across both membranes was not found when the lipophilicity of the penetrant was greater than 3. Further, it was shown previously that when five commonly used physicochemical descriptors were applied to human, pig and rodent membranes they cannot represent the main characteristics of the PDMS dataset when using Gaussian Process (GP) regression to predict skin permeability. However, the previous study in which this process was modelled employed a small dataset (n=19, as part of the wider aims of that work to investigate the effect of dataset construction on model quality).

Estimation of Microphysical Parameters of Atmospheric Pollution Using Machine Learning

The estimation of microphysical parameters of pollution (effective radius and complex refractive index) from optical aerosol parameters entails a complex problem. In previous work based on machine learning techniques, Artificial Neural Networks have been used to solve this problem. In this paper, the use of a classification and regression solution based on the k-Nearest Neighbor algorithm is proposed. Results show that this contribution achieves better results in terms of accuracy than the previous work.