Computational Modelling of Information Processing in Deep Cerebellar Nucleus Neurons

The deep cerebellar nuclei (DCN) function as output gates for a large majority of the Purkinje cells of the cerebellar cortex and thereby determine how the cerebellum influences the rest of the brain and body. In my PhD programme I have investigated how the DCN process two kinds of input patterns received from Purkinje cells: irregularity of spike intervals and pauses in Purkinje cell activity resulting from the recognition of patterns received at the synapses with the upstream parallel fibres (PFs). To that objective I have created a network system of biophysically realistic Purkinje cell and DCN neuron models that enables the exploration of a wide range of network structure and cell physiology parameters. With this system I have performed simulations that show how the DCN neuron changes the information modality of its input, consisting of varying regularity in Purkinje cell spike intervals, to varying spike rates in its output to the nervous system outside of the cerebellum. This was confirmed in simulations where I exchanged the artificial Purkinje cell trains for those received from experimental collaborators. In pattern recognition simulations I have found that the morphological arrangement present in the cerebellum, where multiple Purkinje cells connect to each DCN neuron, has the effect of amplifying pattern recognition already performed in the Purkinje cells. Using the metric of signal-to-noise ratio I show that PF patterns previously encountered and stored in PF - Purkinje cell synapses are most clearly distinguished from those novel to the system by a 10-20 ms shortened burst firing of the DCN neuron. This result suggests that the effect on downstream targets of these excitatory projection neurons is a decreased excitation when a stored as opposed to novel pattern is received. My work has contributed to a better understanding of information processing in the cerebellum, with implications for human motor control as well as the increasingly recognised non-motor functions of the cerebellum

Computational modelling of information processing in deep cerebellar nucleus neurons

The deep cerebellar nuclei (DCN) function as output gates for a large majority of the Purkinje cells of the cerebellar cortex and thereby determine how the cerebellum influences the rest of the brain and body. In my PhD programme I have investigated how the DCN process two kinds of input patterns received from Purkinje cells: irregularity of spike intervals and pauses in Purkinje cell activity resulting from the recognition of patterns received at the synapses with the upstream parallel fibres (PFs). To that objective I have created a network system of biophysically realistic Purkinje cell and DCN neuron models that enables the exploration of a wide range of network structure and cell physiology parameters. With this system I have performed simulations that show how the DCN neuron changes the information modality of its input, consisting of varying regularity in Purkinje cell spike intervals, to varying spike rates in its output to the nervous system outside of the cerebellum. This was confirmed in simulations where I exchanged the artificial Purkinje cell trains for those received from experimental collaborators. In pattern recognition simulations I have found that the morphological arrangement present in the cerebellum, where multiple Purkinje cells connect to each DCN neuron, has the effect of amplifying pattern recognition already performed in the Purkinje cells. Using the metric of signal-to-noise ratio I show that PF patterns previously encountered and stored in PF - Purkinje cell synapses are most clearly distinguished from those novel to the system by a 10-20 ms shortened burst firing of the DCN neuron. This result suggests that the effect on downstream targets of these excitatory projection neurons is a decreased excitation when a stored as opposed to novel pattern is received. My work has contributed to a better understanding of information processing in the cerebellum, with implications for human motor control as well as the increasingly recognised non-motor functions of the cerebellum.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Decoding of Purkinje cell pauses by deep cerebellar nucleus neurons

The recognition of parallel fibre (PF) input patterns by Purkinje cells has been suggested to underlie cerebellar learning [1,2]. A candidate mechanism for the recognition of PF patterns is the long-term depression (LTD) of the PF synapses that is induced when the Purkinje cell receives coincident PF and climbing fibre input [3]. 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. In the present study, we used a two-layer network model to investigate the effect of PF LTD on the target neurons of the Purkinje cells in the deep cerebellar nuclei (DCN). In our simulations, a multi-compartmental conductance-based DCN model [5] received input from up to 450 independent Purkinje cell models through inhibitory GABAergic synapses. PF patterns were stored by depressing the synapses between the PFs and the Purkinje cells. The network was presented with stored and novel PF patterns, and the ability of the DCN model to distinguish between those was evaluated by calculating signal-to-noise ratios for different features of its spike response. The simulations were performed for different Purkinje cell firing rates and for varying fractions of Purkinje cells that received PF input patterns. The presentation of PF patterns to the network resulted in the burst-pause response in the Purkinje cells that had previously been described [4]. These burst-pause sequences caused a characteristic spike response in the DCN model, comprising a short pause that was followed by a rebound burst and another pause. Several features of this DCN response could be used to identify stored PF patterns, but the number of spikes in the rebound burst was clearly the best criterion for pattern recognition. The pattern recognition performance was amplified by the DCN model, with signal-to-noise ratios that were up to seven times higher than those measured for the Purkinje cell response. Our results are robust against varying Purkinje cell firing rates and to a five-fold reduction of the number of Purkinje cells receiving PF input patterns

The Transcriptome of Rhabdomyosarcoma Cells Infected with Cytolytic and Non-Cytolytic Variants of Coxsackievirus B2 Ohio-1.

The transcriptomes of cells infected with lytic and non-lytic variants of coxsackievirus B2 Ohio-1 (CVB2O) were analyzed using next generation sequencing. This approach was selected with the purpose of elucidating the effects of lytic and non-lytic viruses on host cell transcription. Total RNA was extracted from infected cells and sequenced. The resulting reads were subsequently mapped against the human and CVB2O genomes. The amount of intracellular RNA was measured, indicating lower proportions of human RNA in the cells infected with the lytic virus compared to the non-lytic virus after 48 hours. This may be explained by reduced activity of the cellular transcription/translation machinery in lytic enteroviral replication due to activities of the enteroviral proteases 2A and/or 3C. Furthermore, differential expression in the cells infected with the two virus variants was identified and a number of transcripts were singled out as possible answers to the question of how the viruses interact with the host cells, resulting in lytic or non-lytic infections

Gene cloud displaying the 500 most significantly differentially expressed transcripts.

<p>Font size is proportional to the highest change in expression and inversely proportional to the likelihood of the result.</p

Comparison of differentially expressed transcribed sequences.

<p>Comparison of the differences in expression between vVP1Q164K, CVB2Owt and mock, at 48 hours post infection. Difference in expression that (only) occurs in: (pink) vVP1Q164K compared to CVB2Owt; (orange) vVP1Q164K compared to both CVB2Owt and mock; (yellow) vVP1Q164K compared to mock; (purple) CVB2Owt compared to both vVP1Q164K and mock; (white) between all conditions; (green) mock compared to both vVP1Q164K and CVB2Owt; (blue) CVB2Owt compared to mock.</p

Differentially expressed transcripts at alpha 0.01.

<p>The number of transcripts that are differentially expressed compared to the other conditions.</p

Relative amounts of intracellular RNA.

<p>(A) All sequences were mapped against the CVB2O and human genome to obtain the proportions of viral to human RNA in each sample. Sequences that did not match either genome are denoted <i>other</i>. (B) The intracellular amounts of viral RNA have been normalized against the amount in the first sample of the triplicate of the cells infected with vVP1Q164K and incubated for 24 hours. Error bars indicate 95% confidence interval.</p

The 20 transcripts most differentially expressed between vVP1Q164K (48 h) and CVB2Owt.

<p>The conditions are normalized to mock-cell expression levels.</p

STD-dependent and independent encoding of input irregularity as spike rate in a computational model of a cerebellar nucleus neuron

Copyright The Authors 2011. This article is published with open access at Springerlink.comNeurons 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.Peer reviewedFinal Published versio