UH Biocomputation Group - cerebellumhttp://biocomputation.herts.ac.uk/2023-05-17T17:18:16+01:00Computational model of the cerebellar cortex2023-05-17T17:18:16+01:002023-05-17T17:18:16+01:00Eleonora Bernasconitag:biocomputation.herts.ac.uk,2023-05-17:/2023/05/17/computational-model-of-the-cerebellar-cortex.html<p class="first last">Eleonora Bernasconi's Journal Club session where she will talk about a her work "Computational model of the cerebellar cortex".</p>
<p>This week on Journal Club session Eleonora Bernasconi will present her work about "Computational model of the cerebellar cortex". Please find below to see the abstract of one of the related papers.</p>
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<p>Climbing fibers (CFs) provide instructive signals driving cerebellar learning, but
mechanisms causing the variable CF responses in Purkinje cells (PCs) are not fully
understood. Using a new experimentally validated PC model, we unveil the ionic mechanisms
underlying CF-evoked distinct spike waveforms on different parts of the PC. We demonstrate
that voltage can gate both the amplitude and the spatial range of CF-evoked Ca2+ influx by
the availability of K+ currents. This makes the energy consumed during a complex spike
(CS) also voltage dependent. PC dendrites exhibit inhomogeneous excitability with
individual branches as computational units for CF input. The variability of somatic CSs
can be explained by voltage state, CF activation phase, and instantaneous CF firing rate.
Concurrent clustered synaptic inputs affect CSs by modulating dendritic responses in a
spatially precise way. The voltage- and branch-specific CF responses can increase
dendritic computational capacity and enable PCs to actively integrate CF signals.</p>
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<p>Papers:</p>
<ul class="simple">
<li>Y. Zang, S. Dieudonn'e, E. De, Schutter, <a class="reference external" href="https://doi.org/10.1016/j.celrep.2018.07.011">"Voltage- and Branch-Specific Climbing Fiber Responses in Purkinje Cells"</a>, 2018, Cell Reports, 24, 1536--1549</li>
<li>S. Sudhakar, S. Hong, I. Raikov, R. Publio, C. Lang, T. Close, D. Guo, M.
Negrello, E. De, Schutter, <a class="reference external" href="https://doi.org/10.1371/journal.pcbi.1005754">"Spatiotemporal Network Coding of Physiological
Mossy Fiber Inputs by the Cerebellar Granular Layer"</a>, 2017, PLoS computational
biology, 13, e1005754</li>
</ul>
<p><strong>Date:</strong> 2023/05/19 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
On Robot Compliance: A Cerebellar Control Approach2022-11-09T09:17:18+00:002022-11-09T09:17:18+00:00Mahsa Aliakbarzadehtag:biocomputation.herts.ac.uk,2022-11-09:/2022/11/09/on-robot-compliance-a-cerebellar-control-approach.html<p class="first last">Mahsa Aliakbarzadeh's Journal Club session where he will talk about a paper "On Robot Compliance: A Cerebellar Control Approach"</p>
<p>This week on Journal Club session Mahsa Aliakbarzadeh will talk about a paper "On Robot Compliance: A Cerebellar Control Approach".</p>
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<p>The work presented here is a novel biological approach for the compliant
control of a robotic arm in real time (RT). We integrate a spiking cerebellar
network at the core of a feedback control loop performing torque-driven
control. The spiking cerebellar controller provides torque commands allowing
for accurate and coordinated arm movements. To compute these output motor
commands, the spiking cerebellar controller receives the robot's sensorial
signals, the robot's goal behavior, and an instructive signal. These input
signals are translated into a set of evolving spiking patterns representing
univocally a specific system state at every point of time. Spike-
timing-dependent plasticity (STDP) is then supported, allowing for building
adaptive control. The spiking cerebellar controller continuously adapts the
torque commands provided to the robot from experience as STDP is deployed.
Adaptive torque commands, in turn, help the spiking cerebellar controller to
cope with built-in elastic elements within the robot's actuators mimicking
human muscles (inherently elastic). We propose a natural integration of a
bioinspired control scheme, based on the cerebellum, with a compliant robot. We
prove that our compliant approach outperforms the accuracy of the default
factory-installed position control in a set of tasks used for addressing
cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth
movements, fast ballistic movements, and unstructured scenario compliant
movements.</p>
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<p>Papers:</p>
<ul class="simple">
<li>I. Abad'ia, F. Naveros, J. Garrido, E. Ros, N. Luque, <a class="reference external" href="https://doi.org/10.1109/TCYB.2019.2945498">"On Robot Compliance: A Cerebellar Control Approach"</a>, 2021, IEEE Transactions on Cybernetics, 51, 2476--2489</li>
</ul>
<p><strong>Date:</strong> 2022/11/11 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
Long-Term Depression and Recognition of Parallel Fibre Patterns in a Multi-Compartmental Model of a Cerebellar Purkinje Cell2022-06-28T14:18:39+01:002022-06-28T14:18:39+01:00Volker Steubertag:biocomputation.herts.ac.uk,2022-06-28:/2022/06/28/temporal-coding-and-rank-order-coding.html<p class="first last">Volker Steuber's Journal Club session where he will talk about temporal coding and, in particular, rank order coding. Please see the papers below for more details.</p>
<p>This week on Journal Club session Volker Steuber will talk about temporal coding and, in particular, rank order coding. Please see the papers below for more details.</p>
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<p>It has been suggested that long-term depression (LTD) of parallel fibre (PF)
synapses enables a cerebellar Purkinje cell (PC) to learn to recognise PF
activity patterns. We investigate the recognition of PF patterns that have been
stored by LTD of AMPA receptors in a multi- compartmental PC model with a
passive soma. We find that a corresponding artificial neural network
outperforms a PC model with active dendrites by an order of magnitude. Removal
of the dendritic ion channels leads to a further decrease in performance.
Another effect of the active dendrites is an afterhyperpolarization response to
novel PF patterns. Thus, the LTD based storage of PF patterns can lead to a
potentiated late PC response.</p>
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</div>
<p>Papers:</p>
<ul class="simple">
<li>B. Sabatini, W. Regehr, <a class="reference external" href="https://doi.org/10.1038/384170a0">"Timing of Neurotransmission at Fast Synapses in the Mammalian Brain"</a>, 1996, Nature, 384, 170--172</li>
<li>V. Steuber, E. De, Schutter, <a class="reference external" href="https://doi.org/10.1016/S0925-2312(01)00458-1">"Long-Term Depression and Recognition of Parallel Fibre Patterns in a Multi-Compartmental Model of a Cerebellar Purkinje Cell"</a>, 2001, Neurocomputing, 38--40, 383--388</li>
<li>S. Thorpe, D. Fize, C. Marlot, <a class="reference external" href="https://doi.org/10.1016/S0002-9394(14)72148-8">"Speed of Processing in the Human Visual System"</a>, 1996, American Journal of Ophthalmology, 381, 608--609</li>
<li>S. Thorpe, A. Delorme, R. Van, Rullen, <a class="reference external" href="https://doi.org/10.1016/S0893-6080(01)00083-1">"Spike-Based Strategies for Rapid Processing"</a>, 2001, Neural Networks, 14, 715--725</li>
</ul>
<p><strong>Date:</strong> 2022/07/01 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
A Cerebellar-Based Solution to the Nondeterministic Time Delay Problem in Robotic Control2021-10-13T13:28:39+01:002021-10-13T13:28:39+01:00Volker Steubertag:biocomputation.herts.ac.uk,2021-10-13:/2021/10/13/a-cerebellar-based-solution-to-the-nondeterministic-time-delay-problem-in-robotic-control.html<p class="first last">Volker Steuber's Journal Club session where he will talk about a paper "A Cerebellar-Based Solution to the Nondeterministic Time Delay Problem in Robotic Control"</p>
<p>This week on Journal Club session Volker Steuber will talk about a paper "A Cerebellar-Based Solution to the Nondeterministic Time Delay Problem in Robotic Control".</p>
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<p>The presence of computation and transmission-variable time delays
within a robotic control loop is a major cause of instability,
hindering safe human-robot interaction (HRI) under these
circumstances. Classical control theory has been adapted to counteract
the presence of such variable delays; however, the solutions provided
to date cannot cope with HRI robotics inherent features. The highly
nonlinear dynamics of HRI cobots (robots intended for human
interaction in collaborative tasks), together with the growing use of
flexible joints and elastic materials providing passive compliance,
prevent traditional control solutions from being applied. Conversely,
human motor control natively deals with low power actuators, nonlinear
dynamics, and variable transmission time delays. The cerebellum,
pivotal to human motor control, is able to predict motor commands by
correlating current and past sensorimotor signals, and to ultimately
compensate for the existing sensorimotor human delay (tens of
milliseconds). This work aims at bridging those inherent features of
cerebellar motor control and current robotic challengestextemdash
namely, compliant control in the presence of variable sensorimotor
delays. We implement a cerebellar-like spiking neural network (SNN)
controller that is adaptive, compliant, and robust to variable
sensorimotor delays by replicating the cerebellar mechanisms that
embrace the presence of biological delays and allow motor learning and
adaptation.</p>
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</div>
<p>Papers:</p>
<ul class="simple">
<li>I. Abad'ia, F. Naveros, E. Ros, R. Carrillo, N. Luque, <a class="reference external" href="https://doi.org/10.1126/scirobotics.abf2756">"A Cerebellar-Based Solution to the Nondeterministic Time Delay Problem in Robotic Control"</a>, 2021, Science Robotics, 6,</li>
</ul>
<p><strong>Date:</strong> 2021/10/15 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness2020-11-25T12:47:06+00:002020-11-25T12:47:06+00:00Ohki Katakuratag:biocomputation.herts.ac.uk,2020-11-25:/2020/11/25/complex-dynamics-in-simplified-neuronal-models-reproducing-golgi-cell-electroresponsiveness.html<p class="first last">Ohki Katakura's Journal Club session where he will talk about a paper "Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness".</p>
<p>This week on Journal Club session Ohki Katakura will talk about a paper "Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness".</p>
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<p>Brain neurons exhibit complex electroresponsive properties – including intrinsic
subthreshold oscillations and pacemaking, resonance and phase-reset – which are
thought to play a critical role in controlling neural network dynamics. Although
these properties emerge from detailed representations of molecular-level
mechanisms in “realistic” models, they cannot usually be generated by simplified
neuronal models (although these may show spike-frequency adaptation and
bursting). We report here that this whole set of properties can be generated by
the extended generalized leaky integrate-and-fire (E-GLIF) neuron model. E-GLIF
derives from the GLIF model family and is therefore mono-compartmental, keeps
the limited computational load typical of a linear low-dimensional system,
admits analytical solutions and can be tuned through gradient-descent
algorithms. Importantly, E-GLIF is designed to maintain a correspondence between
model parameters and neuronal membrane mechanisms through a minimum set of
equations. In order to test its potential, E-GLIF was used to model a specific
neuron showing rich and complex electroresponsiveness, the cerebellar Golgi
cell, and was validated against experimental electrophysiological data recorded
from Golgi cells in acute cerebellar slices. During simulations, E-GLIF was
activated by stimulus patterns, including current steps and synaptic inputs,
identical to those used for the experiments. The results demonstrate that E-GLIF
can reproduce the whole set of complex neuronal dynamics typical of these
neurons – including intensity-frequency curves, spike-frequency adaptation,
post-inhibitory rebound bursting, spontaneous subthreshold oscillations,
resonance, and phase-reset – providing a new effective tool to investigate brain
dynamics in large-scale simulations.</p>
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</div>
<p>Papers:</p>
<ul class="simple">
<li>Geminiani, A., Casellato, C., Locatelli, F., Prestori, F., Pedrocchi, A. & D'Angelo, E. (2018) <a class="reference external" href="https://doi.org/10.3389/fninf.2018.00088">"Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness"</a> , Frontiers in Neuroinformatics (Front. Neuroinform.)</li>
</ul>
<p><strong>Date:</strong> 27/11/2020 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: online</p>
Rank order decoding of temporal input patterns2019-02-20T13:02:57+00:002019-02-20T13:02:57+00:00Rebecca Mikotag:biocomputation.herts.ac.uk,2019-02-20:/2019/02/20/rank-order-decoding-of-temporal-input-patterns.html<p class="first last">Volker Steuber's journal club session, where he will briefly discuss different forms of neural coding.</p>
<p>Volker Steuber's journal club session, where he will briefly discuss different forms of neural coding.</p>
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<p>In this talk, Volker will briefly discuss different forms of neural coding. Volker will focus on temporal coding and summarise John Hopfield's (1995) suggestion how neurons with subthreshold oscillations in their membrane potential could perform scale-invariant temporal encoding of input patterns (and which conditions have to be met for the encoding to be scale invariant). However, temporal coding is sensitive to noise, and Volker will describe the rank order decoding scheme that was suggested by Simon Thorpe (1998) in order to provide robustness against noise. Furthermore, Volker will outline how Purkinje cells in cerebellar cortex could implement a form of rank-order decoding of temporal parallel fibre input patterns.</p>
<p>Volker will reference the following papers:</p>
<p>"<a class="reference external" href="https://www.nature.com/articles/376033a0">Pattern recognition computation using action potential timing for stimulus representation (J. J. Hopefield, 1995)</a>"</p>
<p>"<a class="reference external" href="https://www.nature.com/articles/381520a0">Speed of processing in the human visual system (S. Thorpe, D. Fize and C. Marlot, 1996)</a>"</p>
<p>"<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0303264798000707">Face processing using one spike per neurone (R. Van Rullen et al., 1998)</a>"</p>
<p>"<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0925231202003880">Rank order decoding of temporal parallel fibre input patterns in a complex Purkinje cell model (V. Steuber and E. De Schutter, 2002)</a>"</p>
<p><strong>Date:</strong> 22/02/2019 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D118</p>
Microcircuits and their interactions in epilepsy: is the focus out of focus?2019-01-22T21:18:35+00:002019-01-22T21:18:35+00:00Rebecca Mikotag:biocomputation.herts.ac.uk,2019-01-22:/2019/01/22/microcircuits-and-their-interactions-in-epilepsy-is-the-focus-out-of-focus-.html<p class="first last">Julia Goncharenko's journal club session, where she will present the paper "<a class="reference external" href="https://www.nature.com/articles/nn.3950.pdf">Microcircuits and their interactions in epilepsy, is the focus out of focus? (Jeanne Paz and John Huguenard, 2015)</a>".</p>
<p>Julia Goncharenko's journal club session, where she will present the paper "<a class="reference external" href="https://www.nature.com/articles/nn.3950.pdf">Microcircuits and their interactions in epilepsy, is the focus out of focus? (Jeanne Paz and John Huguenard, 2015)</a>".</p>
<hr class="docutils" />
<p>Epileptic seizures represent dysfunctional neural networks dominated by excessive and/or hypersynchronous activity. Recent progress in the field has outlined two concepts regarding mechanisms of seizure generation, or ictogenesis. First, all seizures, even those associated with what have historically been thought of as ‘primary generalized’ epilepsies, appear to originate in local microcircuits and then propagate from that initial ictogenic zone. Second, seizures propagate through cerebral networks and engage microcircuits in distal nodes, a process that can be weakened or even interrupted by suppressing activity in such nodes. We describe various microcircuit motifs, with a special emphasis on one that has been broadly implicated in several epilepsies: feed-forward inhibition. Furthermore, we discuss how, in the dynamic network in which seizures propagate, focusing on circuit ‘choke points’ remote from the initiation site might be as important as that of the initial dysfunction, the seizure ‘focus’.</p>
<p><strong>Date:</strong> 25/01/2019 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D118</p>
Computational Model of the Cerebellum and the Basal Ganglia for Interval Timing Learning2018-11-05T14:49:43+00:002018-11-05T14:49:43+00:00Rebecca Mikotag:biocomputation.herts.ac.uk,2018-11-05:/2018/11/05/computational-model-of-the-cerebellum-and-the-basal-ganglia-for-interval-timing-learning.html<p class="first last">Ohki Katakura's journal club session on his master's work "<a class="reference external" href="https://link.springer.com/chapter/10.1007%2F978-3-319-46681-1_30">Computational Model of the Cerebellum and the Basal Ganglia for Interval Timing Learning (Ohki Katakura; Tadashi Yamazaki, 2016)</a>"</p>
<p>Ohki Katakura's journal club session on his master's work "<a class="reference external" href="https://link.springer.com/chapter/10.1007%2F978-3-319-46681-1_30">Computational Model of the Cerebellum and the Basal Ganglia for Interval Timing Learning (Ohki Katakura; Tadashi Yamazaki, 2016)</a>"</p>
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<p>In temporal information processing, both the cerebellum and the basal ganglia play essential roles. In particular, for interval timing learning, the cerebellum exhibits temporally localized activity around the onset of the unconditioned stimulus, whereas the basal ganglia represents the passage of time by their ramping-up activity from the onset of the conditioned stimulus to that of the unconditioned stimulus. We present a unified computational model of the cerebellum and the basal ganglia for the interval timing learning task. We report that our model reproduces the localized activity in the cerebellum and the gradual increase of the activity in the basal ganglia. These results suggest that the cerebellum and the basal ganglia play different roles in temporal information processing.</p>
<p><strong>Date:</strong> 09/11/2018 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D120</p>
Three Review Articles on the Cerebellum2018-10-05T08:09:22+01:002018-10-05T08:09:22+01:00Reinoud Maextag:biocomputation.herts.ac.uk,2018-10-05:/2018/10/05/three-review-articles-on-the-cerebellum.html<p class="first last">Reinoud Maex's journal club session on three review papers related to
his past cerebellum work.</p>
<p>Reinoud will briefly present three review papers related to his past cerebellum
work.</p>
<p>The three papers are as follows:</p>
<p>Carpenter RH (2011)
What Sherrington missed: the ubiquity of the neural integrator.
Ann N Y Acad Sci. 1233, 208-213.
doi: 10.1111/j.1749-6632.2011.06110.x.</p>
<p>Noorani I, Carpenter RH. (2017)
Not moving: the fundamental but neglected motor function.
Philos Trans R Soc Lond B Biol Sci. 372, 1718.
doi: 10.1098/rstb.2016.0190.</p>
<p>Shadmehr R. (2017)
Distinct neural circuits for control of movement vs. holding still.
J Neurophysiol. 117, 1431-1460.
doi: 10.1152/jn.00840.2016.</p>
<p><strong>Date:</strong> 05/10/2018 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
Open Position: PhD studentships in Computational Neuroscience2017-05-31T13:20:03+01:002017-05-31T13:20:03+01:00Volker Steubertag:biocomputation.herts.ac.uk,2017-05-31:/2017/05/31/open-position-phd-studentships-in-computational-neuroscience.html<p class="first last">Applications are invited for PhD studentships at the Biocomputation Research Group. Details within.</p>
<p>Applications are invited for PhD positions in the Biocomputation Research Group at the University of Hertfordshire. Projects involve the development and simulation of models of neurons and neuronal networks to study information processing in the cerebellum or olfactory system and/or the application of machine learning techniques for the analysis of neural data. A description of our research interests and a list of publications can be found on our webpage (<a class="reference external" href="http://biocomputation.herts.ac.uk/">http://biocomputation.herts.ac.uk/</a>).</p>
<p>Applicants should have excellent computational and numerical skills and a very good first degree in computer science, biology, maths, physics, neuroscience, or a related discipline. Successful candidates are eligible for a research studentship award from the University (GBP 14,553 per annum bursary plus payment of the student fees). Applicants from outside the UK or EU are eligible.</p>
<p>Research in Computer Science at the University of Hertfordshire has been recognised as excellent in the latest Research Excellence Framework Assessment, with 50% of the research submitted rated as internationally excellent or world leading. The Centre for Computer Science and Informatics Research provides a very stimulating environment, offering a large number of specialised and interdisciplinary seminars as well as general training and researcher development opportunities. The University is situated in Hatfield, in the green belt just north of London.</p>
<p>Please contact Dr Volker Steuber for informal enquiries. Application forms are available under <a class="reference external" href="http://www.herts.ac.uk/apply/schools-of-study/computer-science/our-research/the-phd-programme-in-computer-science">http://www.herts.ac.uk/apply/schools-of-study/computer-science/our-research/the-phd-programme-in-computer-science</a></p>
Computational models of synaptic plasticity and information processing in the cerebellum2016-10-04T08:21:45+01:002016-10-04T08:21:45+01:00Volker Steubertag:biocomputation.herts.ac.uk,2016-10-04:/2016/10/04/computational-models-of-synaptic-plasticity-and-information-processing-in-the-cerebellum.html<p class="first last">Volker Steuber's journal club session on computational models of synaptic plasticity and information processing in the cerebellum.</p>
<p>A central theme of the computational neuroscience research in the Biocomputation Research Group is synaptic plasticity, the activity-dependent strengthening and weakening of connections between neurons in the brain. In this talk, I will describe a number of previous PhD projects in the group that have studied computational functions, and the underlying mechanisms, of synaptic plasticity. I will focus on the functional roles and mechanisms of synaptic plasticity in the cerebellum, a brain structure that is important for the control of movements, motor learning and many higher cognitive functions. Our results suggest that different forms of synaptic plasticity at different time scales can implement many diverse functions such as associative memory, noise resistance, multiplicative operations and the transformation between different types of neural code. Moreover, I will discuss the relation between cerebellar synaptic plasticity and movement disorders that are based on cerebellar dysfunction, and I will describe the application of machine learning algorithms to analyse neuronal activity during epileptic seizures.</p>
<p><strong>Date:</strong> 07/10/2016 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
Exploring the cerebellar Purkinje cell2016-02-03T10:56:52+00:002016-02-03T10:56:52+00:00Kirsty Kiddtag:biocomputation.herts.ac.uk,2016-02-03:/2016/02/03/exploring-the-cerebellar-purkinje-cell.html<p class="first last">Kirsty Kidd's journal club session where she summarises her work in investigating changes in the morphology of cerebellar Purkinje cells over different species and the effects of these changes on the cells' ability to process and transfer information.</p>
<p>Purkinje cells are a constant feature of the cerebellar cortex. Though often characterised by their complex dendritic branching, phylogenetically earlier Purkinje cells are much simpler structures. With studies of in vitro electrophysiological behaviour suggesting that firing patterns are very similar across species [1, 2, 3], what has informed such an expensive change in morphology and what effect has this change had on the cells ability to process and transfer information?</p>
<p>This talk will summarise my work in investigating these questions by modelling and analysing Purkinje cells from seven different species.</p>
<p><strong>Date:</strong> 05/02/2016 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
<p>[1] <a class="reference external" href="http://jn.physiology.org/content/jn/32/6/871.full.pdf">Bloedel J.R. and Llinás R. (1969) Neuronal interactions in frog cerebellum, Journal of neurophysiology, 32(6):871-880</a> <br />
[2] <a class="reference external" href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1191918/">Hounsgaard J. and Midtgaard J. (1988) Intrinsic determinants of firing pattern in Purkinje cells of the turtle cerebellum, The Journal of Physiology, 402(1):731-749</a> <br />
[3] <a class="reference external" href="http://science.sciencemag.org/content/160/3832/1132.short">Llinás R., Nicholson C., Freeman A., Hillman D.E. (1968) Dendritic spikes and their inhibition in alligator Purkinje cells, Science, 160(3832):1132-1135</a> <br /></p>
The effect of regularity, synchrony, and STD on deep cerebellar nuclei in physiological conditions and during downbeat nystagmus2015-11-11T16:40:36+00:002015-11-11T16:40:36+00:00Julia Goncharenkotag:biocomputation.herts.ac.uk,2015-11-11:/2015/11/11/the-effect-of-regularity-synchrony-and-std-on-deep-cerebellar-nuclei-in-physiological-conditions-and-during-downbeat-nystagmus.html<p class="first last">Julia Goncharenko's journal club session on the effect of regularity, synchrony, and STD on deep cerebellar nuclei in physiological conditions and during downbeat nystagmus.</p>
<p>It was previously believed, that the reason for downbeat nystagmus (DBN, the symptom of CACNA1a gene mutation) is a lack of inhibition, and that it could be alleviated by an increase of the overall firing rate. It is postulated that 4AP (4-aminopyridine, non-selective voltage-dependent K+ channel blocker) enhances Purkinje cell activity in the flocculus and restores inhibition of anterior canal pathways to a normal level [Glasauer 2005]. But this theory has been disproved by electrophysiological experiments on tg/tg mice cerebellum slices showing that therapeutic concentrations of 4-AP do not increase the inhibitory drive of cerebellar Purkinje cells. Therefore, 4-AP restores the severely diminished precision of pacemaking in Purkinje cells of episodic ataxia type two (EA2, channelopathie, affecting the PQ calcium channel-encoding gene,CACNA1A) mutant mice to normal level by prolonging and increasing the action potential after hyperpolarization [Alvin˜a and Khodakhah, 2010]. In other word, the main consequence of tg/tg chanelopathy is an increase of spike irregularity.</p>
<p>The recent electrophysiological findings encouraged German scientists to reconsider their previous findings by investigating the potential
effect of changing the cerebellar output of Purkinje cells (PC, class of GABAergic neurons located in the cerebellum which plays a fundamental role in controlling motor movement) regularity in the vestibular nuclei in a modelling study [Glasauer 2011]. Their first result was that only regularity together with synchrony would have a significant effect on the postsynaptic target neuron. But these results are incompatible with their system-level model, as well as with the current view on the nature of DBN.</p>
<p>Neither the population level model [Glasauer et al, 2008] nor the conductance-based model of a single neuron [Glasauer et al, 2011] appeared able to explain the electrophysiological findings.</p>
<p>The main assumption is that their model didn't give the correct results due to not taking into an account the effect of short-term depression (STD). In my findings, there is no significant difference in data with the presence or absence of STD. But difference in firing rate with the presence and absence of STD becomes significant for higher noise levels. I assume, that such a small difference in noise levels between wild-type and tg/tg could be explained taking into account the way, how known CV values for wild-type and tg/tg mice were converted to noise. Noise levels were obtained after building a dependency graph of CV form
noise.</p>
<p><strong>Date:</strong> 13/11/2015 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>