UH Biocomputation Group - Purkinje cellhttp://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>
Computational 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>
<hr class="docutils" />
<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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<div class="line"><br /></div>
</div>
<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>
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>
Neuronal modelling of cerebellar Purkinje cell2019-10-23T10:04:59+01:002019-10-23T10:04:59+01:00Emil Dmitruktag:biocomputation.herts.ac.uk,2019-10-23:/2019/10/23/neuronal-modelling-of-cerebellar-purkinje-cell.html<p class="first last">Ohki Katakura's journal club session where he will talk about neuronal modelling of cerebellar Purkinje cell while referencing various papers.</p>
<p>This week on Journal Club session Ohki Katakura's will talk about neuronal modelling of cerebellar Purkinje cell while referencing various papers.</p>
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<p>Cerebellar Purkinje cell is the hugest and the most complicated neuron
in the cerebellum. As well as cerebellar cortex network, some
researchers think that the cell has rich computational capacity because
of its complexity. Based on experimental studies, it was realistically
modelled in 1994 (De Schutter & Bower, 1994ab), and then the model has
been recently updated (Masoli et al., 2015; Zang et al., 2018). In this
session, I will introduce details of the models (i.e., morphology and
embedded ion channels) and the differences between all three of them.</p>
<p>Papers:</p>
<ul class="simple">
<li>De Schutter, E., Bower, J.M., 1994a. <a class="reference external" href="https://doi.org/10.1152/jn.1994.71.1.375">"An active membrane model of the
cerebellar Purkinje cell. I. Simulation of current clamps in slice"</a>.
Journal of Neurophysiology 71, 375–400.</li>
<li>De Schutter, E., Bower, J.M., 1994b. <a class="reference external" href="https://doi.org/10.1152/jn.1994.71.1.401">"An active membrane model of the
cerebellar Purkinje cell II. Simulation of synaptic responses"</a>. Journal
of Neurophysiology 71, 401–419.</li>
<li>Masoli, S., Solinas, S., D’Angelo, E., 2015. <a class="reference external" href="https://doi.org/10.3389/fncel.2015.00047">"Action potential
processing in a detailed Purkinje cell model reveals a critical role for
axonal compartmentalization"</a>. Front. Cell. Neurosci. 9.</li>
<li>Zang, Y., Dieudonné, S., De Schutter, E., 2018. <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>. Cell Reports
24, 1536–1549.</li>
</ul>
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<p><strong>Date:</strong> 25/10/2019 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D449</p>