UH Biocomputation Group - Ohki Katakurahttp://biocomputation.herts.ac.uk/2024-02-19T17:14:00+00:00How the cerebellum recognises learnt and novel patterns: computational approach with a biologically detailed network model2024-02-19T17:14:00+00:002024-02-19T17:14:00+00:00Ohki Katakuratag:biocomputation.herts.ac.uk,2024-02-19:/2024/02/19/how-the-cerebellum-recognises-learnt-and-novel-patterns-computational-approach-with-a-biologically-detailed-network-model.html<p class="first last">Ohki Katakura's Journal Club session where he will talk about "How the cerebellum recognises learnt and novel patterns: computational approach with a biologically detailed network model".</p>
<p>On this week's Journal Club session, Ohki Katakura will talk about his work in the talk entitled "How the cerebellum recognises learnt and novel patterns: computational approach with a biologically detailed network model".</p>
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<p>The cerebellum is essential for motor control, timing and cognition. Although its anatomy
and physiology have been investigated, recent experimental studies raise new open
questions. These include how the granule cells process mossy fibre signals and how the
Purkinje cells code learnt and novel patterns. In this research, a detailed cerebellar
cortex network model was constructed by incorporating existing models [1,2] and
introducing long-term depression at granule cell-Purkinje cell synapses. The network
connectivity switches between the oscillatory and non-oscillatory activity states,
affecting the sparsity of activated granule cells. Pattern recognition criteria differed
across these states: in the oscillatory network, novel prompted longer pauses in Purkinje
cell spikes, while the non-oscillatory network responded with longer bursts. The number of
storable patterns in the network corresponds to the sparsity of activation.</p>
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<p>Papers:</p>
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<li>E. De, Schutter, J. Bower, <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>, 1994, Journal of Neurophysiology, 71, 375--400</li>
<li>S. Sudhakar, S. Hong, I. Raikov, R. Publio, C. Lang, T. Close, D. Guo, M. Negrello, E. 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> 2024/02/23 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: C258 & 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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<p>Papers:</p>
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<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>
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<p><strong>Date:</strong> 27/11/2020 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: online</p>
Revisiting a theory of cerebellar cortex2019-05-14T21:28:45+01:002019-05-14T21:28:45+01:00Ohki Katakuratag:biocomputation.herts.ac.uk,2019-05-14:/2019/05/14/revisiting-a-theory-of-cerebellar-cortex.html<p class="first last">Ohki Katakura's journal club session, where he presented the paper "<a class="reference external" href="https://doi.org/10.1016/j.neures.2019.03.001">Revisiting a theory of cerebellar cortex (Yamazaki & Lennon 2019 in press)</a>".</p>
<p>Ohki Katakura's journal club session, where he presented the paper "<a class="reference external" href="https://doi.org/10.1016/j.neures.2019.03.001">Revisiting a theory of cerebellar cortex (Yamazaki & Lennon 2019 in press)</a>".</p>
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<p>Long-term depression at parallel fiber-Purkinje cell synapses plays a principal role in learning in the cerebellum, which acts as a supervised learning machine. Recent experiments demonstrate various forms of synaptic plasticity at different sites within the cerebellum. In this article, we take into consideration synaptic plasticity at parallel fiber-molecular layer interneuron synapses as well as at parallel fiber-Purkinje cell synapses, and propose that the cerebellar cortex performs reinforcement learning, another form of learning that is more capable than supervised learning. We posit that through the use of reinforcement learning, the need for explicit teacher signals for learning in the cerebellum is eliminated; instead, learning can occur via responses from evaluative feedback. We demonstrate the learning capacity of cerebellar reinforcement learning using simple computer simulations of delay eyeblink conditioning and the cart-pole balancing task.</p>
<p><strong>Date:</strong> 03/05/2019 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D118</p>