UH Biocomputation Group - STDPhttp://biocomputation.herts.ac.uk/2022-11-28T10:49:06+00:00Encoding and retrieval in a model of the hippocampal CA1 microcircuit2022-11-28T10:49:06+00:002022-11-28T10:49:06+00:00Eleonora Bernasconitag:biocomputation.herts.ac.uk,2022-11-28:/2022/11/28/encoding-and-retrieval-in-a-model-of-the-hippocampal-ca1-microcircuit.html<p class="first last">Eleonora Bernasconi's Journal Club session where he will talk about a paper "Encoding and retrieval in a model of the hippocampal CA1 microcircuit"</p>
<p>This week on Journal Club session Eleonora Bernasconi will talk about a paper "Encoding and retrieval in a model of the hippocampal CA1 microcircuit".</p>
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<p>It has been proposed that the hippocampal theta rhythm (4- 7 Hz) can
contribute to memory formation by separating encoding (storage) and retrieval
of memories into different functional half- cycles (Hasselmo et al. (2002)
Neural Comput 14:793- 817). We investigate, via computer simulations,
the biophysical mechanisms by which storage and recall of spatio-temporal input
patterns are achieved by the CA1 microcircuitry. A model of the CA1
microcircuit is presented that uses biophysical representations of the major
cell types, including pyramidal (P) cells and four types of inhibitory
interneurons: basket (B) cells, axo-axonic (AA) cells, bistratified (BS) cells
and oriens lacunosum-moleculare (OLM) cells. Inputs to the network come from
the entorhinal cortex (EC), the CA3 Schaffer collaterals and medial septum. The
EC input provides the sensory information, whereas all other inputs provide
context and timing information. Septal input provides timing information for
phasing storage and recall. Storage is accomplished via a local STDP mediated
hetero-association of the EC input pattern and the incoming CA3 input pattern
on the CA1 pyramidal cell target synapses. The model simulates the timing of
firing of different hippocampal cell types relative to the theta rhythm in
anesthetized animals and proposes experimentally confirmed functional roles for
the different classes of inhibitory interneurons in the storage and recall
cycles (Klausberger et al., (2003, 2004) Nature 421:844- 848, Nat
Neurosci 7:41- 47). Measures of recall performance of new and
previously stored input patterns in the presence or absence of various
inhibitory interneurons are employed to quantitatively test the performance of
our model. Finally, the mean recall quality of the CA1 microcircuit is tested
as the number of stored patterns is increased.</p>
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<p>Papers:</p>
<ul class="simple">
<li>V. Cutsuridis, S. Cobb, B. Graham, <a class="reference external" href="https://doi.org/10.1002/hipo.20661">"Encoding and retrieval in a model of the hippocampal CA1 microcircuit"</a>, 2010, Hippocampus, 20, 423--446</li>
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<p><strong>Date:</strong> 2022/12/02 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
Multi-contact synapses for stable networks: a spike-timing dependent model of dendritic spine plasticity and turnover2017-02-20T14:22:35+00:002017-02-20T14:22:35+00:00Ankur Sinhatag:biocomputation.herts.ac.uk,2017-02-20:/2017/02/20/multi-contact-synapses-for-stable-networks-a-spike-timing-dependent-model-of-dendritic-spine-plasticity-and-turnover.html<p class="first last">Ankur Sinha's journal club session where he discusses the pre-print paper, "<a class="reference external" href="https://arxiv.org/abs/1609.05730">Multi-contact synapses for stable networks: a spike-timing dependent model of dendritic spine plasticity and turnover (Deger, M., Seeholzer, A., Gerstner, W. (2016))</a>"</p>
<p>Ankur Sinha's journal club session where he discusses the pre-print paper, "<a class="reference external" href="https://arxiv.org/abs/1609.05730">Multi-contact synapses for stable networks: a spike-timing dependent model of dendritic spine plasticity and turnover (Deger, M., Seeholzer, A., Gerstner, W. (2016))</a>"</p>
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<p>Excitatory synaptic connections in the adult neocortex consist of multiple synaptic contacts, almost exclusively formed on dendritic spines. Changes of dendritic spine shape and volume, a correlate of synaptic strength, can be tracked in vivo for weeks. Here, we present a combined model of spike-timing dependent dendritic spine plasticity and turnover that explains the steady state multi-contact configuration of synapses in adult neocortical networks. In this model, many presynaptic neurons compete to make strong synaptic connections onto postsynaptic neurons, while the synaptic contacts comprising each connection cooperate via postsynaptic firing. We demonstrate that the model is consistent with experimentally observed long-term dendritic spine dynamics under steady-state and lesion induced conditions, and show that cooperation of multiple synaptic contacts is crucial for stable, long-term synaptic memories. In simulations of a simplified network of barrel cortex, our plasticity rule reproduces whisker-trimming induced rewiring of thalamo-cortical and recurrent synaptic connectivity on realistic time scales.</p>
<p><strong>Date:</strong> 25/02/2017 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>