UH Biocomputation Group - neurosciencehttp://biocomputation.herts.ac.uk/2019-11-21T10:14:04+00:00Efficient codes and balanced networks2019-11-21T10:14:04+00:002019-11-21T10:14:04+00:00Emil Dmitruktag:biocomputation.herts.ac.uk,2019-11-21:/2019/11/21/efficient-codes-and-balanced-networks.html<p class="first last">Samuel Sutton journal club session where he will talk about the paper "Efficient codes and balanced networks".</p>
<p>This week on Journal Club session Samuel Sutton will talk about the paper "Efficient codes and balanced networks".</p>
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<p>Recent years have seen a growing interest in inhibitory interneurons and their circuits. A striking property of cortical inhibition
is how tightly it balances excitation. Inhibitory currents not only match excitatory currents on average, but track them on a
millisecond time scale, whether they are caused by external stimuli or spontaneous fluctuations. We review, together with
experimental evidence, recent theoretical approaches that investigate the advantages of such tight balance for coding and
computation. These studies suggest a possible revision of the dominant view that neurons represent information with firing rates
corrupted by Poisson noise. Instead, tight excitatory/inhibitory balance may be a signature of a highly cooperative code, orders of
magnitude more precise than a Poisson rate code. Moreover, tight balance may provide a template that allows cortical neurons to
construct high-dimensional population codes and learn complex functions of their inputs.</p>
<p>Papers:</p>
<ul class="simple">
<li>Sophie Denève & Christian K Machens <a class="reference external" href="https://www.nature.com/articles/nn.4243">"Efficient codes and balanced networks"</a> Nat Neurosci 19, 375–382 (2016) doi:10.1038/nn.4243</li>
</ul>
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<p><strong>Date:</strong> 22/11/2019 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D449</p>
Clique topology reveals intrinsic geometric structure in neural correlations2019-10-30T17:04:05+00:002019-10-30T17:04:05+00:00Emil Dmitruktag:biocomputation.herts.ac.uk,2019-10-30:/2019/10/30/clique-topology-reveals-intrinsic-geometric-structure-in-neural-correlations.html<p class="first last">Shabnam Kadir's journal club session where she will present the paper "Clique topology reveals intrinsic geometric structure in neural correlations" (C. Giusti et al., 2015).</p>
<p>This week on Jurnal Club session Shabnam Kadir will present the paper
"Clique topology reveals intrinsic geometric structure in neural correlations",
by C. Giusti, E. Pastalkova, C. Curto and V. Itskov, Arxiv Prepr., pp. 1–29, 2015.</p>
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<p>Detecting meaningful structure in neural activity and connectivity data is
challenging in the presence of hidden nonlinearities, where traditional
eigenvalue-based methods may be misleading. We introduce a novel approach
to matrix analysis, called clique topology, that extracts features of the
data invariant under nonlinear monotone transformations. These features
can be used to detect both random and geometric structure, and depend only
on the relative ordering of matrix entries. We then analyzed the activity
of pyramidal neurons in rat hippocampus, recorded while the animal was
exploring a two-dimensional environment, and confirmed that our method
is able to detect geometric organization using only the intrinsic pattern
of neural correlations. Remarkably, we found similar results during
non-spatial behaviors such as wheel running and REM sleep. This suggests
that the geometric structure of correlations is shaped by the underlying
hippocampal circuits, and is not merely a consequence of position coding.
We propose that clique topology is a powerful new tool for matrix analysis
in biological settings, where the relationship of observed quantities to
more meaningful variables is often nonlinear and unknown.</p>
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<p><strong>Date:</strong> 01/11/2019 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D449</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>
Re-uptake of potassium by neurons and glial cells2019-10-14T11:38:08+01:002019-10-14T11:38:08+01:00Emil Dmitruktag:biocomputation.herts.ac.uk,2019-10-14:/2019/10/14/re-uptake-of-potassium-by-neurons-and-glial-cells.html<p class="first last">Reinoud Maex's journal club session where he will present the paper "Computer simulations of neuron-glia interactions mediated by ion flux (Somjen et al., 2008)".</p>
<p>This week on Jurnal Club session Reinoud Maex will present the paper <a class="reference external" href="https://link.springer.com/article/10.1007%2Fs10827-008-0083-9">"Computer simulations of neuron-glia interactions mediated by ion flux"</a>
, by Somjen, Kager and Wadman(2008) J. Comput. Neurosci. 25, 349-365.</p>
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<p>This paper resolves two questions that have bothered me since long:</p>
<ul class="simple">
<li>How can (neuronal or glial) potassium channels contribute to the re-uptake of potassium, hence how can the outward flow of potassium become an inward flow?</li>
<li>Why is the sodium-potassium pump electrogenic, hence why are three sodium ions exchanged for only two potassium ions?</li>
</ul>
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<p><strong>Date:</strong> 18/10/2019 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D449</p>
Modelling nicotine addiction2019-02-14T13:58:52+00:002019-02-14T13:58:52+00:00Rebecca Mikotag:biocomputation.herts.ac.uk,2019-02-14:/2019/02/14/modelling-nicotine-addiction.html<p class="first last">Reinoud Maex's journal club session, where he will summerise the work he did in Paris, which was sponsored by <a class="reference external" href="https://www.catalystbiosciences.com/">Targacept</a>: a pharmaceutical company which specialised in nicotinic compounds.</p>
<p>Reinoud Maex's journal club session, where he will summerise the work he did in Paris, which was sponsored by <a class="reference external" href="https://www.catalystbiosciences.com/">Targacept</a>: a pharmaceutical company which specialised in nicotinic compounds.</p>
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<p>Nicotine and nicotinic compounds bind to acetylcholine receptors, but, surprisingly, it is largely unknown whether they act by activating or by desensitising (= inactivating) these receptors.</p>
<p>We simulated a simple circuit of dopaminergic and gaba-ergic neurons, each of them expressing different subtypes of nicotinic receptors.</p>
<p>Our main conclusion was that many nicotinic compounds act by desensitising their receptors. We also formulated a new hypothesis on nicotine addiction. We propose that smoking in addicted people my be a form of self-medication: by desensitising the nicotinic receptors on GABAergic neurons, nicotine disinhibits the dopamine-neurons and restores their physiological response to acetylcholine.</p>
<p><strong>Date:</strong> 15/02/2019 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D118</p>
Learning in Cephalopod Brains2019-02-06T16:11:06+00:002019-02-06T16:11:06+00:00Rebecca Mikotag:biocomputation.herts.ac.uk,2019-02-06:/2019/02/06/learning-in-cephalopod-brains.html<p class="first last">Damien Drix's journal club session, where he will present an overview of learning in Caphalopod brains, while referencing various papers.</p>
<p>Damien Drix's journal club session, where he will present an overview of learning in Caphalopod brains, while referencing various papers.</p>
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<p>Cephalopods are very much unlike the familiar model organisms of mammalian neuroscience; yet they are emerging as a promising model organism in comparative neurobiology and in bio-inspired robotics. This talk will start with a general overview of cephalopod brains and behaviour. We will then focus on learning in the vertical lobe and compare it with the mushroom body of insects.</p>
<p>Damien will reference the following papers:</p>
<p>"<a class="reference external" href="https://www.journals.uchicago.edu/doi/10.2307/1542389">Computation in the learning system of cephalopods (J. Z. Young, 1991)</a>"</p>
<p>"<a class="reference external" href="https://www.bioscience.org/fbs/getfile.php?FileName=/2010/v2s/af/99/99.pdf">Functional and comparative assessments of the octopus learning and memory system (Binyamin Hochner, 2010)</a>"</p>
<p>"<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S096098221101013X">Alternative sites of synaptic plasticity in two homologous "fan-out fan-in" learning and memory networks (Tal Shomrat et al., 2011)</a>"</p>
<p>"<a class="reference external" href="https://ac.els-cdn.com/S0960982212010640/1-s2.0-S0960982212010640-main.pdf?_tid=b4ba6ec1-1f1e-4f0f-82a1-d78f8ae8966d&acdnat=1549470231_8bc7cd16b3d4d218fd870962517e5afc">An Embodied View of Octopus Neurobiology (Binyamin Hochner, 2012)</a>"</p>
<p><strong>Date:</strong> 08/02/2019 <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>
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<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>
NeuroFedora: a ready to use Free/Open source platform for neuroscientists2018-12-04T13:39:23+00:002018-12-04T13:39:23+00:00Ankur Sinhatag:biocomputation.herts.ac.uk,2018-12-04:/2018/12/04/neurofedora-a-ready-to-use-free-open-source-platform-for-neuroscientists.html<p class="first last">Ankur Sinha's journal club session on the <a class="reference external" href="https://fedoraproject.org/wiki/SIGs/NeuroFedora">NeuroFedora</a> initiative.</p>
<p>In this seminar, I introduce the <a class="reference external" href="https://fedoraproject.org/wiki/SIGs/NeuroFedora">NeuroFedora</a> initiative to the group. I
explain our goals, our philosophy, our methods, our current state and, solicit
feedback on our work.</p>
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<p>The (current) goal of the <a class="reference external" href="https://fedoraproject.org/wiki/SIGs/NeuroFedora">NeuroFedora SIG</a> is to provide a ready to use
platform for neuroscientists. We aim to do this by making commonly used
Neuroscience software easily installable on a <a class="reference external" href="https://getfedora.org">Fedora</a> Linux system.</p>
<p>Neuroscience is an extremely multidisciplinary field. It brings together
mathematicians, chemists, biologists, physicists, psychologists, engineers
(electrical and others), computer scientists, and more. A lot of software is
used nowadays in Neuroscience for:</p>
<ul class="simple">
<li>data collection, analysis, and sharing.</li>
<li>image processing (a lot of ML is used here, think Data Science).</li>
<li>simulation of brain networks (<a class="reference external" href="https://neuron.yale.edu/neuron/">NEURON</a>, <a class="reference external" href="https://nest-simulator.org">Nest</a>, <a class="reference external" href="https://github.com/BhallaLab/moose">Moose</a>, <a class="reference external" href="https://github.com/NeuralEnsemble/PyNN">PyNN</a>, <a class="reference external" href="http://briansimulator.org/">Brian</a>).</li>
<li>dissemination of scientific results (peer reviewed and otherwise, think
<a class="reference external" href="http://tug.org/">LaTeX</a>).</li>
</ul>
<p>Given that a large proportion of neuroscientists are not trained in computer
science, a lot of resources are spent setting up systems, installing software
(often building whole <a class="reference external" href="https://en.wikipedia.org/wiki/Dependency_hell">dependency chains</a> from source). This can be
especially hard for people not well-versed in software development and related
fields.</p>
<p>So, at <a class="reference external" href="https://fedoraproject.org/wiki/SIGs/NeuroFedora">NeuroFedora</a>, we aim to enable Neuroscience research by providing a
ready to use <a class="reference external" href="https://getfedora.org">Fedora</a> based system for researchers to work with. <a class="reference external" href="https://fedoraproject.org/wiki/SIGs/NeuroFedora">NeuroFedora</a> is
<a class="reference external" href="https://www.fsf.org/about/what-is-free-software">Free software</a> and is
therefore free to all to use, modify, study, and share.</p>
<p><strong>Date:</strong> 07/12/2018 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: D120</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>
Complexity of Biological Systems: Deterministic vs Stochastic Modelling2018-06-26T12:17:09+01:002018-06-26T12:17:09+01:00Rene te Boekhorsttag:biocomputation.herts.ac.uk,2018-06-26:/2018/06/26/complexity-of-biological-systems-deterministic-vs-stochastic-modelling.html<p class="first last">Rene te Boekhorst's journal club session where he discusses the complexity of biological systems and presents the paper "<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0022519305803431?via%3Dihub">A model of ion channel kinetics using deterministic chaotic rather than stochastic processes (Liebovitch and Toth, 1991)</a>".</p>
<p>Rene te Boekhorst's journal club session where he discusses the complexity of biological systems and presents the paper "<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0022519305803431?via%3Dihub">A model of ion channel kinetics using deterministic chaotic rather than stochastic processes (Liebovitch and Toth, 1991)</a>".</p>
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<p>In this talk, Rene will highlight some issues concerning the modelling of biological processes. The conventional approach to capture the complexity of biological systems is to focus on their unpredictability and to explain this in terms probability theory, i.e. as stochastic processes. Time series analysis and Markov Chains are popular tools for stochastic modelling and Rene will cover a few fundamental methodological aspects and assumptions of both formalisms. Rene will pay special attention to some terminological confusion concerning the probability distributions underlying Markov models.</p>
<p>If time permits, Rene will compare the results of a stochastic approach with a deterministic view of complexity, illustrated with a rather “heretic” application of chaos theory to model ion-channel kinetics by Liebovitch and Toth (1991).</p>
<p><strong>Date:</strong> 29/06/2018 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
Decoding gas source proximity from turbulent plumes2018-05-31T10:15:31+01:002018-05-31T10:15:31+01:00Michael Schmukertag:biocomputation.herts.ac.uk,2018-05-31:/2018/05/31/decoding-gas-source-proximity-from-turbulent-plumes.html<p class="first last">Michael Schmuker's journal club session on "Decoding gas source proximity from turbulent plumes".</p>
<p>Estimating the distance of a gas source is important in many applications of chemical sensing, like e.g. environmental monitoring, or chemically-guided robot navigation. If an estimation of the gas concentration at the source is available, source proximity can be estimated from the time-averaged gas concentration at the sensing site. However, in turbulent environments, where fast concentration fluctuations dominate, comparably long measurements are required to obtain a reliable estimate. A lesser known feature that correlates with source proximity in a turbulent environment is the temporal variance of local gas concentration: Gas encounters become more intermittent farther from the source. However, is has commonly been assumed that exploiting this feature requires gas concentration measurements at the millisecond scale, usually requiring expensive photo-ionisation detectors. We have recently shown that, with appropriate signal processing, off-the-shelf metal-oxide sensors are capable of extracting rapidly fluctuating features of gas plumes that strongly correlate with source distance [1]. We present a straightforward analysis method to decode events of large, consistent changes in the measured signal, which we denote ‘bouts’. The frequency of these bouts predicted the distance of a gas source in wind-tunnel experiments with good accuracy. In addition, we found that the variance of bout counts indicates cross-wind offset to the centre- line of the gas plume. Our results offer an alternative approach to estimating gas source proximity that is largely independent of gas concentration, using off-the-shelf metal-oxide sensors.</p>
<p>[1] Michael Schmuker, Viktor Bahr, and Ramón Huerta, “Exploiting Plume Structure to Decode Gas Source Distance Using Metal-Oxide Gas Sensors,” Sensors and Actuators B: Chemical 235 (November 2016): 636–46. Open access version available at <a class="reference external" href="https://arxiv.org/abs/1602.01815">https://arxiv.org/abs/1602.01815</a>.</p>
<p><strong>Date:</strong> 01/06/2018 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
A comparison of deterministic and stochastic ion channel representations in a model of a cerebellar nucleus neuron2018-05-21T14:55:04+01:002018-05-21T14:55:04+01:00Maria Psarroutag:biocomputation.herts.ac.uk,2018-05-21:/2018/05/21/a-comparison-of-deterministic-and-stochastic-ion-channel-representations-in-a-model-of-a-cerebellar-nucleus-neuron.html<p class="first last">Maria Psarrou's journal club session on 'A comparison of deterministic and stochastic ion channel representations in a model of a cerebellar nucleus neuron'.</p>
<p>Ion channels can either be modelled at a macroscopic level, using a deterministic representation such as the Hodgkin-Huxley formalism, or at a more detailed single-channel level, where their stochastic nature is taken into account by using a Markov formalism. The Hodgkin-Huxley model describes the combined collective effect of the channel population on the membrane potential, but it does not provide a comprehensive kinetic diagram. As a result, various aspects of the behaviour and consequently the functional role of individual channels can be overlooked. On the other hand, a more accurate alternative channel formalism is the Markov model. In Markov models, a single channel is represented by a kinetic scheme comprising a finite set of discrete intermediate states with probabilistic transitions from one state to another. Channel noise, introduced by the stochastic gating of the ion channels, can affect the generation and timing of action potentials and therefore potentially also single neuron computations.</p>
<p>In the present study, the voltage-gated channels of a morphologically realistic conductance based cerebellar nucleus (CN) neuron model were expressed as Markov formalisms and their behaviour was compared with their deterministic Hodgkin-Huxley type counterparts. Our results show that the majority of the deterministic CN channel models could easily be replaced by stochastic versions, without affecting neuronal behaviour. However, this was not the case for the fast sodium channel, where the parameter changes that had to be introduced in order to match the activity of the stochastic and deterministic models depended on the level of activation of the neuron, even for very small single channel conductances in the stochastic model.</p>
<p><strong>Date:</strong> 25/05/2018 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
A metric for consciousness, and the decline of the Hodgkin-Huxley theory2018-05-11T11:15:22+01:002018-05-11T11:15:22+01:00Reinoud Maextag:biocomputation.herts.ac.uk,2018-05-11:/2018/05/11/a-metric-for-consciousness-and-the-decline-of-the-hodgkin-huxley-theory.html<p class="first last">Reinoud Maex's journal club session on a metric for consciousness, and the decline of the Hodgkin-Huxley theory.</p>
<p>Having to wait a lot in railway stations, I will present two recent articles from Scientific American.</p>
<p>The first one (November 2017, written by Christof Koch) discusses a study by Casarotto et al. (Annals of Neurology 80, 718-729) where a metric of algorithmic complexity of an evoked EEG response is used to measure consciousness in coma patients and controls.</p>
<p>The second one (April 2018) discusses old work by Ichiji Tasaki (1960-1990s) and recent work by Thomas Heimburg claiming that action potential propagation is not an electrical but a mechanical phenomenon (a shock wave with piezo-electric side-effects).</p>
<p><strong>Date:</strong> 11/05/2018 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
The role of inhibition in structural plasticity of neural circuits2018-02-07T11:14:06+00:002018-02-07T11:14:06+00:00Ankur Sinhatag:biocomputation.herts.ac.uk,2018-02-07:/2018/02/07/the-role-of-inhibition-in-structural-plasticity-of-neural-circuits.html<p class="first last">Ankur Sinha's journal club session on the role of inhibition in
structural plasticity of neural circuits.</p>
<p>Activity dependent structural plasticity continuously reshapes cortical
circuits. While the reconfiguration of excitatory circuits is known to produce
functional changes, studies have recently shown that this is accompanied by the
simultaneous remodelling of inhibitory circuits also. Further, recent evidence
suggests that inhibition plays a critical role in the structural
reorganization of neural circuits.</p>
<p>In this session, I shall provide an overview of the observations documented in
the literature, discuss proposed mechanisms underlying structural plasticity,
and summarise questions that require further study.</p>
<p><strong>Date:</strong> 09/02/2018 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
An introduction to NEURON2017-05-11T10:05:33+01:002017-05-11T10:05:33+01:00Maria Psarroutag:biocomputation.herts.ac.uk,2017-05-11:/2017/05/11/an-introduction-to-neuron.html<p class="first last">Maria Psarrou's journal club session where she introduces the NEURON simulator.</p>
<p>Biological computational modelling is a powerful tool to simulate a system and draw conclusions regarding its function. It also allows to make predictions for processes that still haven’t been investigated in the laboratory. NEURON [1, 2] is an simulation environment, where empirical data are combined with analytic mathematical expressions, in order to model single neurons or neural networks. Neuronal cells are created as a series of connected sections, able to form realistic morphologies, and where different membrane properties (ionic, synaptic and passive) can be inserted. The interface and programming syntax are designed to offer an intuitive environment and emphasise on the biological functions in detail, rather than the the programming or numerical methods.
The purpose of this workshop is to give an introduction to the NEURON software and how it could be used, by building a simple neuronal cell model and testing its behaviour under different conditions.</p>
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<ol class="arabic simple">
<li>Carnevale, N.T. and Hines, M.L. The NEURON Book. Cambridge, UK: Cambridge University Press, 2006.</li>
<li>NEURON for empirically-based simulations of neurons and networks of neurons (2017). [online] Available at: <a class="reference external" href="https://www.neuron.yale.edu/">https://www.neuron.yale.edu/</a></li>
</ol>
<p><strong>Date:</strong> 12/05/2017 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
Loss of sensory input causes rapid structural changes of inhibitory neurons in adult mouse visual cortex2017-04-24T11:17:31+01:002017-04-24T11:17:31+01:00Ankur Sinhatag:biocomputation.herts.ac.uk,2017-04-24:/2017/04/24/loss-of-sensory-input-causes-rapid-structural-changes-of-inhibitory-neurons-in-adult-mouse-visual-cortex.html<p class="first last">Ankur Sinha's journal club session where he discusses the paper, "<a class="reference external" href="http://www.sciencedirect.com/science/article/pii/S0896627311005642">Loss of sensory input causes rapid structural changes of inhibitory neurons in adult mouse visual cortex (Keck et al. (2011))</a>".</p>
<p>Ankur Sinha's journal club session where he discusses the paper, "<a class="reference external" href="http://www.sciencedirect.com/science/article/pii/S0896627311005642">Loss of sensory input causes rapid structural changes of inhibitory neurons in adult mouse visual cortex (Keck et al. (2011))</a>".</p>
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<p>A fundamental property of neuronal circuits is the ability to adapt to altered sensory inputs. It is well established that the functional synaptic changes underlying this adaptation are reflected by structural modifications in excitatory neurons. In contrast, the degree to which structural plasticity in inhibitory neurons accompanies functional changes is less clear. Here, we use two-photon imaging to monitor the fine structure of inhibitory neurons in mouse visual cortex after deprivation induced by retinal lesions. We find that a subset of inhibitory neurons carry dendritic spines, which form glutamatergic synapses. Removal of visual input correlates with a rapid and lasting reduction in the number of inhibitory cell spines. Similar to the effects seen for dendritic spines, the number of inhibitory neuron boutons dropped sharply after retinal lesions. Together, these data suggest that structural changes in inhibitory neurons may precede structural changes in excitatory circuitry, which ultimately result in functional adaptation following sensory deprivation.</p>
<p><strong>Date:</strong> 28/04/2017 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
Could a neuroscientist understand a microprocessor?2016-06-09T17:20:32+01:002016-06-09T17:20:32+01:00Christoph Metznertag:biocomputation.herts.ac.uk,2016-06-09:/2016/06/09/could-a-neuroscientist-understand-a-microprocessor.html<p class="first last">Christoph Metzner's journal club session where he discusses the paper, <a class="reference external" href="http://www.biorxiv.org/content/early/2016/05/26/055624.abstract">'Could a neuroscientist understand a microprocessor?'</a></p>
<p>There is a popular belief in neuroscience that we are primarily data
limited, that producing large, multimodal, and complex datasets will,
enabled by data analysis algorithms, lead to fundamental insights into
the way the brain processes information. Microprocessors are among
those artificial information processing systems that are both complex
and that we understand at all levels, from the overall logical flow,
via logical gates, to the dynamics of transistors. Here we take a
simulated classical microprocessor as a model organism, and use our
ability to perform arbitrary experiments on it to see if popular data
analysis methods from neuroscience can elucidate the way it processes
information. We show that the approaches reveal interesting structure
in the data but do not meaningfully describe the hierarchy of
information processing in the processor. This suggests that current
approaches in neuroscience may fall short of producing meaningful
models of the brain.</p>
<p>The complete paper can be found here: <a class="reference external" href="http://www.biorxiv.org/content/early/2016/05/26/055624.abstract">http://www.biorxiv.org/content/early/2016/05/26/055624.abstract</a></p>
<p><strong>Date:</strong> 10/06/2016 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>