UH Biocomputation Group - Dendritic computationhttp://biocomputation.herts.ac.uk/2016-11-10T10:15:13+00:00Neuronal computation: dendrites at work2016-11-10T10:15:13+00:002016-11-10T10:15:13+00:00Benjamin Torben-Nielsentag:biocomputation.herts.ac.uk,2016-11-10:/2016/11/10/neuronal-computation-dendrites-at-work.html<p class="first last">Benjamin Torben-Nielsen's journal club session on dendritic computation.</p>
<p>Brain dynamics emerge from the collective and orchestrated activity of single neurons. The main characteristics of neurons are their morphologically elaborate structures to receive and integrate inputs (i.e., the dendrites) and communicate their signal to other neurons (i.e., the axons). Because dendrites receive, integrate and transform inputs into relevant output they can be considered as the functional workhorses of the brain.</p>
<p>In this presentation, I’ll outline the problematic relation between dendrite structure and function in neurons. Then I’ll show how highly non-trivial processing can take place in neurons due to the spatial extend of dendrites. Lastly, I will present my current work on distilling the essence of dendritic computations using simplified models.</p>
<p><strong>Date:</strong> 11/11/2016 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
Open Position: PhD studentship in Computational Neuroscience2016-04-28T23:57:17+01:002016-04-28T23:57:17+01:00Volker Steubertag:biocomputation.herts.ac.uk,2016-04-28:/2016/04/28/open-position-phd-studentship-in-computational-neuroscience.html<p class="first last">A funded PhD position at the Biocomputation group is available. The shortlisting process begins 30 May, 2016. Details within.</p>
<!-- *This position has been filled.* -->
<hr class="docutils" />
<p>We welcome applications for a funded PhD position in the Biocomputation Research Group at the University of Hertfordshire.</p>
<p>The successful applicant will work on a project related to the detailed modelling of neuronal dynamics arising through dendritic processing and/or the analysis of morphological and circuit data. Potential projects in the fields of neuroinformatics and computational neuroscience include (but are not limited to):</p>
<ul class="simple">
<li>Analysis of neuronal morphology and micro-circuitry.</li>
<li>Simulation of development of neuronal morphologies and tissues.</li>
<li>Simulation of dendritic processing on hardware.</li>
<li>Sensory processing and behaviour generation in individual invertebrate neurons.</li>
<li>Development of experimental robot controllers based on dendritic computation.</li>
<li>Structural plasticity at the single neuron and micro-circuitry level.</li>
</ul>
<p>More project ideas can be found here: <a class="reference external" href="http://www.dendrites.club/Positions.html">http://www.dendrites.club/Positions.html</a></p>
<p>The successful candidate will have extensive programming experience, preferably in Python (and/or other programming languages depending on the precise project). Depending on the project, experience with parallel programming (MPI, ZMQ), meshing software (VTK, CGAL, ITK, ...), or statistical analysis in R or Python are an advantage. In addition, we greatly value curiosity and a personal motivation to find out how things work.</p>
<p>We collaborate closely with leading experimentalists and theoreticians all over the world, such as Prof. Adrian Moore (RIKEN, Japan), Prof. Erik De Schutter (OIST, Japan) and Dr. Marylka Uusisaari (Erasmus Medical Center Rotterdam, The Netherlands).</p>
<p>The student will be based in the <a class="reference external" href="http://biocomputation.herts.ac.uk">Biocomputation Group</a> at the University of Hertfordshire and will be supervised by Drs. Ben Torben-Nielsen (b.torben-nielsen at herts.ac.uk) and Volker Steuber (v.steuber at herts.ac.uk) to whom informal enquiries can be sent.</p>
<p>Successful candidates are eligible for a research studentship award from the University (approximately GBP 14,250 per annum bursary plus the payment of the student fees). Application forms can be obtained from:</p>
<p>Mrs Lorraine Nicholls, <br />
Research Student Administrator, <br />
STRI, <br />
University of Hertfordshire, <br />
College Lane, <br />
Hatfield, Herts, <br />
AL10 9AB, <br />
Tel: +44 01707 286083, <br />
l.nicholls @ herts.ac.uk.</p>
<p>The short-listing process will begin on 30 May, 2016.</p>
Dendritic Computation2016-03-30T18:54:31+01:002016-03-30T18:54:31+01:00Ankur Sinhatag:biocomputation.herts.ac.uk,2016-03-30:/2016/03/30/dendritic-computation.html<p class="first last">Ankur Sinha's journal club session where he speaks about dendritic computation.</p>
<p>Ankur Sinha's journal club session where he speaks about dendritic computation.</p>
<p><strong>Date:</strong> 01/04/2016 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
Capturing dendritic computation with the Green’s function formalism2016-01-06T14:44:56+00:002016-01-06T14:44:56+00:00Willem Wybotag:biocomputation.herts.ac.uk,2016-01-06:/2016/01/06/capturing-dendritic-computation-with-the-green-s-function-formalism.html<p class="first last">Special journal club session - Willem Wybo from the Human Brain Project at EPFL will speak about his work on enhancing the Green's function formalism to study neuronal integration, dendritic compartmentalization and interactions between synaptic inputs.</p>
<p>Neurons are spatially extended structures with an elaborate dendritic tree that integrates spatio-temporal input patterns. To model this integration, researchers have relied on compartmental simulations of the cable equation, where space is discretized in many equipotential compartments. However, this approach is computationally expensive and obfuscates the interactions among distant synapses .</p>
<p>The Green's function (GF) formalism can potentially solve these issues: it is conceptually simple, as the complicated spatio-temporal interactions are captured in a system of temporal kernels, and provides a simulation paradigm that scales independently of the morphological complexity. However, the computational cost scales quadratically in the number of inputs. Historically, the GF formalism was abandoned because of this reason, along with the fact that it required computationally costly convolutions and the belief that is was restricted to linear membranes.</p>
<p>In this talk, I will show how all aforementioned pitfalls can be circumvented. First, I will outline how the system can be transformed so that it scales linearly in the number of input locations. Second, I will show that convolutions can be re-expressed as simple differential equations. Third, I will discuss the inclusion of non-linear membrane currents and related, how this 'sparsified' GF formalism reduces to the canonically used 2nd order finite difference approximation.</p>
<p>I will also briefly outline how this new simulation algorithm for neurons can be implemented and discuss possible use cases in which computational advantage over the classic methods is expected.</p>
<p><strong>Date:</strong> 15/01/2016 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>