UH Biocomputation Group - Electrode positioninghttp://biocomputation.herts.ac.uk/2020-11-18T10:58:33+00:00Prediction of Electrode Position inside rat brain using NeuromorphicHardware2020-11-18T10:58:33+00:002020-11-18T10:58:33+00:00Emil Dmitruktag:biocomputation.herts.ac.uk,2020-11-18:/2020/11/18/prediction-of-electrode-position-inside-rat-brain-using-neuromorphichardware.html<p class="first last">Shavika Rastogi's Journal Club session where she will talk about her master thesis entitled "Prediction of Electrode Position inside rat brain using Neuromorphic Hardware".</p>
<p>This week on Journal Club session Shavika Rastogi will talk about her master thesis entitled "Prediction of Electrode Position inside rat brain using Neuromorphic Hardware".</p>
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<p>Neural probes with large number of close packed recording sites each comprising
of 32 electrodes are being developed for large scale neuronal recordings from
multiple brain areas simultaneously to understand complex brain activity in vivo.
By precisely mapping the position of each site inside rat brain, they help us to
characterize neural activity on the basis of cortical depth from where it is
obtained. Their application lies in neurosurgery, where it is important to locate
the target of surgical interest inside the brain in real time. In this work, we
have first compared various methods from literature to analyze extracellular
activity recorded using CMOS neural probes from different cortical depths and
from different locations along same lateral axis of rat brain to find a criterion
on the basis of which recordings can be classified. After finding out the most
promising criterion, we have tried to implement it on neuromorphic hardware
SpiNNaker. We tested single neuron and spiking excitatory-inhibitory network
for implementation and found that excitatory-inhibitory network is more robust to
noise present in signal and its output can be improved by introducing lateral
inhibition. Our results show that SpiNNaker can be used for rough indication of
cortical depth.</p>
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<p><strong>Date:</strong> 20/11/2020 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: online</p>