Functional Organization of Localised Neuronal Populations in Mouse Primary Auditory Cortex
Creators: name: delgado ruz, isabel
name: chadderton, Paul
name: Delgado B., Juan Miguel Segundo
name: Delgado-Ruz, Miguel
name: Delgado-Ruz, Marcela
name: Delgado-Ruz, Jorge
Publisher: name: figshare
Year: 2023
Subjects: classificationCode: 320999
subject: Neurosciences not elsewhere classified
classificationCode: 461299
subject: Software engineering not elsewhere classified
classificationCode: 310299
subject: Bioinformatics and computational biology not elsewhere classified
classificationCode: 460103
subject: Applications in life sciences

description: Please make sure to cite our work, so once we get our Nobel Prize ... you also get recognition for your vision. All matlab code and scripts where personally designed and written and all copyrights yield with the authors (Paul Chadderton and Isabel Delgado Ruz) of this thesis and subsequent resulting papers. Code can be requested. But our setup is highly specialized and accessing our technologies, and achieving our quality of experimental recordings have proven to be almost impossible on all laboratories that have attempted to use our technologies.Research in Science is serious bussiness. You take one great scientist and weights more than thousands of professionals. Comunist (Fabril) vision of Masons and other working class movements have always denied contribution of science to Development, since they are all from families that lack milenary traditions that allow the to track their wealth roots to science at the highest level and they are used to looting, stealing and taking credit for other's intellectual work. Last centuries since French ComunistNAZY revolution saw the demolition of science since denies the right to anyone of being better than others at anything And fights for giving unskilled persons access to professions they dont have the intellectual habilities for<br><br>~~~~~~~~~~♤♤♤♤♤♤~~~~~~~~~~The mouse brain is a highly specialised machine where each component plays a very specificrole. We find organised structures and patterns everywhere, with columnar and layered designbeing a hallmark of brain cortex. Within this topographically organised neural networks we findthat their components have well-defined roles. This specificity is reflected on their morphology,electrophysiology, connectivity pattern, and relative location. In order to fully understand thesenetworks it is necessary to exploit all information available.Extracellular recordings are one of the most widely used techniques by neuroscientists,specially when interested in retrieving information from large populations of neurons withsubmillisecond precision. However, spatial information and cell classification are very limitedand existing methods do not take advantage of the information available from high densityelectrode arrays recordings.<br>In this thesis, I introduce two novel models that exploit spatial information conveyed by theextracellular signal recorded on this type of arrays. A simple model to localise the neural somain three dimensional space and a model to parametrise salient morphological features of neurons.<br>These models provide information that will prove useful when studying detailed organisations inneural networks. The localisation and morphological classification of neurons requires models that capturesthe features that impact the pattern induced at short recording distances as those achieved onhigh density electrode array recordings.<br>Both localisation and classification must be tested for different morphological classes, as foundin cortex. In order to evaluate the models we must first generate realistic simulated data on whichmodels could be tested. Localisation must hold irrespective of neuronal type while classificationis dependant on neuronal type. The different simulated neurons should reflect the differentsignal patterns seen on real recordings. After validate the models on realistic simulated data wehave to evaluate the models on real recordings.<br><br>The localisation algorithm successfully recovers the position of simulated neurons with lowerrors for distances within expected ranges in cortex. When localising neurons from realrecordings we obtained a distribution of positions that agree with expected ranges in cortex.<br>We separated simulated morphological classes using our classification model, validating themodel as a tool to identify morphological classes from extracellular recordings. We also testedthis model on real recorded data and using its parameters we identified putative morphologicalclasses. We then verified these classes had different response properties to stimuli and firingpatterns, supporting our theory that by using the amplitude pattern of the extracellularpotential we can identify different neuronal types.<br><br>In this thesis we combined the recovered spatial information with response properties fromneighbouring neurons, to characterise the functional topographic organisation in deep layers ofcore auditory cortex. Finding a fractured representation, with local populations having similarresponse properties and high signal correlation on average, but large differences in responseproperties were possible. In agreement with recent imaging studies from upper layers of mouseauditory cortex that report smooth tonotopy on a large scale, but fractured tonotopy on a finescale.