Nicholas Vaughan Swindale

 
Prospective Graduate Students / Postdocs

This faculty member is currently not actively recruiting graduate students or Postdoctoral Fellows, but might consider co-supervision together with another faculty member.

Professor

Research Classification

Neuronal Modeling
Neuronal Systems
Eye and Visual System Diseases
Visual System

Relevant Degree Programs

 

Research Methodology

anesthesia, cats, etracellular recording, stereotaxis

Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - May 2019)
Characterizing single neuron activity patterns and dynamics using multi-scale spontaneous neuronal activity recordings of cat and mouse cortex (2018)

Throughout most of the 20th century the brain has been studied as a reflexive system with ever improving recording methods being applied within a variety of sensory and behavioural paradigms.Yet the brains of most animals (and all mammals) are spontaneously active with incoming sensory stimuli modulating rather than driving neural activity.The aim of this thesis is to characterize spontaneous neural activity across multiple temporal and spatial scales relying on biophysical simulations, experiments and analysis of recordings from the visual cortex of cats and dorsal cortex and thalamus of mouse.Biophysically detailed simulations yielded novel datasets for testing spike sorting algorithms which are critical for isolating single neuron activity. Sorting algorithms tested provided low error rates with operator skill being as important as sorting suite. Simulated datasets have similar characteristics to in vivo acquired data and ongoing larger-scope efforts are proposed for developing the next generation of spike sorting algorithms and extracellular probes.Single neuron spontaneous activity was correlated to dorsal cortex neural activity in mice. Spike-triggered-maps revealed that spontaneously firing cortical neurons were co-activated with homotopic and mono-synaptically connected cortical areas, whereas thalamic neurons co-activatedwith more diversely connected areas. Both bursting and tonic firing modes yielded similar maps and the time courses of spike-triggered-maps revealed distinct patterns suggesting such dynamics may constitute intrinsic single neuron properties. The mapping technique extends previous work tofurther link spontaneous neural activity across temporal and spatial scales and suggests additional avenues of investigation.Synchronized state cat visual and mouse sensory cortex electrophysiological recordings revealed that spontaneously occurring activity UP-state transitions fall into stereotyped classes of events that can be grouped. Single visual cortex neurons active during UP-state transitions fire in a partially preserved order extending previous findings on high firing rate neurons in rat somatosensory and auditory cortex. The firing order for many neurons changes over periods longer than 30-minutes suggesting a complex non-stationary temporal neural code may underlie spontaneous and stimulusevoked neural activity.This thesis shows that ongoing spontaneous brain activity contains substantial structure that can be used to further our understanding of brain function.

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Characterizing patches of primary visual cortex with minimal bias (2015)

The brain is highly complex, and studying it requires simplifying experiments, analyses, and theories. New techniques can capture more of the brain's complexity while reducing biases in our understanding of how it works. This thesis describes experiments in primary visual cortex of anesthetized cat, using high-density silicon multisite electrodes to simultaneously record from as many neurons as possible across all cortical layers, thereby characterizing local cortical populations with minimal bias. Recordings were maintained for many hours at a time, and included both spontaneous and stimulus-evoked periods, with a wide variety of naturalistic and artificial visual stimuli. A new "divide-and-conquer" spike sorting method translated correlated multisite voltages into action potentials of spatially localized, isolated neurons. This method tracked neurons over periods of many hours despite drift, and distinguished neurons with firing rates
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From synaptogenesis to map formation - modeling visual system development exploring the contribution of cellular mechanisms to the emergence of retinotopic projections and eye-specific segregation (2009)

The brain is composed of many anatomically distinct areas that control different functions. Acommon feature of these areas is that information is represented in a spatially organized manner. Inthe visual system, retinal representation is spatially mapped onto visual areas such that neighboringneurons respond to adjacent retinal locations, forming a retinotopic map. When axons from tworetinas project to the same target structure, both produce similar retinotopic projections on thelarge scale but these segregate into eye-specific domains locally. How these spatial representationsare formed is not well understood. Experimental studies have shown that many mechanisms areinvolved.Several modeling studies have addressed how such organization arises, with most representingdifferent varying subsets of the mechanisms known to be present and showing how the particularrepresentation of mechanisms can produce the emergent properties of organization. This resultsin models producing similar outputs yet coming to different conclusions that often cannot be reconciled.By omitting behaviors that are present and likely to be involved in organization, suchas spiking neurons and the dynamics of axon and synapse growth and retraction, the models arepoorly constrained. This limits their explanatory and predictive scope regarding how organizationdevelops, and further limits their ability to examine how the different mechanisms interact.To more accurately analyze both how such organization develops and the interactions betweenunderlying mechanisms, a model of the developing retinocollicular pathway was produced thatrepresented a wide range of cellular and subcellular phenomena, including spike-timing dependentplasticity (STDP), chemoaffinity, spontaneous retinal activity, trophic factors, and growth andretraction of synapses and axons. The model demonstrated retinotopic refinement and eye-specificsegregation across a wide range of parameters and variations in implementation. Results indicatedthat the mechanisms necessary for organization were chemoaffinity, retinal waves, trophic factorsand homeostatic controls. Analysis of the relative roles of activity and chemoaffinity suggested thatthese mechanisms play distinct and complementary roles. Among the predictions of the model arethat smaller synapses produce more refined projections and, surprisingly, that STDP does not playa significant role in organization.

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Master's Student Supervision (2010 - 2018)
How well does a linear model predict the responses of primary visual cortex neurons to a natural scene stimulus? (2011)

The goal was to test how well a linear model of the responses of neurons in area 18 of cat visual cortex, derived from recordings made in anaesthetized adult cats, predicts responses to natural scene stimuli. Methods: Estimates of the spatio-temporal receptive field profile of the neurons were obtained by reverse correlation to an m-sequence stimulus (Reid et al., 1997). The receptive field estimate, together with a non-linear response function, was then used to give the expected probability, or rate, of spike firing (Chichilnisky, 2001; Ringach & Malone, 2007) during a time-varying natural scene stimulus. The ability of the model to describe the responses was assessed by computing the correlation coefficient between the rates predicted by the model and those observed during stimulation with a natural scene (Willmore & Smyth, David & Gallant, 2005). For each LN functional model identified for all real A18 neurons using m-sequence responses, a Poisson spike generator was added (Heeger, 2000) to simulate ‘LNP’ responses to m-sequence and natural scene stimuli, and was used to assess the statistical significance of the results.Results: The LN model, with parameters derived from responses to m-sequence stimuli, was able to predict responses to m-sequence stimuli with fairly high reliability (correlation coefficients in the range 0.84 – 0.96). However the model was only able to weakly predict responses to natural scene stimuli. This result was confirmed by comparing the correlation coefficients between predicted and observed firing rates obtained for actual and for simulated responses to the natural scene stimulus; values ranged from 0.14 to 0.59, in marked contrast to the simulated ones ranging from 0.47 to 0.88. Reasons for the inability of the LNP model to predict responses to natural scene stimuli are discussed.

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