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Current research

I’m currently a post-doc in Rufin VanRullen’s lab, working on a computer model of oscillatory network computations. Specifically, I’m interested in the interplay between oscillations at different frequencies (e.g. theta and gamma). The model is designed to test the implementation of two influential theories :

  • John Lisman’s theory of the sequential activation of neuron groups representing items, with each item constrained to a single gamma cycle, and the sequence of items set across the theta cycle (it’s recognised that more strongly driven neurons will be activated at an earlier phase of the theta cycle, thus this is a convenient way of controlling item order by saliency).
  • Pascal Fries’ theory of communication-through-coherence, whereby communication of the activity of a group of neurons in a lower level to a higher level is facilitated by matching the phase of gamma oscillations in the two layers. In effect, the higher level can be made to ’listen’ to one or other group of neurons in the lower level simply by manipulating the phase of the gamma oscillations. In this case, the role of the slower oscillation (theta or alpha) is to periodically break the locking of activity between layers, allowing the system to explore the different items being represented in the lower level.

Previous Research

For my DPhil, in Dr Ole Paulsen’s lab at the University of Oxford (at the time), I studied the responses of hippocampal pyramidal cells in vitro, using whole-cell patch clamp and dynamic clamp. We were interested in the computations performed by cells receiving oscillatory input (of physiologically relevant amplitude and frequency). Consistent with theoretical predictions, these cells perform a systematic transformation from the level of excitatory input (DC) to the phase of firing relative to a theta frequency oscillation (a rate-to-phase transform). This was not true for a gamma frequency oscillation. The experimental work was extended by single-cell modelling and an analytical description of the system, showing that this kind of behaviour is very generalisable (it is not some special property of hippocampal pyramidal cells, rather the simplest integrate-and-fire model shows this kind of behaviour), and setting out a framework for understanding the parameter ranges for which it’s valid, and for the effects of basic cell properties on the behaviour (membrane time constant, oscillation frequency and amplitude...). Finally, I used an information theoretic analysis to show that, in our experimental data, the phase of firing carried more information about the level of DC input than did the rate of firing.

More recently, I worked as a post-doc in Dr Wyeth Bair’s lab at the University of Oxford, combining single-unit recording in the early visual system in vivo with network modelling. Broadly we looked at adaptive neural computations, with my work following two strands

  • Neural responses both during and after the presentation of prolonged static stimuli (thus, presumably, the responses underlying the perceptual phenomena of visual fading and negative afterimages). Here we were looking at adaptation on a long time scale, with both responses and after-responses in V1 decaying with a time constant on the order of seconds, and in the LGN, on the order of tens of seconds.
  • Adaptive computation in the motion pathway. Previously, Wyeth had shown that direction selective cells in V1 (and in MT) change their temporal integration as a function of stimulus statistics, such that a shorter time window of integration is implemented for a fast-moving stimulus (and vice versa), termed Adaptive Temporal Integration (ATI). This relates to a larger body of work describing a change in neural processing (both gain and temporal integration) as a function of stimulus statistics, that is phenomenologically similar across systems (typically, as stimulus variance increases, both gain and time window of integration decrease, described with respect to visual contrast and motion, and also in the auditory system). We used a novel experimental paradigm using a combination of differently oriented stimulus gratings to investigate the mechanisms underlying ATI.


Ahmed B, Cordery PM, McLelland D, Bair W & Krug K (2012) "Long-range clustered connections within extrastriate visual area V5/MT of the rhesus macaque." Cereb Cortex 22(1):60-73

Kwag J*, McLelland D*, Paulsen O (2011) "Phase of firing as a local window for efficient neuronal computation : tonic and phasic mechanisms in the control of theta spike phase." Front Hum Neurosci 5:3 *equal contribution

McLelland D, Baker PM, Ahmed B & Bair W (2010) "Neuronal responses during and after the presentation of static visual stimuli in macaque primary visual cortex." J Neurosci 30(38):12619-31

McLelland D, Ahmed B & Bair W (2009) "Responses to static visual images in macaque lateral geniculate nucleus : implications for adaptation, negative afterimages, and visual fading." J Neurosci 29(28):8996-9001

McLelland D, Paulsen O (2009) "Neuronal oscillations and the rate-to-phase transform : mechanism, model and mutual information." J Physiol 587:769-85

McLelland D, Paulsen O (2007) "Cortical songs revisited : a lesson in statistics." Neuron 53(3):319-21

Conferences Presentations and Invited Lectures

ECVP 2011, Toulouse : "Neural Response to Prolonged Static Visual Stimuli : Modelling Afterimages"

University of Toulouse, 2011 : "Insights from Visual Illusions : Neuronal Responses During and After the Presentation of Prolonged Stationary Visual Stimuli"

FENS 2010 : "Cortical normalization and the origins of adaptive temporal integration of motion in macaque primary visual cortex"

COSYNE 2010 : "Temporal integration of motion and cortical normalization in macaque V1"

SfN 2009 : "Responses and After-Responses to Prolonged Static Stimuli in V1 of the Macaque"

SfN 2008 : "Neuronal Correlates of Visual Afterimages in Macaque LGN"

Bristol University, 2008 : "Neuronal correlates of visual afterimages place novel constraints on cortical circuits"

Richardson Lecture, Keble College, Oxford, 2008 : "Tricking the Brain— to See How It Works : Scientific Insights from Visual Illusions"

COSYNE 2007 : "The Origin of Adaptive Temporal Integration in Visual Cortex"

COSYNE 2006 : "Oscillations enhance the information efficiency of individual hippocampal pyramidal cells"

Mise à jour 04/03/2016