2nd October: Mike Ashby
Room: SM3
Spatial clustering of new synapses
9th October: Naoki Masuda
Room: SM3
Energy landscape of human brain activity during bistable perception
Individual differences in the structure of parietal and prefrontal cortex predict the stability of bistable visual perception. However, the mechanisms linking such individual differences in brain structures to behaviour remain elusive. Here we demonstrate a systematic relationship between the dynamics of brain activity, cortical structure and behaviour underpinning bistable perception. Using fMRI in humans, we find that the activity dynamics during bistable perception are well described as fluctuating between three spatially distributed energy minimums in an energy landscape constructed from fMRI data. Transitions between these energy minimums predicted behaviour, with participants whose brain activity tend to reflect the visual-area-dominant energy minimum exhibiting more stable perception and those whose activity transits to the frontal-area-dominant energy minimum reporting more frequent perceptual switches. These brain activity dynamics are also correlated with individual differences in grey matter volume of the corresponding brain areas.
16th October: Aleks Domanski
Room: SM3
Capture, dissection and manipulation of complex cortical network dynamics in models of Autism
Cortical network function critically depends on many physiological parameters that develop in concert during early postnatal critical periods, notably intrinsic neuronal properties, synaptic function and appropriately balanced synaptic connectivity. Perturbation of normal network development processes by genetic insults associated with Autism can lead to complex network effects but it is unclear to what extent these individual factors contribute to the overall pathophysiology. Combining slice electrophysiology in a mouse model of Autism with single-cell and network simulation, I will dissect the mechanisms by which changes in multiple electrophysiological parameters lead to complex network-level effects, ask which are dominant drivers towards pathological network states, and provide insight into potential scenarios for pharmacological rescue.
23rd October: Alex Cope, Green Brain Project (University of Sheffield)
Room: SM3
Modeling the honeybee
The honeybee, with a brain consisting of 1 million neurons (100,000 times fewer than the human brain), is nevertheless capable of complex tasks normally considered the domain of advanced vertebrates. By studying this versatile insect we can therefore gain insight into the neural basis of such tasks.
30th October: Jon Hanley
Room: SM1
Predicting plasticity from protein-protein interactions
A multitude of interconnecting and highly regulated protein complexes are at the core of cell biology. Synaptic plasticity involves processes such as receptor trafficking, changes in dendritic spine morphology, and rapid, local regulation of protein synthesis. These processes are all underpinned by dynamic protein-protein interactions that are regulated by numerous signalling pathways. I will present a couple of examples of such protein complexes that we are studying, and discuss their importance in determining the outcome of plasticity-inducing stimuli.
6th November: Mark Humphries, University of Manchester
Room: SM3
Population dynamics in a locomotion neural network converge to a recurrent attractor
Many neural systems are thought to implement some form of cyclical or periodic attractor, in which neural activity “rotates” over time to drive some periodic process, such as locomotion or heading direction. However, we lack direct evidence that such neural attractors exist. We tested the hypothesis that the crawling motor program of the sea-slug Aplysia is directly implemented by a periodic attractor in its pedal ganglion network. Evoking the locomotion program caused population activity to rapidly settle into a low-dimensional, slowly decaying rotational orbit. These recurrent dynamics were consistent between programs evoked in the same animal, indicating convergence on the same basin of attraction, but could differ considerably between animals. Despite this heterogeneity we could decode a specific portion of the motor program directly from the low-dimensional dynamics. Collectively our results support the hypothesis that Aplysia’s pedal ganglion is a cyclical attractor network.
13th November: Laurence Hunt, UCL
Room: SM3
Bridging microscopic and macroscopic choice dynamics in prefrontal cortex
The significance of prefrontal cortex for reward-guided choice is well known from both human imaging and animal neurophysiology studies. However, dialogue between human and animal research remains limited by difficulties in relating observations made across different techniques. A unified modelling framework may help reconcile these data. We have previously used attractor network models to demonstrate that varying decision values can elicit changes in local field potentials (LFP) dynamics, causing value correlates observable with human magnetoencephalography (Hunt et al., Nature Neuroscience 2012). Extended, hierarchical forms of such models can also predict human functional MRI signal in different frames of reference during a multi-attribute decision process (Hunt et al., Nature Neuroscience 2014). In light of this framework, we have recently sought to relate simultaneously recorded LFP and single-unit data from prefrontal cortex of macaque monkeys performing a cost-benefit decision task. I will discuss key findings from these studies, which help us to relate value correlates in human neuroimaging studies to their cellular origins.
20th November: Denize Atan
Room: SM3
From neural stem cells to neural networks: finding light in the dark
Human cognition and behaviour are functions of neuronal networks that comprise the mammalian brain. Of these cognitive abilities, the formation of new episodic memories is critically dependent on the hippocampal formation. The hippocampal dentate gyrus is one of a small number of brain regions in which neurogenesis continues throughout adulthood, and where neurogenesis is important for learning and memory. Transcription factors play key roles in directing neural differentiation and circuit assembly through their precise spatial, temporal, and cell type-specific control of gene expression. In this talk, I will describe how our recent investigations of gene regulation and expression have taken us on a journey from events that occur at a molecular level in differentiating neurons……to hippocampal circuit formation, network dynamics, learning and memory, and finally population genetics.
27th November: Marc Goodfellow (Exeter)
Room: SM3
The role of networks in seizure generation
Epilepsy is characterised by the repeated occurrence of seizures, which are periods of pathological brain activity that arise spontaneously from a predominantly healthy functional state. Since the goal of epilepsy treatment is to abolish or reduce the tendency of the brain to transition into seizures (its ictogenicity), it is important to better understand these transitions, and how we might interact with the brain to abate them. However, seizure dynamics emerge in, and affect, large-scale brain networks, and the network paradigm for ictogenesis introduces unfamiliar challenges and new opportunities to understand epilepsy.
In this talk I will introduce a mathematical model-based approach to quantify ictogenicity in brain networks. I will demonstrate how this approach can be used to quantify differences in brain networks between patients with generalised epilepsies and healthy controls. I will also describe how we can extend this approach to quantify the contribution of each component of a network to seizure generation. This quantification is based upon the effect that a treatment-specific perturbation has on network ictogenicity. Using exemplar networks I will explore how the apparent ictogenicity of nodes can vary according to network structure and the presence or absence of “pathological” nodes (seizure foci). I will explain how this approach can potentially provide an insightful and principled way to interpret and describe generalised or focal seizure dynamics, and may enhance our strategies for the classification and treatment of epilepsies.
4th December: 3 Short Talks
Room: SM3
1. Bridget Lumb
11th December: Rafal Bogacz (University of Oxford)
Room: SM2
Learning in cortical networks through error back-propagation