18th Oct: Amelia Burroughs
Using microscopy to map the spatial distribution of synapses onto single cells
The precise arrangement of excitatory and inhibitory synapses onto individual neurons remains largely unknown. Many theories exist regarding the optimal distribution of synapses throughout the dendritic tree, but this has been difficult to test experimentally with any accuracy. Here we use a new microscopy technique (Focused Ion Beam Scanning Electron Microscopy) to map individual synaptic connections on Purkinje cells and stellate cells of the cerebellum. We can conclude that the number of excitatory connections far outweighs the number of inhibitory synapses. Inhibition may be specifically located to act as a regulatory mechanism preventing excessive excitation reaching the soma. This occurs at a spatial scale just greater than a single dendrite.
25th Oct: Denize Atan
An eye on the brain
Neurodevelopmental disorders are globally prevalent with a huge healthcare burden on society. Embryologically, the neural retina shares its origin with the CNS, and so predictably, many of the same processes and genes implicated in the development of the CNS are also required for normal retinal development. Transcription factors (TFs) play key roles in directing neural circuit assembly through their precise spatial, temporal, and cell type-specific control of gene expression, and this is highlighted by the large number of mouse mutants that have been identified in which loss of a particular TF results in a specific defect in neural connectivity in the retina and or brain. The importance of properly constructed neuronal networks is particularly pertinent to the epilepsy syndromes, in which imbalanced excitatory (glutamatergic) and inhibitory (GABAergic) inputs lead to cortical hyperexciteability. Although we suspect that many cases are genetic in aetiology, it is often difficult to make a definitive genetic diagnosis. This is because standard technology has a low sensitivity and it is estimated that 4 times the number of genes that have been associated with neurodevelopmental disorders are yet to be discovered. In this talk, I will discuss how the genes that influence the development of neural circuits in the eye have provided insights into similar processes that occur during the development of the brain.
1st Nov: Jonathan Lawry
The Practical Applications of Vagueness
This talk will explore the potential benefits of embedding vagueness as part of formal knowledge representation in intelligent systems. By focusing on the utility of vagueness in multi-agent communications, natural language generation, consensus modelling and decision making, we will investigate different aspects of the phenomenon and outline how these are captured by a number of distinct theories.
8th Nov: Jeff Orchard
From Spikes to Dynamics
Sure, neurons fire spikes. But what do all those spikes mean, and how do they perform computations? In this talk, I will describe a framework for thinking about neural computations, and show you how to turn a system-level description of a network into a network of spiking neurons. The populations of neurons are connected in a way that does your computation for you. Even though you’re working with spiking neurons, you can think about your network in the data domain, and ignore the neural details (if you wish). I’ll also show lots of computer demonstrations.
15th Nov: Dimitris Bampasakis, University of Hertfordshire
Short-term depression of inhibitory Purkinje cell synapses enhances gain modulation in the cerebellar nuclei
Information in neurons can be encoded by their action potential rate, thus making the transformation of input to output rate, the input-output (I-O) relationship, a core computational function. Introduction of a second input, often called modulatory input, can modify this I-O relationship in ways that correspond to different arithmetic operations 1. Here, we examine the modulation of the slope of the I-O relationship, also referred to as gain modulation. Gain modulation can be based on a wide variety of biophysical mechanisms, with short-term depression (STD) of excitatory synapses being one of them. Commonly, gain modulation is studied by examining the effect of tonic or synaptic inhibition on the excitatory I-O relationship. However, some projection neurons, like cerebellar Purkinje cells (PCs), are inhibitory. Therefore, the opposite scenario, in which the effect of inhibition on output rate is being modulated by an excitatory input, may occur as well. As a previous study found that inhibitory synaptic input variability can change the output rate of neurons in the cerebellar nuclei (CN), the question arises how excitatory input can modulate this relationship. Considering the excitatory input from mossy fibres (MF) onto CN neurons as modulatory, we investigated the effects on gain control exerted by STD of the inhibitory synapses that PCs make on a model CN neuron. We found that STD at the inhibitory PC-CN synapse enhanced gain modulation. Thus, like STD at excitatory synapses, STD at inhibitory synapses can enable neurons to perform multiplicative operations on their inputs.
22nd Nov: Nathan Lepora
Probabilistic neuroscience: Evolution or Revolution?
Over the last few decades, probabilistic/statistical methods have became increasingly influential in neuroscience, and also other disciplines such as computer science and robotics. In this talk, I describe how some of this progress is impacting our theoretical conceptualization of brain function. In particular, I focus on perception and learning in the cortico-basal ganglia network, and describe how the probabilistic modelling framework can give a basis for understanding neural function/dysfunction in health and disease.
29th Nov: Matt Jones
Losing control under ketamine
Pharmacology is complicated. Drugs are dirty, and their systemic effects in animals, volunteers and patients arise from a bewildering array of direct and indirect actions on pre- and post-synaptic receptors throughout the CNS. In collaboration with Rosalyn Moran (Virginia Tech), we have been attempting to disentangle the effects of the psychotomimetic ketamine on limbic-cortical theta and gamma network oscillations using Dynamic Causal Modelling (DCM). Setting our findings in the context of predictive coding theories suggests that NMDA receptor antagonism by ketamine in hierarchical and reciprocal networks may result in failure of top-down connections from frontal cortical regions to signal predictions to hippocampus, thereby disrupting error signalling. Given that theta and gamma rhythm abnormalities are also evident in schizophrenic patients, this approach may represent a framework for the study of the synaptic bases of schizo-typical cognitive disturbance. Or not. Please help me to decide.
13th Dec: Ute Leonards, Adeline Paiement and Peta Sharples
Automatic 3D and 4D modelling from sparse and misaligned tomographic data
This talk will present and illustrate with an example how computer vision can enhance the analysis of medical images. It will focus on the automatic modelling of organs from MRI and CT datasets that suffer from misalignments and gaps between the images. We will first present the challenges raised by such datasets, and notably the three inter-dependent issues of registration, segmentation, and interpolation. We then propose an integrated framework to solve these issues and produce an accurate and fully automatic modelling. At the end of the talk, Ute Leonards will introduce a new project that may be an application and an extension of this framework, namely the detection of longitudinal cerebral modifications after childhood traumatic brain injury.