27th Apr: Marc Box
Decoding the neural representation of space
Spike train decoding is the inverse inference of some state of an animal (sensory, behavioural, etc) from concurrently recorded spike trains. Spatial decoding – inference of position in an environment using spikes recorded from spatially sensitive “place cells” in the hippocampus – has been a proving ground for decoding methodology.
Very high predictive accuracy can be obtained using Bayesian decoding in a state space or hidden Markov model framework. With efficient computational methods and real time decoding, applications such as brain-machine interfaces become possible. Decoding might also have something to teach us about how the brain encodes information.
This provokes some important questions. What can we really say about how the brain encodes information? How should we interpret this kind of model? We implemented a Bayesian decoder on data recorded from the hippocampus of a rat navigating a maze in a rule learning task. Some interesting observations can be made, of features that resemble phenomena found by others, but are these justified? The challenge is to improve the model such that anything it might reveal about the neural code is statistically sound and based on good evidence.
4th May: Martin Pearson
Neurorobotic modelling of the neuroethology of mammalian whisker sensory system
The whisker sensory system serves as a useful model in the study of active touch. Adopting a neurorobotic methodology to assist with this study provides a unique perspective from which to test hypotheses. The Bristol Robotics Laboratory has been working with neuroscientists in this role for some time, the latest generation of biomimetic mobile whiskered robots being used to investigate behavioural switching strategies and the neuroethology of predictive prey pursuit behaviour observed in the Etruscan Shrew. We propose that gross behaviours seen in small whiskered mammals such as “exploring”, “wall following”, “orienting” and so on, emerge as a result of a simple tactile foveation behaviour driven by an attention based ego-centric mapping. Further, we have demonstrated that this control architecture can be usefully deployed in modelling prey pursuit behaviour, however, questions have arisen as to how multi-whisker contact information is fused and object shape inferred in the brain, especially considering the sparse nature of the information and the rapid response of shrew hunting.
11th May: Nadia Cerminara
The importance of complex spikes in cerebellar contributions to motor behaviour
18th May: Jade Thai
Clinical applications of functional and effective to investigate neural networksconnectivity methods
The recent theoretical shift towards understanding the brain in terms of neural network/systems in neuroimaging studies has provided a means towards understanding how these neural networks interact and underpin function in both the healthy and pathological brain. Here we report two case studies that demonstrate how the application of functional and effective connectivity analysis can enhance our understanding of epileptic neural networks.
25th May: Linford Briant
The A-current as a Tuneable Low-Pass Filter of Synaptic Drives to Sympathetic Preganglionic Neurones
Sympathetic preganglionic neurones (SPN) provide the motor output from the brain to blood vessels. This motor output is increased in hypertension although the underlying mechanism is unknown. The sympathetic output is bursty and one prominent rhythm of sympathetic discharge is entrained to the central respiratory pattern generator (CRPG) output. This respiratory-sympathetic coupling is augmented in the spontaneously hypertensive rat (SHR). On the basis of experimentally measured changes in the intrinsic properties of SPN we hypothesised that this altered coupling may be due to changes in the potassium A-current (IA) and we proposed that this could augment the response of SPN to synaptic drives. In other systems IA has been shown to regulate firing frequency and after-hyperpolarisation (AHP) shape. To test this hypothesis we have built the first mathematical model of a SPN (in NEURON). This has A-current parameters that fit detailed experimental data using the Borg-Graham method of formulating the rate-constants of activation and inactivation. The result was a model that exhibited similar electrophysiology to SPN in vivo, and steady-state curves that well-fit data for the normotensive Wistar (WKY) rat.
We identified IA parameters that could transform our WKY neuronal behaviour into that seen in the SHR. These included reducing the maximal conductance density and altering the slope factors for the steady-state curves.
We next generated CRPG-related synaptic input, and quantified the influence of IA on the output. During synaptic bursts, the proportion of suprathreshold EPSPs increased in the SHR model (SHR=0.99±0.01, WKY=0.6±0.1, p < 0.001). Examination of EPSP shape showed that decreasing IA increased EPSP duration (τSHR=28ms,τWKY=13ms) with little effect on amplitude. This change in EPSP decay has a major effect on EPSP summation at higher frequencies of synaptic drive which, when combined with an augmentation of AHP amplitude, low-pass filtered the sympathetic output. Our data shows that a reduction of IA in SHRs would result in an amplification of respiratory-sympathetic coupling. These modelling data are consistent with IA playing a major role in determining SPN excitability thus regulating the sympathetic output as well as having a novel role as a tuneable low pass filter of synaptic integration.