Neural Dynamics Forum
Neural Dynamics Forum is a seminar series during which researchers from Bristol will discuss their current research and open problems related to mathematical or computational methods and neuroscience. To facilitate informal discussion each speaker will only show upto 3 slides.
If you would like to be added to the Neural Dynamics Forum mailing list for updates please email Hannah Julienne.
Dates and Venue
The seminars take place on Fridays from 13.10-14.10 in the AIMS building(adjacent to the Medical Sciences Teaching Laboratories in the Cantock zone, see map). Any changes of venue will be shown in the list below.
An archive of previous Neural Dynamics Forums is available here)
Fri 12th October, 13.10-14.10
David Coyle
'Intelligent machines and human agency'
Intelligent computing systems - particularly in research contexts - are becoming increasing complex. These systems have the potential to infer human intentions and then provide assistance or act on these inferences. This raises important questions regarding the ownership and control of actions when humans use, and interact with, new technologies. In neuroscience literature the sense of agency is defined as the experience of being in control of one’s own actions and, through this control, affecting the external word and having responsibility for the consequences of our actions. This talk will describe research I have undertaken over the past two years, in collaboration with psychiatrists and cognitive neuroscientists at the Behavioural and Clinical Neuroscience Institute in Cambridge. We have applied neuro-cognitive experimental techniques to investigate peoples’ experience of agency when interacting with intelligent computer interfaces, and with changing modalities of human-computer interactions. I will discuss two specific experiments, but would also like to discuss the ways in which inter-disciplinary collaborations, between human-computer interaction and neuroscience researchers, offers new opportunities to extend both disciplines. David Coyle is Lecturer in Human Computer Interaction with the Department of Computer Science, in the University of Bristol.
AIMS 2A/B
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Fri 19th October, 13.10-14.10
Alan Roberts
'How to build the connectome of a small CNS controlling rhythmic activity'
The brainstem and spinal neurons and networks controlling swimming in hatchling frog tadpoles have been defined in some detail. A simple axon growth model can match real neuron populations and generate a synaptic connection map or "connectome". When this connectome is mapped onto a functional model it can swim in response to brief "sensory" stimuli. Our knowledge of this system allows detailed questions to be asked about the crucial features of the neurons and network controlling a rhythmic activity, in particular a population of electrically coupled pacemaker neurons.
AIMS 2A/B
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Fri 26th October, 13.10-14.10
Jonathan Brooks
Exploring brainstem - spinal cord connectivity during distraction based analgesia
Distraction based analgesia is a robust finding from human and experimental animal studies. The amount of pain perceived by a subject can be modified by attention related processes, which allow continued performance at tasks during the experience of pain. The first point at which painful stimuli are processed in the central nervous system lies in the dorsal horn of spinal cord, and key areas in the brainstem (peri-aqueductal grey matter) and rostral ventromedial medulla have been shown to influence these incoming pain-related signals. I will present data from a recent functional magnetic resonance imaging (fMRI) study which explored these interactions through a 2x2 factorial design (factors: task difficulty - hard or easy; applied temperature - high or low). The question remains whether (in man) there is a tight coupling between the brainstem and spinal cord activity that facilitates the suppression of incoming nociceptive (i.e. pain-related) signals.
AIMS 2A/B
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Fri 2nd November, 13.10-14.10
Ruth Betterton
'Brain waves: Gamma Oscillations in the Hippocampus
A neuronal oscillation can be broadly described as the synchronised firing of a population of cells. Gamma oscillations (30-100 Hz) are associated with a variety of cognitive functions including attention, sensory processing and learning and memory. We developed an in vitro preparation to study the properties of gamma oscillations within CA3 of the rat hippocampus. This system enabled concomitant local field potential and whole cell electrophysiological recordings. Simulations run in a computational model showed many of the properties observed in the slice preparation and in vivo work by others. Future work will extend both models for the investigation of the cholinergic modulation of these processes.
AIMS 2A/B
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Fri 9th November, 13.10-14.10
Rafal Bogacz
New evidence for Bayes' theorem being hardwired in the basal ganglia
This talk will present results of two experiments testing predictions of a model assuming that during decision making the cortico-basal-ganglia circuit computes probabilities that considered alternatives are correct, according to Bayes theorem. The talk will start with a review of the model. Then it will present results of an experiment from the lab of Peter Magill in Oxford on the microcircuitry of globus pallidus (Mallet et al., 2012, Neuron), and an experiment performed by Chrystalina Antoniades from Oxford on the effect of deep brain stimulation on patients representation of probabilities.
AIMS 2A/B
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Fri 16th November, 13.10-14.10
Tom Jahans-Price
Hippocampal-prefrontal information coding during spatial decision-making
In order to investigate information processing during decision-making, we introduce a computational model describing a maze-based task in which rats have choose between a left or right turn depending on the direction of their previous turn (Jones & Wilson 2005, PLoS Biology 3 e402). The model uses differential equations to describe the behaviour and interactions of populations of neurons, and integrates sensory input with working memory and rule-learning to produce learning and performance that accurately recapitulate behavioural data from rats. The model predicts the occurrence of turn- and memory-dependent activity in neuronal networks subserving task performance.
We tested these model predictions using a new software toolbox (Maze Query Language, MQL) to analyse activity of prefrontal cortical (PFC) and hippocampal (CA1) neurons recorded from 6 adult rats during task performance. The firing rates of CA1 neurons discriminated context (i.e. precise trajectory between reward points on a given trial) but were not turn-selective. In contrast, we found a subset of PFC neurons selective for turn–direction and/or trajectory that display a gradual buildup of activity before the decision turn; turn-selectivity in PFC was significantly reduced during error trials. We found some PFC neurons selective for turn, some selective for context and some conjunctively encoding both.
These analyses complement data from neurophysiological recordings in non-human primates indicating that firing rates of cortical neurons correlate with integration of sensory evidence during perceptual decision-making. Further analyses of the rodent data will allow us to link this cortical processing to input from subcortical structures including hippocampus and striatum.AIMS 2A/B
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23rd November, 13.10-14.10
Stafford Lightman and Jamie Walker
Neuroendocrine Dynamics
Oscillating levels of adrenal glucocorticoid hormones are essential for optimal gene expression, and for maintaining physiological and behavioural responsiveness to stress. The biological basis for these oscillations is not known, but a neuronal pulse generator within the hypothalamus has remained a popular hypothesis. We have used mathematical modelling combined with experiments to show that pulsatile hypothalamic activity is not required for generating ultradian glucocorticoid oscillations, and that the oscillations are generated by a sub-hypothalamic pituitary-adrenal system, which functions as a deterministic peripheral hormone oscillator with a characteristic ultradian frequency. We will present these findings and discuss some of the new challenges that follow on from our results
AIMS 2A/B
30th November, 13.10-14.10
Alex Pavlides
A key pathology of Parkinson's disease is the occurrence of persistent beta oscillations. We investigate a model of the circuit composed of subthalamic nucleus and globus pallidus, which receives delayed feedback. This feedback models the closed loop structure of the basal ganglia. I will show how the network's stability and frequency is influenced by the delayed feedback and discuss how this model builds on earlier work.
AIMS1
7th December, 13.10-14.10
Roland Baddeley
AIMS 2A/B
25th January, 13.10-14.10
Clea Warburton
Recognition memory is our ability to distinguish between familiar objects or places i.e. those which we have encountered before and novel objects or places that we have never come across before. This form of memory is central to our ability to recall day-to-day events, and is notably lost in cases of amnesia following head trauma or neurodegeneration.
Recognition memory is not a unitary process, but rather may be sub-divided depending on the type of information to be remembered. We and others have shown that recognition memory for objects is mediated by the perirhinal cortex in the medial temporal lobe; while the recognition of places is dependent on the hippocampus. Of particular interest to my laboratory are two facets of recognition memory which allows us to remember whether we have encountered an object within a particular location, object-in-place memory, or in a particular sequence, temporal order or serial recognition memory.
Data from our lab. demonstrates that recognition memory is dependent upon a number of key brain regions; namely the perirhinal cortex, medial prefrontal cortex and hippocampus and more importantly we have evidence, which shows that these regions form components of an integrated memory system. Further we have examined the role of synaptic plasticity in the formation of different forms of recognition memory and revealed the importance of a number of neurotransmitter and intracellular signalling mechanisms in the formation of memories with in the neural circuit we have identified.AIMS1
1st February, 13.10-14.10
John Grogan
Dopamine's effects on reinforcement learning and memory
Dopamine and the basal ganglia have been implicated in reinforcement learning and memory, although there is disagreement on whether dopamine during learning or retrieval is the deciding factor. This talk will focus on an experiment I am running that attempts to separate out these effects using Parkinson's Disease patients, and the computational models fit to the data.
AIMS1
8th February, 13.10-14.10
Krasi Tsaneva-Attanasova
Gonadotrophin-releasing hormone (GnRH) is a hormone released from the brain to control the secretion of reproductive hormones. Pulsatile GnRH can increase fertility (e.g. in IVF programmes) whereas sustained GnRH reduces fertility (and is used to treat hormone-dependent cancer) but the ways in which the GnRH receptor and its intracellular signalling cascade decode these kinetic aspects of stimulation are essentially unknown. In addition, our knowledge is scarce of the intracellular mechanisms that govern frequency modulation of gonadotropins secretion, much less how such fine-tuning is regulated by different signal inputs. We develop a signalling pathway model of GnRH-dependent transcriptional activation in order to dissect the dynamic mechanisms of differential regulation of gonadotropin subunits gene. The model incorporates key signalling molecules, including extracellular-signal regulated kinase (ERK) and calcium-dependent activation of Nuclear Factor of Activated T-Cells (NFAT), as well as translocation of activated/inactivated ERK and NFAT across the nuclear envelope. In silico experiments designed to probe trancriptional effects downstream of ERK and NFAT reveal that interaction between transcription factors is sufficient to account for frequency discrimination..
AIMS1
15th February, 13.10-14.10
Ullrich Bartsch
Neural trajectories of working memory A graphical approach to cognition [work in progress]
To this day the very nature of neural computation still remains elusive. Recently it was proposed that cortical networks operate near the edge of chaos, where transient non-linear network dynamics constitute a fundamental principle of neuronal computing. One implementation of this principle is known as liquid state machine, or reservoir computing (for a recent review see Buonomano and Maass, 2009).
There is only limited evidence from in vivo electrophysiology to corroborate this type of computing in biological networks. Transient dynamics have been identified during encoding of odours in projection neurons in bees and during working memory tasks in cortical networks in rodents.
Inspired by the concept of reservoir computing, I will present some preliminary analysis on extracellular unit recordings in rats during a spatial working memory task. This newly developed analysis aims to embed recorded neural activity into a low dimensional neural state space through calculating distances between spike trains and subsequent multidimensional scaling. This allows visualising neural dynamics during the task in a time resolved manner inside a meaningful coordinate system.
At this stage this is merely a tool for visualising network dynamics over time. One preliminary result is the separation of neuronal trajectories during the working memory period of the task. The dynamics resemble winnerless competition type computation, with brief periods of high synchrony between recorded units.
I would like to use the forum as an opportunity to present these very preliminary results, discuss the usefulness of this approach and most importantly spur a discussion about the nature of neuronal computing.AIMS1
22nd February, 13.10-14.10
Alan Winfield
The Thinking Robot
Press headlines frequently refer to robots that think like humans, have feelings, or even behave ethically, but is there any basis of truth in such headlines, or are they simply sensationalist hype? Computer scientist EW Dijkstra famously wrote the question of whether machines can think is about as relevant as the question of whether submarine can swim , but the question of robot thought is one that cannot so easily be dismissed. In this talk, I will outline the state-of-the-art in robot intelligence, attempt to answer the question how intelligent are present day intelligent robots? and describe efforts to design robots that are not only more intelligent but also have a sense of self. But if we should be successful in designing such robots, would they think like animals, or even humans? Are there risks, or ethical issues, in attempting to design robots that think?
AIMS1
1st March, 13.10-14.10
Eoin Lynch
Paramter estimation of an auditory spiking neuron model
Spiking neuron models can accurately model the spike trains of cortical neurons in response to somatically injected currents. An evolutionary optimisation method is presented here for fitting generic spiking neuron models to spike train data. The method is initially tested on in-vitro spike train recordings from cortical neurons responding to known in-vivo like current injection. An extended model, consisting of a cascade of a receptive field like structure which estimates the somatically injected current and a a spiking neuron model, is optimised using the method to find a model that characterises the spike train responses of auditory neurons in the zebra finch auditory forebrain responding to natural auditory stimuli in vivo.
AIMS1
8th March, 13.10-14.10
Casimir Ludwig
Control over fixation duration
Human vision relies critically on sampling the visual environment during brief periods of stable fixation. During any one fixation, the observer essentially performs three tasks: (i) analyse the visual information at the current point of gaze; (ii) analyse peripheral visual information in order to decide where to fixate next; (iii) decide when to shift gaze to the next target location. In this seminar I will focus on this temporal component. I will discuss models based on integrating sensory evidence to a decision criterion. In this regard, one critical question is whether fixation duration is controlled by the quality of sensory evidence at all, and if so, whether this evidence comes from the current point of fixation or from potential target locations in the periphery.
AIMS1
15th March, 13.10-14.10
Simon Farrell
Clustering in working memory and episodic memory
I'll present part of a programme of work that suggests some common principles and mechanisms that underlie working memory and episodic memory. I'll talk about some data from serial recall (a prototypical short-term memory task) and free recall (a standard episodic memory task), and discuss a model that gives a fine-grained account of these data. The essential idea of the model is that longer sequences of information are segregated into clusters of serially ordered information, and that free recall and serial recall primarily differ in the strategies employed to access those clusters. Depending on time I'll talk about individual differences, effects of ageing, amnesia, and the question of why memory should behave in this fashion.
AIMS1
26th April, 13.10-14.10
L.J.B. Briant
Clustering in working memory and episodic memory
High blood pressure (BP), or neurogenic hypertension, is known to be related to dysfunction of the sympathetic nervous system (SNS). To investigate how SNS dysfunction can cause a chronic rise in BP, we have constructed a model of the pathway of transmission from the SNS to the smooth muscle cells (SMCs) that are responsible for the contraction of arteries. The differential equations describe spike generation in the nerve cells, to the calcium-mediated contractile response of SMCs.
Data from hypertensive rats indicates that a change in the phase and amplitude of respiratory component of the sympathetic input to SMCs occurs in this disease state. We use the model to show that changing the respiratory component of the input influences the contractile force generated in the SMC.AIMS2A/2B
3rd May, 13.10-14.10
No Forum this week
10th May, 13.10-14.10
Barak Pearlmutter
"The Slow Axon Blockade Hypothesis for DBS."
Deep brain stimulation (DBS) can ameliorate essential and Parkinsonian tremor. The detailed mechanism by which this is achieved is unclear, but clues to the mechanism may lie in the known destabilising influence of time delays upon closed-loop systems. We hypothesise that DBS tends to stabilise the system and reduce tremor oscillations by reducing time delays in motor control feedback loops. We posit that the reduction is associated with a partial blockade of axonal pathways by antidromic activation, with the blockade being less complete for axons with higher propagation velocities. The inverse relationship between blockade effectiveness and propagation velocity is due to the blocking pulses clearing the axon faster when their velocity is higher, leaving a larger fraction of the time for signalling activity. Two mathematical models have been used to illustrate the idea: a biomechanical model of arm movement and a random neuronal network. Both models exhibit changes of behaviour under simulated slow axon blockade that agree with several experimental observations of DBS. The hypothesis in general accounts for a variety of known features of DBS, especially regarding the target area and the stimulation frequency, and makes a number of testable predictions.
AIMS2A/2B
17th May, 13.10-14.10
Martin Homer
"The mathematical modelling of oscillatory dynamics in the accessory olfactory bulb."
AIMS2A/2B
24th May, 13.10-14.10
Jack Mellor
"Modulation of hippocampal synaptic transmission and network oscillations"
The hippocampus is sometimes referred to as a very large random access synaptic space where new information can be rapidly compared to existing memories. Networks of associated neurons form rapidly and interact with other networks by the processes of synaptic plasticity and network oscillations. We have been exploring how these processes interact and can be modulated by neuromodulatory input from the cholinergic system.
AIMS2A/2B
