25th Jan: 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.
1st Feb: 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.
8th Feb: Krasimira Tsaneva-Atanasova
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..
15th Feb: Ullrich Bartsch
Neural trajectories of working memory A graphical approach to cognition
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.
22nd Feb: 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?
1st Mar: Eoin Lynch
Parameter 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.
8th Mar: 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.
15th Mar: 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.
26th Apr: L.J.B. Briant
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.