UH Biocomputation Group - computational neuroscience

PhD in Computational & Cognitive Neuroscience

2020-08-25T14:11:50+01:00

PhD in Computational & Cognitive Neuroscience

An exciting full-time funded PhD opportunity has arisen at the University of Hertfordshire associated to a collaborative project with King’s College London and Brunel University London funded by the US Air Force.

The project aims to build an explorative and predictive model of the brain that is sensitive to the transitions between sustained attention and mind-wandering states using an already collected simultaneously acquired EEG/fMRI dataset. Towards this goal, novel methods for characterizing, sequencing, and predicting neural dynamics at two complementary spatio-temporal resolution levels will be developed: level 1) electroencephalography (EEG) microstates, which are short quasi-stable topographies of brain electrical activity as measured at the scalp; and level 2) functional connectivity maps derived from the functional Magnetic Resonance Imaging (fMRI) data.

We are seeking to appoint a graduate in Computer Science, Bioengineering, Physics, Mathematics, Neuroscience, or related fields, with an interest in cognitive neuroscience and neuroimaging, who has proven programming skills (e.g., Python, Matlab, C++). Knowledge of signal processing, time-series analysis, and machine learning would be an advantage. Previous experience of EEG and/or fMRI data analysis is highly desirable.

The 3-year full-time PhD studentship includes a stipend of £15,285 per annum in addition to covering tuition fees. Only EU and UK citizens are eligible to apply. The start date of the PhD will be January 2021.

The PhD will be supervised by Dr Shabnam Kadir (University of Hertfordshire), Dr Elena Antonova (Brunel University London), Prof Robert Leech (Institute of Psychiatry, Psychology and Neuroscience, King’s College London), and Prof Chrystopher Nehaniv (University of Hertfordshire, United Kingdom, and University of Waterloo, Ontario, Canada).

Interested candidates are encouraged to make informal inquiries with Dr Shabnam Kadir (s.kadir2 AT herts.ac.uk) before making a formal application.

To apply, submit an application form (downloadable from https://www.herts.ac.uk/__data/assets/pdf_file/0010/31105/uh-application-form.pdf) together with a cover letter, CV, and scanned copies of university transcripts and degree certificates (BSc, and if relevant MSc) via email to doctoralcollegeadmissions AT herts.ac.uk, cc-ing Dr Kadir on s.kadir2 AT herts.ac.uk and Dr Antonova on elena.antonova AT brunel.ac.uk

Closing Date for the applications: 23rd October 2020 (Interviews are expected to be scheduled in the week commencing 16th November 2020).

PhD Studentship in Computational Neuroscience and Rehabilitation Robotics

2020-04-30T16:40:02+01:00

Biocomputation Research Group

Centre for Computer Science and Informatics Research

University of Hertfordshire, UK

Application deadline 1 June 2020

Bursary GBP 15,285 p.a.

Applications are invited for PhD positions in the Biocomputation Research Group at the University of Hertfordshire. (http://biocomputation.herts.ac.uk/).

Project description

Stroke is a major cause of disability in adults. More than 15 million strokes occur every year in the world, and more than 100,000 of these affect patients in the UK. Stroke patients often have an impaired ability to control their upper limbs and need assistance with every-day tasks. Relearning motor skills after stroke is similar to learning new motor skills, for example learning to play tennis, but a problem for stroke survivors is that their impaired movements often restrict the ability to use sensory feedback for re-learning.

Rehabilitation robotics has shown promise to augment the rehabilitation process and to offer feedback on performance. However, the personalisation of the therapy to individual needs remains a major challenge to date.

The proposed project will use a computational model of the cerebellum that is being developed by the Biocomputation Research Group (biocomputation.herts.ac.uk) to optimise robotic rehabilitation for individual subjects. The cerebellum has been optimised throughout vertebrate evolution to become an adaptive controller of biological skeletomuscular structures that is unrivalled by any artificial adaptive motor control algorithm. This has led and is still leading to the development of a rapidly increasing number of computational models of cerebellar learning, and to the successful applications of these cerebellar models to controlling simulated and real robots. The PhD project will involve the development and application of personalised cerebellar models in order to optimise rehabilitation robots for individual subjects.

Applicants should have excellent computational and numerical skills and a very good first degree in computer science, biology, maths, physics, neuroscience, or a related discipline. Successful candidates are eligible for a research studentship award from the University (GBP 15,285 per annum bursary plus payment of the student fees). Applicants from outside the UK or EU are eligible.

Research in Computer Science at the University of Hertfordshire has been recognised as excellent in the latest Research Excellence Framework Assessment, with 50% of the research submitted rated as internationally excellent or world leading. The Centre for Computer Science and Informatics Research provides a very stimulating environment, offering a large number of specialised and interdisciplinary seminars as well as general training and researcher development opportunities. The University is situated in Hatfield, in the green belt just north of London.

Please contact Dr Volker Steuber or Prof Farshid Amirabdollahian for informal enquiries. Application forms are available under https://www.herts.ac.uk/study/schools-of-study/engineering-and-computer-science/research-in-engineering-and-computer-science/the-phd-programme-in-computer-science and should be returned to doctoralcollegeadmissions AT herts DOT ac DOT uk.

The short-listing process will begin on 1 June 2020.

Growth rules for the repair of asynchronous irregular neuronal networks after peripheral lesions

2019-11-25T16:35:45+00:00

Ankur Sinha's journal club where he discusses his results from his Ph.D. research documented in the preprint: Growth rules for the repair of asynchronous irregular neuronal networks after peripheral lesions (Sinha et al. 2019). The abstract is below:

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model, simulations of which accurately reproduce network rewiring after peripheral lesions as reported in experiments, to study these activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we deafferent a biologically realistic network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex.

Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed model of homeostatic structural plasticity and the inhibitory synaptic plasticity mechanism that also balances our AI network are both necessary for successful rewiring of the network.

Date: 29/11/2019
Time: 16:00
Location: D449

Standards and Tools in Neuroscience: a report on the Open Source Brain Workshop 2019

2019-10-01T10:52:42+01:00

While scientific work and output was traditionally limited to relatively small expert communities, the landscape is rapidly changing. Modern Science is far too complex to be carried out in isolation, and the need to increase the uptake of scientific output in society now seems far more pressing. As a result, scientific communities are pushing to make Science more Open.

The Neuroscience research community has also made this commitment. The philosophy of Free/Open Science, however, must be backed by Free/Open standards and tools. The Open Source Brain project is one of many initiatives that focus on developing Free/Open tools and standards for Neuroscience. A recent publication summarises their work.

While initially targeting computational Neuroscience, following the renewal of their funding from the Wellcome Trust, the Open Source Brain project are expanding their deliverables to support experimental data as well. With this in mind, they organised a workshop in September to discuss two key themes:

Accessible sharing of cellular Neuroscience data: by supporting the Neurodata Without Borders (NWB) format.
Modelling the cortex across scales: by further expanding the NeuroML model description language.

Since I was fortunate enough to attend this workshop, in this talk, I will summarise the main points that were discussed here. Time permitting, I will hope to begin a discussion in our group on how we can ensure that we follow and contribute to these standards and tools to make our research work and its outputs "default to Free/Open".

I will conclude with a (another?) short marketing pitch for our NeuroFedora project which shares these goals.

References/URLs

Open Science: https://en.wikipedia.org/wiki/Open_science
Free/Open Source Software: https://www.fsf.org/blogs/community/user-liberation-watch-and-share-our-new-video/
Open letter committing to the use of Open Source for Neuroscience: http://opensourceforneuroscience.org/
Open Source Brain: http://www.opensourcebrain.org
Neurodata Without Borders (NWB): https://nwb.org
NeuroML: https://www.neuroml.org
NeuroFedora: https://neuro.fedoraproject.org

Date: 04/10/2019
Time: 16:00
Location: D449

Variational AutoEncoders for fragrant molecule data

2019-09-25T15:14:12+01:00

Vinesh Bhunjun's journal club session, where will he present the effects of his cooperation with our laboratory.

In this talk, I will explore a summary of the work I have done over the past 4 weeks on Variational AutoEncoders (VAE) as applied to fragrant molecule data. VAEs provide a compact latent representation of the input from which it is possible to reconstruct the input.

Date: 27/09/2019
Time: 16:00
Location: D449

Magnetic-inspired optimization algorithms: Operators and structures

2019-05-16T15:02:58+01:00

Mohammad Tayarani-Najaran's journal club session, where he will present the paper "Magnetic-inspired optimization algorithms Operators and structures (M.-H. Tayarani-N., M.-R. Akbarzadeh-T, 2014)"

A novel optimization algorithm, called the Magnetic Optimization Algorithms (MOAs), is proposed in this paper which is inspired by the principles of magnetic field theory. In MOA, the possible solutions are some magnetic particles scattered in the search space. In this respect, each magnetic particle has a measure of mass and magnetic field according to its fitness. In this scheme, the fitter magnetic particles are more massive, with stronger magnetic field. In terms of interaction, these particles are located in a structured population and apply a long range force of attraction to their neighbors. Ten different structures are proposed for the algorithm and the structure that offers the best performance is found. Also, to improve the exploration ability of the algorithm, several operators are proposed: a repulsive short-range force, an explosion operator, a combination of short-range force and explosion operator and a crossover interaction between the neighboring particles. In order to test the proposed algorithm and the proposed operators, the algorithm is compared with a variety of existing algorithms on 21 numerical benchmark functions. The experimental results suggest that the proposed algorithm outperforms some of the existing algorithms.

Date: 17/05/2019
Time: 16:00
Location: E251

Revisiting a theory of cerebellar cortex

2019-05-14T21:28:45+01:00

Ohki Katakura's journal club session, where he presented the paper "Revisiting a theory of cerebellar cortex (Yamazaki & Lennon 2019 in press)".

Long-term depression at parallel fiber-Purkinje cell synapses plays a principal role in learning in the cerebellum, which acts as a supervised learning machine. Recent experiments demonstrate various forms of synaptic plasticity at different sites within the cerebellum. In this article, we take into consideration synaptic plasticity at parallel fiber-molecular layer interneuron synapses as well as at parallel fiber-Purkinje cell synapses, and propose that the cerebellar cortex performs reinforcement learning, another form of learning that is more capable than supervised learning. We posit that through the use of reinforcement learning, the need for explicit teacher signals for learning in the cerebellum is eliminated; instead, learning can occur via responses from evaluative feedback. We demonstrate the learning capacity of cerebellar reinforcement learning using simple computer simulations of delay eyeblink conditioning and the cart-pole balancing task.

Date: 03/05/2019
Time: 16:00
Location: D118

An EMG-CT method using multiple surface electrodes in the forearm

2019-04-10T11:08:44+01:00

Emil Dmitruk's journal club session, where he will present an overview of an EMG-CT method, while referencing various papers.

Electromyography computed tomography (EMG-CT) method is proposed for visualizing the individual muscle activities in the human forearm. An EMG conduction model was formulated for reverse-estimation of muscle activities using EMG signals obtained with multi surface electrodes. The optimization process was calculated using sequential quadratic programming by comparing the estimated EMG values from the model with the measured values. The individual muscle activities in the deep region were estimated and used to produce an EMG tomographic image.

Emil will reference the following papers:

"An EMG-CT method using multiple surface electrodes in the forearm (Y. Nakajima et al, 2014)"

"An Experimental Model on the Activity of Forearm Muscles Using Surface Electromyography (Y. Nakajima et al, 2009)"

"Surface Conduction Analysis of EMG Signal from Forearm Muscles (Y. Nakajima et al, 2009)"

Date: 12/04/2019
Time: 16:00
Location: D118

What Is Decidable About Low Dimensional Hybrid Systems?

2019-03-13T19:01:21+00:00

Olga Tveretina's journal club session, where she will present herself and Andrei Sandler's work.

A hybrid system is a dynamic system that exhibits both continuous and discrete behaviour, and the number of its dimensions is determined by the number of the continuous variables. The hybrid system paradigm is a useful tool for describing a wide range of real-world applications. Examples come from robotics, avionics, biological networks, chemical processes, etc. Most of the hybrid systems are safety critical. Formally, verifying safety properties of hybrid systems consists of construction of a set of reachable states and checking whether this set intersects with a set of unsafe states. Therefore, one of the most fundamental problems in the analysis of hybrid systems is the reachability problem. The reachability problem is known for being difficult, and it is only decidable for special kinds of hybrid systems. Even though many attempts have been made to define the boundary between decidable and undecidable hybrid systems, it is far from being resolved. Asarin, Mysore, Pnueli and Schneider defined some classes of low dimensional hybrid systems lying on the boundary between decidable and undecidable systems in their seminal paper "Low dimensional hybrid systems – decidable, undecidable, don’t know (Asarin et al., 2012)", and for which decidability is unknown. In this talk, Olga will present an overview of the area and discuss the recent work on the decidability of reachability for a class of hybrid systems due to Asarin, Mysore, Pnueli and Schneider.

Date: 15/03/2019
Time: 16:00
Location: D118

ASP: Learning to Forget With Adaptive Synaptic Plasticity in Spiking Neural Networks

2019-02-27T15:41:47+00:00

Sam Sutton's journal club session, where he will present the paper "ASP, Learning to Forget With Adaptive Synaptic Plasticity in Spiking Neural Networks (Panda et al., 2018)"

A fundamental feature of learning in animals is the “ability to forget” that allows an organism to perceive, model, and make decisions from disparate streams of information and adapt to changing environments. Against this backdrop, we present a novel unsupervised learning mechanism adaptive synaptic plasticity (ASP) for improved recognition with spiking neural networks (SNNs) for real time online learning in a dynamic environment. We incorporate an adaptive weight decay mechanism with the traditional spike timing dependent plasticity (STDP) learning to model adaptivity in SNNs. The leak rate of the synaptic weights is modulated based on the temporal correlation between the spiking patterns of the pre- and post-synaptic neurons. This mechanism helps in gradual forgetting of insignificant data while retaining significant, yet old, information. ASP, thus, maintains a balance between forgetting and immediate learning to construct a stable-plastic self-adaptive SNN for continuously changing inputs. We demonstrate that the proposed learning methodology addresses catastrophic forgetting, while yielding significantly improved accuracy over the conventional STDP learning method for digit recognition applications. In addition, we observe that the proposed learning model automatically encodes selective attention toward relevant features in the input data, while eliminating the influence of background noise (or denoising) further improving the robustness of the ASP learning.

Date: 01/03/2019
Time: 16:00
Location: D118

Rank order decoding of temporal input patterns

2019-02-20T13:02:57+00:00

Volker Steuber's journal club session, where he will briefly discuss different forms of neural coding.

In this talk, Volker will briefly discuss different forms of neural coding. Volker will focus on temporal coding and summarise John Hopfield's (1995) suggestion how neurons with subthreshold oscillations in their membrane potential could perform scale-invariant temporal encoding of input patterns (and which conditions have to be met for the encoding to be scale invariant). However, temporal coding is sensitive to noise, and Volker will describe the rank order decoding scheme that was suggested by Simon Thorpe (1998) in order to provide robustness against noise. Furthermore, Volker will outline how Purkinje cells in cerebellar cortex could implement a form of rank-order decoding of temporal parallel fibre input patterns.

Volker will reference the following papers:

"Pattern recognition computation using action potential timing for stimulus representation (J. J. Hopefield, 1995)"

"Speed of processing in the human visual system (S. Thorpe, D. Fize and C. Marlot, 1996)"

"Face processing using one spike per neurone (R. Van Rullen et al., 1998)"

"Rank order decoding of temporal parallel fibre input patterns in a complex Purkinje cell model (V. Steuber and E. De Schutter, 2002)"

Date: 22/02/2019
Time: 16:00
Location: D118

On the Nernst-Planck equation

2018-11-13T17:21:23+00:00

Reinoud Maex's journal club session on his review: "On the Nernst-Planck equation (Reinoud Maex, 2017)"

Curious to know why the electro-diffusion equation is called the Nernst-Planck equation, I read the original papers by these authors a few years ago. This reading formed the basis of a little review paper of mine with the above title (published in Journal of Integrative Neuroscience 2017).

This review first discussed Nernst's and Planck's early papers on electro-diffusion, the brief priority conflict that followed, and the role these papers played in shaping the emerging concept of membrane excitability. The second part of this review discussed in greater detail the constraints of the Nernst-Planck theory, and showed more recent examples of its applicability to neuronal modelling.

Date: 16/11/2018
Time: 16:00
Location: D120

Computational Model of the Cerebellum and the Basal Ganglia for Interval Timing Learning

2018-11-05T14:49:43+00:00

Ohki Katakura's journal club session on his master's work "Computational Model of the Cerebellum and the Basal Ganglia for Interval Timing Learning (Ohki Katakura; Tadashi Yamazaki, 2016)"

In temporal information processing, both the cerebellum and the basal ganglia play essential roles. In particular, for interval timing learning, the cerebellum exhibits temporally localized activity around the onset of the unconditioned stimulus, whereas the basal ganglia represents the passage of time by their ramping-up activity from the onset of the conditioned stimulus to that of the unconditioned stimulus. We present a unified computational model of the cerebellum and the basal ganglia for the interval timing learning task. We report that our model reproduces the localized activity in the cerebellum and the gradual increase of the activity in the basal ganglia. These results suggest that the cerebellum and the basal ganglia play different roles in temporal information processing.

Date: 09/11/2018
Time: 16:00
Location: D120

Signal Propagation and Logic Gating in Networks of Integrate-and-Fire Neurons

2018-06-20T15:35:20+01:00

Sam Sutton's journal club session where he discusses the paper "Signal Propagation and Logic Gating in Networks of Integrate-and-Fire Neurons (Vogels and Abbott, 2005)".

Transmission of signals within the brain is essential for cognitive function, but it is not clear how neural circuits support reliable and accurate signal propagation over a sufficiently large dynamic range. Two modes of propagation have been studied: synfire chains, in which synchronous activity travels through feedforward layers of a neuronal network, and the propagation of fluctuations in firing rate across these layers. In both cases, a sufficient amount of noise, which was added to previous models from an external source, had to be included to support stable propagation. Sparse, randomly connected networks of spiking model neurons can generate chaotic patterns of activity. We investigate whether this activity, which is a more realistic noise source, is sufficient to allow for signal transmission. We find that, for rate-coded signals but not for synfire chains, such networks support robust and accurate signal reproduction through up to six layers if appropriate adjustments are made in synaptic strengths. We investigate the factors affecting transmission and show that multiple signals can propagate simultaneously along different pathways. Using this feature, we show how different types of logic gates can arise within the architecture of the random network through the strengthening of specific synapses.

Date: 22/06/2018
Time: 16:00
Location: LB252

Open Position: PhD studentship in Biocomputation Research Group

2018-05-31T12:43:55+01:00

Applications are invited for a PhD studentship on Computational frameworks for high-dimensional neural data with Dr. Shabnam Kadir in the Biocomputation Research Group in the Centre for Computer Science and Informatics Research, University of Hertfordshire, U.K.

New developments in experimental technology have led to petabytes of raw data being produced by experimental neuroscientists, which are increasingly publicly available, e.g. Allen Institute data (http://www.brain-map.org/). In particular, we are in the realm where population recordings of tens of thousands of neurons are feasible thanks to, e.g. a new generation of large dense probes for electrophysiological recordings, imaging using 2-photon microscopy coupled with calcium fluorescent sensors. Large scale neuronal recordings require novel approaches for both processing and quantitative analysis.

As well as using techniques from high-dimensional statistics, machine learning, information theory, we aim to explore new approaches from mathematical fields outside statistics, such as algebraic topology. The study of networks is a particularly important topic in neuroscience: neurons communicate with each other electrically via synapses, forming intricate networks. These networks can be studied using techniques from computational topology (e.g. persistent homology, clique topology). These could be used to extract information about subnetworks and assemblies, both from large scale recordings, and via connectomics derived from simulations (Blue Brain Project).

We aim in this project to go beyond spike sorting and develop new tools and computational frameworks which would help interpret high dimensional data and interrogate how information is being processed by the brain, e.g. How are sensory stimuli (location in environment, visual and auditory stimuli) encoded? How can we characterise the neural activity associated with memory, attention, decision making and motor control?

We shall be collaborating with labs at Imperial, Pennsylvania State University and UCL.

More information can be found here: http://www.herts.ac.uk/__data/assets/pdf_file/0003/187455/Dec2017-computational-frameworks-for-high-dimensional-neural-data.pdf

We are looking for candidates with the following profile:

Strong first degree in a quantitative field such as mathematics, physics, computer science, engineering, computational neuroscience.
Strong programming skills (e.g. Python, MATLAB, C++).
Interest in neuroscience and biology would be helpful.

A studentship from the PhD Programme in Computer Science provides approximately £14,750 per annum bursary plus the payment of student fees. Applicants from outside the UK or EU are eligible.

Research in Computer Science at the University of Hertfordshire has been recognised as excellent in the latest Research Excellence Framework Assessment (2014), with 50% of the research submitted rated as internationally excellent or world-leading. The Centre for Computer Science and Informatics Research provides a very stimulating environment, offering a large number of specialised and interdisciplinary seminars as well as general training and researcher development opportunities. The University is situated in Hatfield, in the green belt just north of London.

Please contact Dr Shabnam Kadir (s.kadir2 AT herts DOT ac DOT uk) for informal enquiries. Application forms are available under http://www.herts.ac.uk/apply/schools-of-study/computer-science/our-research/the-phd-programme-in-computer-science and should be returned to:

Ms Emma Thorogood,
Research Student Administrator,
University of Hertfordshire, College Lane,
Hatfield, Herts, AL10 9AB,
Tel: 01707 286083
E-mail: doctoralcollegeadmissions AT herts DOT ac DOT uk.

The short-listing process will begin on 25th June 2018.

Open Position: Professorship in Computational Neuroscience

2018-05-31T12:29:30+01:00

School of Computer Science,
University of Hertfordshire,
College Lane Campus, Hatfield, UK

Salary: £58,293 to £67,437 p.a. depending on skills and experience.
FTE: Full time position working 37 hours per week (1.0 FTE).
Duration of contract: Permanent.

Closing date: 24 July 2018.
Interview date: Week commencing 17 September 2018.

Applications are invited for an exceptionally well qualified Professor of Computational Neuroscience of international standing, who shares our commitment to research-informed teaching, embraces the research impact agenda, and values collegiality. Applicants will strengthen existing activity within the School and develop it further, enhancing the research strengths and external reputation of the School of Computer Science and contributing significantly to its performance in future research assessments and collaborations with commercial and public partners. The successful applicant will complement the research conducted by the Biocomputation Research Group within the Centre for Computer Science and Informatics Research. As well as providing research supervision at PhD and MSc level, the School of Computer Science offers modules in neural computing and machine learning on the BSc and MSc programmes.

Applicants must hold a PhD (or equivalent) in a relevant subject, possess excellent communication skills in English, an internationally excellent research record and reputation, including publications of the highest quality suitable for submission to the REF. Applicants must show evidence of experience and success in research/commercial income generation, engagement with the research impact agenda, engagement with key stakeholders in the Computational Neuroscience community, and management and leadership ability.

The university offers a range of benefits including a pension scheme, professional development, family friendly policies, child care vouchers, a fee waiver of 50% for all children of staff under the age of 21 at the start of the course, discounted memberships at the Hertfordshire Sports Village and generous annual leave. The University is situated in Hatfield, in the green belt just north of London.

Applications must be made online at: https://www.herts.ac.uk/contact-us/jobs-and-vacancies/academic-vacancies, reference 016195.

For informal enquiries please contact Dr Volker Steuber (v DOT steuber AT herts DOT ac DOT uk, Head of Biocomputation Research Group, Associate Dean - Research) or Prof William Clocksin (w DOT clocksin AT herts DOT ac DOT uk, Dean of School of Computer Science).

A comparison of deterministic and stochastic ion channel representations in a model of a cerebellar nucleus neuron

2018-05-21T14:55:04+01:00

Ion channels can either be modelled at a macroscopic level, using a deterministic representation such as the Hodgkin-Huxley formalism, or at a more detailed single-channel level, where their stochastic nature is taken into account by using a Markov formalism. The Hodgkin-Huxley model describes the combined collective effect of the channel population on the membrane potential, but it does not provide a comprehensive kinetic diagram. As a result, various aspects of the behaviour and consequently the functional role of individual channels can be overlooked. On the other hand, a more accurate alternative channel formalism is the Markov model. In Markov models, a single channel is represented by a kinetic scheme comprising a finite set of discrete intermediate states with probabilistic transitions from one state to another. Channel noise, introduced by the stochastic gating of the ion channels, can affect the generation and timing of action potentials and therefore potentially also single neuron computations.

In the present study, the voltage-gated channels of a morphologically realistic conductance based cerebellar nucleus (CN) neuron model were expressed as Markov formalisms and their behaviour was compared with their deterministic Hodgkin-Huxley type counterparts. Our results show that the majority of the deterministic CN channel models could easily be replaced by stochastic versions, without affecting neuronal behaviour. However, this was not the case for the fast sodium channel, where the parameter changes that had to be introduced in order to match the activity of the stochastic and deterministic models depended on the level of activation of the neuron, even for very small single channel conductances in the stochastic model.

Date: 25/05/2018
Time: 16:00
Location: LB252

Resources for Neuronal Modelling

2018-01-29T13:03:58+00:00

Neuronal simulation software packages such as GENESIS and NEURON have been available for more than 25 years. However, the last 5 years have seen a rapid growth of a variety of openly available resources for neuronal modelling, with a particular focus on open access to software, models and simulation and experimental data. I will give an overview of resources that are currently available and present a selection of simulators for neuronal modelling at different levels of detail, computational and experimental databases, tools for the generation and analysis of neuronal morphologies and interoperability frameworks.

Date: 02/02/2018
Time: 16:00
Location: LB252

How the Olfactory Bulb processes naturalistic time-varying inputs

2017-10-11T18:31:43+01:00

Rebecca will be presenting the work she conducted for her master's thesis titled 'How the Olfactory Bulb processes naturalistic time-varying inputs'.

Abstract is below:

The olfactory bulb in mammals is responsible for receiving, processing and relaying olfactory information (odours). This project investigates how naturalistic temporally fluctuating odour signals are processed and which neurons or neural mechanisms are able to extract information from these signals. Multiple computation models were created to represent different OB circuits between periglomerular cells and mitral cells using NEURON (Hines and Carnevale, 2006, 2001). The results show that the strength and frequency of these odour signals can be determined by looking at a combination of the latency and the firing rates of the output from the mitral cells.

Date: 20/10/2017
Time: 16:00
Location: LB252

Compartmental modelling of neurons and the Hodgkin-Huxley formalism

2017-09-14T22:49:43+01:00

Volker Steuber will give a basic introduction to compartmental modelling of neurons and discuss the Hodgkin-Huxley model, published in the paper A quantitative description of membrane current and its application to conduction and excitation in nerve (Hodgkin and Huxley, 1952)'. Abstract is below:

Computational models of neurons vary in their biological realism and level of detail, ranging from point neurons such as simple and adaptive exponential integrate-and-fire and Izhikevich models to morphologically realistic conductance based multi-compartmental models. Multi-compartmental models often include representations of voltage and ligand gated ion channels; these can be modelled using (commonly) the Hodgkin-Huxley or (sometimes) the Markov formalism. In this talk, I will give a basic introduction to compartmental modelling of neurons and discuss the Hodgkin-Huxley model. The Hodgkin-Huxley model is one of the foundations of computational neuroscience, having been published in a series of seminal articles in 1952 and leading to the award of the Nobel Prize in Physiology or Medicine to Alan Hodgkin and Andrew Huxley in 1963.

Date: 15/09/2017
Time: 16:00
Location: LB252

Associative properties of structural plasticity based on firing rate homeostasis in a balanced recurrent network of spiking neurons

2017-09-04T11:21:43+01:00

Ankur Sinha's discusses the paper, 'Associative properties of structural plasticity based on firing rate homeostasis in a balanced recurrent network of spiking neurons (Gallinaro and Stefan (arxiv pre-print: 2017)'. The abstract is below:

Hebbian and homeostatic plasticity have been studied extensively in the past, both experimentally and theoretically, but many aspects of their interaction remain to be elucidated. Hebbian plasticity is thought to shape neuronal connectivity during development and learning, whereas homeostatic plasticity would stabilize network activity. Here we investigate another aspect of this interaction, which is whether Hebbian associative properties can also emerge as a network effect from a plasticity rule based on homeostatic principles on the neuronal level. The maturation of cortical networks during sensory experience is an ideal case to explore this question. Excitatory neurons in the visual cortex of rodents have been shown to connect preferentially to neurons that respond to similar visual features. Since this connectivity bias is not existent at the time of eye opening, but only after some weeks of visual experience, it has been suggested that plastic mechanisms are responsible for the changes taking place during sensory stimulation. We consider a structural plasticity rule driven by a homeostasis of firing rate in a recurrent network of leaky integrate-and-fire (LIF) neurons exposed to external input that is modulated by the orientation of a visual stimulus. Our results show that feature specific connectivity, similar to what has been experimentally observed in rodent visual cortex, can emerge out of a random balanced network of LIF neurons with a plasticity rule that is not explicitly dependent on correlations between pre- and postsynaptic neuronal activity. The synaptic association of neurons responding to similar stimulus features occurs as a side-effect of controlling the activity of individual neurons. The experience dependent structural changes that are triggered by simulation are long lasting and decay only slowly when the neurons are exposed again to non modulated external input.

Date: 08/09/2017
Time: 16:00
Location: LB252

The Correlation between EEG Signals as Measured in Different Positions on Scalp Varying with Distance

2017-07-13T12:48:34+01:00

Biomedical signals such as electroencephalogram (EEG) are the time varying signal, and different position of electrodes give different time varying signals. There might be a correlation between these signals. It is likely that the correlation is related to the actual position of electrodes. In this paper, we show that correlation is related to the physical distance between electrodes as measured. This finding is independent of participants and brain hemisphere. Our results indicate that the EEG signal is not transmitted via neurons but through white matter in a brain.

Date: 14/07/2017
Time: 16:00
Location: LB252

Opportunities and challenges in the Human Brain Project

2017-07-05T17:19:34+01:00

The Human Brain Project (HBP) is a 10-year "flagship research initiative" funded by the EU. Last September, the University of Hertfordshire has joined this initiative as one of over 120 international partners. The goal of HBP is to "accelerate the fields of neuroscience, computing and brain-related medicine" [1].

In my presentation I will give an overview on the science that is done in HBP, with a special focus on our specific role within this huge project. The HBP provides 6 "research platforms" that are potentially highly relevant to ongoing projects in the Biocomputation group. I will introduce two of those platforms in more detail, i.e. the neuromorphic computing platform and the neurorobotics platform.

[1] https://www.humanbrainproject.eu/en/

Date: 07/07/2017
Time: 16:00
Location: LB252

Time as it could be measured in artificial living systems

2017-06-21T11:39:34+01:00

Being able to measure time, whether directly or indirectly, is a significant advantage for an organism. It permits it to predict regular events, and prepare for them on time. Thus, clocks are ubiquitous in biology. In the presented paper, we consider the most minimal abstract pure clocks and investigate their characteristics with respect to their ability to measure time. Amongst others we find fundamentally diametral clock characteristics, such as oscillatory behaviour for local time measurement or decay-based clocks measuring time periods in scales global to the problem. We also include cascades of independent clocks ("clock bags") and composite clocks with controlled dependency; the latter show various regimes of markedly different dynamics.

Date: 23/06/2017
Time: 16:00
Location: LB252

Role of intraglomerular circuits in shaping temporally structured responses to naturalistic inhalation-driven sensory input to the olfactory bulb

2017-06-14T14:19:34+01:00

Olfaction in mammals is a dynamic process driven by the inhalation of air through the nasal cavity. Inhalation determines the temporal structure of sensory neuron responses and shapes the neural dynamics underlying central olfactory processing. Inhalation-linked bursts of activity among olfactory bulb (OB) output neurons [mitral/tufted cells (MCs)] are temporally transformed relative to those of sensory neurons. We investigated how OB circuits shape inhalation-driven dynamics in MCs using a modeling approach that was highly constrained by experimental results. First, we constructed models of canonical OB circuits that included mono- and disynaptic feedforward excitation, recurrent inhibition and feedforward inhibition of the MC. We then used experimental data to drive inputs to the models and to tune parameters; inputs were derived from sensory neuron responses during natural odorant sampling (sniffing) in awake rats, and model output was compared with recordings of MC responses to odorants sampled with the same sniff waveforms. This approach allowed us to identify OB circuit features underlying the temporal transformation of sensory inputs into inhalation-linked patterns of MC spike output. We found that realistic input-output transformations can be achieved independently by multiple circuits, including feedforward inhibition with slow onset and decay kinetics and parallel feedforward MC excitation mediated by external tufted cells. We also found that recurrent and feedforward inhibition had differential impacts on MC firing rates and on inhalation-linked response dynamics. These results highlight the importance of investigating neural circuits in a naturalistic context and provide a framework for further explorations of signal processing by OB networks.

Date: 30/06/2017
Time: 16:00
Location: LB252

Model driven engineering: an emerging technical space

2017-06-05T14:41:55+01:00

Marco Craveiro's journal club session where he presents the paper, "Model Driven Engineering: An Emerging Technical Space (Bézivin, 2006)".

As an emerging solution to the handling of complex and evolving software systems, Model Driven Engineering (MDE) is still very much in evolution. The industrial demand is quite high while the research answer for a sound set of foundation principles is still far from being stabilized. Therefore it is important to provide a current state of the art in MDE, describing what its origins are, what its present state is, and where it seems to be presently leading. One important question is how MDE relates to other contemporary technologies. This tutorial proposes the ”technical space” concept to this purpose. The two main objectives are to present first the basic MDE principles and second how these principles may be mapped onto modern platform support. Other issues that will be discussed are the applicability of these ideas, concepts, and tools to solve current practical problems. Various organizations and companies (OMG, IBM, Microsoft, etc.) are currently proposing several environments claiming to support MDE. Among these, the OMG MDATM(Model Driven Architecture) has a special place since it was historically one of the original proposals in this area. This work focuses on the identification of basic MDE principles, practical characteristics of MDE (direct representation, automation, and open standards), original MDE scenarios, and discussions of suitable tools and methods.

The objectives of this talk are as follows:

provide a general overview of Model Driven Engineering (MDE)
cover some of the difficulties of this approach

Date: 9/06/2017
Time: 16:00
Location: LB252

Open Position: Lecturer/Senior Lecturer in Computer Science (Machine Learning/Biocomputation)

2017-05-20T14:30:16+01:00

This position has been filled.

Salary £32,004 to £48,327 per annum depending on qualifications and experience
Full time position working 37 hours per week (1.0 FTE)
Closing date 18 June 2017

Applications are invited for a Lecturer or Senior Lecturer in the School of Computer Science, University of Hertfordshire. The successful candidate will be expected to contribute to the School's teaching and curriculum development activities, and to strengthen its research activities. We are looking to recruit specifically a computer scientist with background in machine learning or data science related to biocomputation (including computational neuroscience). By Data Science, we broadly mean the extraction of meaning from large quantities of data. The successful candidate will also have the flexibility to teach across mainstream topics in computer science. The School has an international reputation for teaching and research, with 58 academic staff, 20 adjunct lecturer staff, and 65 research students and postdoctoral research staff. With a history going back to 1958, the School teaches one of the largest cohorts of undergraduate students in the UK, and also delivers a thriving online computer science degree programme.

The person appointed will be expected to contribute to learning and teaching relevant to core computer science topics, participate in curriculum review and development, design and develop new modules, and supervise student projects at all levels. The appointee will strengthen the research culture in the School by pursuing research as part of a larger research team, seeking external funding, publishing papers, supervising research students, and participating in commercial activity as appropriate. Preference will be given to candidates who can contribute to teaching and research in databases as outlined above.

Applicants must hold a PhD (or equivalent) in a relevant subject, possess excellent communication skills in English and the ability to teach at undergraduate and postgraduate level. It is desirable that candidates have a track record of publication, external research funding, collaboration across disciplines, experience of different types of assessment and higher education quality assurance. They should also have the ability to play a role in the routine running of the School of Computer Science.

Applications should be made through http://www.herts.ac.uk/contact-us/jobs-and-vacancies/academic-vacancies (reference 014050). Informal enquiries may be addressed to Dr Volker Steuber (Head of the Biocomputation Research Group, v.steuber AT herts.ac.uk) or Professor William Clocksin (Dean of School, w.clocksin AT herts.ac.uk). Please note that applications sent directly to these email addresses will not be accepted.

We are committed to providing a supportive environment. The University offers a range of benefits including a pension scheme, professional development, family friendly policies, child care vouchers, a fee waiver of 50% for all children of staff under the age of 21 at the start of the course, discounted memberships at the Hertfordshire Sports Village and generous annual leave.

An introduction to NEURON

2017-05-11T10:05:33+01:00

Biological computational modelling is a powerful tool to simulate a system and draw conclusions regarding its function. It also allows to make predictions for processes that still haven’t been investigated in the laboratory. NEURON [1, 2] is an simulation environment, where empirical data are combined with analytic mathematical expressions, in order to model single neurons or neural networks. Neuronal cells are created as a series of connected sections, able to form realistic morphologies, and where different membrane properties (ionic, synaptic and passive) can be inserted. The interface and programming syntax are designed to offer an intuitive environment and emphasise on the biological functions in detail, rather than the the programming or numerical methods. The purpose of this workshop is to give an introduction to the NEURON software and how it could be used, by building a simple neuronal cell model and testing its behaviour under different conditions.

Carnevale, N.T. and Hines, M.L. The NEURON Book. Cambridge, UK: Cambridge University Press, 2006.
NEURON for empirically-based simulations of neurons and networks of neurons (2017). [online] Available at: https://www.neuron.yale.edu/

Date: 12/05/2017
Time: 16:00
Location: LB252

Loss of sensory input causes rapid structural changes of inhibitory neurons in adult mouse visual cortex

2017-04-24T11:17:31+01:00

Ankur Sinha's journal club session where he discusses the paper, "Loss of sensory input causes rapid structural changes of inhibitory neurons in adult mouse visual cortex (Keck et al. (2011))".

A fundamental property of neuronal circuits is the ability to adapt to altered sensory inputs. It is well established that the functional synaptic changes underlying this adaptation are reflected by structural modifications in excitatory neurons. In contrast, the degree to which structural plasticity in inhibitory neurons accompanies functional changes is less clear. Here, we use two-photon imaging to monitor the fine structure of inhibitory neurons in mouse visual cortex after deprivation induced by retinal lesions. We find that a subset of inhibitory neurons carry dendritic spines, which form glutamatergic synapses. Removal of visual input correlates with a rapid and lasting reduction in the number of inhibitory cell spines. Similar to the effects seen for dendritic spines, the number of inhibitory neuron boutons dropped sharply after retinal lesions. Together, these data suggest that structural changes in inhibitory neurons may precede structural changes in excitatory circuitry, which ultimately result in functional adaptation following sensory deprivation.

Date: 28/04/2017
Time: 16:00
Location: LB252

Multi-contact synapses for stable networks: a spike-timing dependent model of dendritic spine plasticity and turnover

2017-02-20T14:22:35+00:00

Ankur Sinha's journal club session where he discusses the pre-print paper, "Multi-contact synapses for stable networks: a spike-timing dependent model of dendritic spine plasticity and turnover (Deger, M., Seeholzer, A., Gerstner, W. (2016))"

Excitatory synaptic connections in the adult neocortex consist of multiple synaptic contacts, almost exclusively formed on dendritic spines. Changes of dendritic spine shape and volume, a correlate of synaptic strength, can be tracked in vivo for weeks. Here, we present a combined model of spike-timing dependent dendritic spine plasticity and turnover that explains the steady state multi-contact configuration of synapses in adult neocortical networks. In this model, many presynaptic neurons compete to make strong synaptic connections onto postsynaptic neurons, while the synaptic contacts comprising each connection cooperate via postsynaptic firing. We demonstrate that the model is consistent with experimentally observed long-term dendritic spine dynamics under steady-state and lesion induced conditions, and show that cooperation of multiple synaptic contacts is crucial for stable, long-term synaptic memories. In simulations of a simplified network of barrel cortex, our plasticity rule reproduces whisker-trimming induced rewiring of thalamo-cortical and recurrent synaptic connectivity on realistic time scales.

Date: 25/02/2017
Time: 16:00
Location: LB252