UH Biocomputation Group - Shabnam Kadir

Combinatorial threshold linear networks and how dynamics can be ascertained via graph motifs in the connectivity matrix.

2025-03-12T22:00:58+00:00

On this week's Journal Club session, Shabnam Kadir will talk about "Combinatorial threshold linear networks and how dynamics can be ascertained via graph motifs in the connectivity matrix.".

I shall talk about combinatorial threshold linear networks and how dynamics can be ascertained via graph motifs in the connectivity matrix. I shall explore ways in which it could be connected to Yaqoob’s work:

M. Yaqoob, V. Steuber, B. Wróbel, "Autapses enable temporal pattern recognition in spiking neural networks", 2023, bioRxiv.

Papers:

C. Curto, K. Morrison, "Graph rules for recurrent neural network dynamics: extended version", 2023, arXiv,
M. Yaqoob, V. Steuber, B. Wróbel, "Autapses enable temporal pattern recognition in spiking neural networks", 2023, bioRxiv,
C. Curto, J. Geneson, K. Morrison, "Stable fixed points of combinatorial threshold-linear networks", 2023, arXiv,

Date: 2025/03/14
Time: 14:00
Location: SP3011 & online

Visual experience, topology, and the perception of art

2024-06-11T23:12:40+01:00

On this week's Journal Club session, Shabnam Kadir will talk about her work in the presentation entitled "Visual experience, topology, and the perception of art".

The brain has access to visual information via a variety of neural codes, e.g. there are cells tuned to edge orientation, spatial frequency, edges, corners, contrasts and colour. Higher cortical areas of the brain are thought to then further process visual information as well as integrate a visual scene with other senses and the conscious mind. How this visual information is combined with experience to form a holistic experience that elicits an emotional response of pleasure, distress and/or other forms of meaning is an active research question. The human perception of art and the question of what constitutes art touch upon all these elements of perception and integration. We shall show how topology provides a vital new lens in examining these questions.

We discuss recent work where methods from applied topology, namely persistent homology of certain cubical complexes, are applied to images produced by both a human artist and a neural network shown in two exhibitions in Torun, Poland. The images shown in the two exhibitions were matched for certain information-theoretic pixel-based characteristics. Experimental measurements of EEG, tracking of eye movement, as well as conscious perception/appreciation of these paintings were collected from a selection of visitors to these exhibitions, namely second-year art students.

Date: 2024/06/14
Time: 14:00
Location: C258 & online

Consciousness and Information Theory

2023-02-15T16:34:20+00:00

This week on Journal Club session Shabnam Kadir will talk about Consciousness and Information Theory based on selection of 3 papers. For more information, please see the list of papers and an abstract below.

This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific - it is what it is by how it differs from alternative experiences; integration says that it is unified - irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or logic gates, must be configured to generate experience (phenomenology). The postulates are used to define intrinsic information as "differences that make a difference" within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure (MICS, a constellation of concepts in qualia space), and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information Φ_Max its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true "zombies" - unconscious feed-forward systems that are functionally equivalent to conscious complexes.

Papers:

L. Albantakis, L. Barbosa, G. Findlay, M. Grasso, A. Haun, W. Marshall, W. Mayner, A. Zaeemzadeh, M. Boly, B. Juel, S. Sasai, K. Fujii, I. David, J. Hendren, J. Lang, G. Tononi, "Integrated Information Theory (IIT) 4.0: Formulating the Properties of Phenomenal Existence in Physical Terms", 2022, arXiv,
J. Jost, "Information Theory and Consciousness", 2021, Frontiers in Applied Mathematics and Statistics, 7,
M. Oizumi, L. Albantakis, G. Tononi, "From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0", 2014, PLOS Computational Biology, 10, e1003588

Date: 2023/02/17
Time: 14:00
Location: online

Feasibility of Topological Data Analysis for Event-Related fMRI

2021-05-20T09:12:19+01:00

This week on Journal Club session Shabnam Kadir will talk about a paper "Feasibility of Topological Data Analysis for Event-Related fMRI".

Recent fMRI research shows that perceptual and cognitive representations are instantiated in high-dimensional multivoxel patterns in the brain. However, the methods for detecting these representations are limited. Topological data analysis (TDA) is a new approach, based on the mathematical field of topology, that can detect unique types of geometric features in patterns of data. Several recent studies have successfully applied TDA to study various forms of neural data; however, to our knowledge, TDA has not been successfully applied to data from event-related fMRI designs. Event-related fMRI is very common but limited in terms of the number of events that can be run within a practical time frame and the effect size that can be expected. Here, we investigate whether persistent homology- a popular TDA tool that identifies topological features in data and quantifies their robustness- can identify known signals given these constraints. We use fmrisim, a Python-based simulator of realistic fMRI data, to assess the plausibility of recovering a simple topological representation under a variety of conditions. Our results suggest that persistent homology can be used under certain circumstances to recover topological structure embedded in realistic fMRI data simulations.How do we represent the world? In cognitive neuroscience it is typical to think representations are points in high-dimensional space. In order to study these kinds of spaces it is necessary to have tools that capture the organization of high- dimensional data. Topological data analysis (TDA) holds promise for detecting unique types of geometric features in patterns of data. Although potentially useful, TDA has not been applied to event-related fMRI data. Here we utilized a popular tool from TDA, persistent homology, to recover topological signals from event-related fMRI data. We simulated realistic fMRI data and explored the parameters under which persistent homology can successfully extract signal. We also provided extensive code and recommendations for how to make the most out of TDA for fMRI analysis.

Papers:

C. Ellis, M. Lesnick, G. {Henselman-Petrusek}, B. Keller, J. Cohen, "Feasibility of Topological Data Analysis for Event-Related {{fMRI}}", 2019, Network Neuroscience, 3, 695--706
C. Giusti, E. Pastalkova, C. Curto, V. Itskov, "Clique Topology Reveals Intrinsic Geometric Structure in Neural Correlations", 2015, National Academy of Sciences, 13455--13460
B. Stolz, H. Harrington, M. Porter, "Persistent Homology of Time-Dependent Functional Networks Constructed from Coupled Time Series", 2017, Chaos: An Interdisciplinary Journal of Nonlinear Science, 27, 047410
A. Zomorodian, "Computing Persistent Homology", 2005, Discrete & Computational Geometry, 33, 249--274

Date: 2021/05/21
Time: 14:00
Location: online

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).

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.