UH Biocomputation Group - olfaction

Metabolic activity organizes olfactory representations

2024-04-02T23:21:03+01:00

On this week's Journal Club session, Jim Bower will talk about the paper "Metabolic activity organizes olfactory representations".

Hearing and vision sensory systems are tuned to the natural statistics of acoustic and electromagnetic energy on earth and are evolved to be sensitive in ethologically relevant ranges. But what are the natural statistics of odors, and how do olfactory systems exploit them? Dissecting an accurate machine learning model (Lee et al., 2022) for human odor perception, we find a computable representation for odor at the molecular level that can predict the odor-evoked receptor, neural, and behavioral responses of nearly all terrestrial organisms studied in olfactory neuroscience. Using this olfactory representation (principal odor map [POM]), we find that odorous compounds with similar POM representations are more likely to co-occur within a substance and be metabolically closely related; metabolic reaction sequences (Caspi et al., 2014) also follow smooth paths in POM despite large jumps in molecular structure. Just as the brain’s visual representations have evolved around the natural statistics of light and shapes, the natural statistics of metabolism appear to shape the brain’s representation of the olfactory world.

Papers:

P. Wang, Y. Sun, R. Axel, L. Abbott, G. Yang, "Evolving the olfactory system with machine learning", 2021, Neuron, 109, 3879--3892.e5
W. Qian, J. Wei, B. Sanchez-Lengeling, B. Lee, Y. Luo, M. Vlot, K. Dechering, J. Peng, R. Gerkin, A. Wiltschko, "Metabolic activity organizes olfactory representations", 2023, eLife, 12, e82502
B. K. Lee et al., "A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception", 2022, bioRxiv, 504602

Date: 2024/04/05
Time: 14:00
Location: online

Functional Neurology of a Brain System: A 3D Olfactory Bulb Model to Process Natural Odorants

2023-05-04T11:00:52+01:00

This week on Journal Club session Maria Psarrou will talk about a paper "Functional Neurology of a Brain System: A 3D Olfactory Bulb Model to Process Natural Odorants".

The network of interactions between mitral and granule cells in the olfactory bulb is a critical step in the processing of odor information underlying the neural basis of smell perception. We are building the first computational model in 3 dimensions of this network in order to analyze the rules for connectivity and function within it. , The initial results indicate that this network can be modeled to simulate experimental results on the activation of the olfactory bulb by natural odorants, providing a much more powerful approach for 3D simulation of brain neurons and microcircuits.

Papers:

M. Migliore, F. Cavarretta, M. Hines, G. Shepherd, "Functional Neurology of a Brain System: A 3D Olfactory Bulb Model to Process Natural Odorants", 2013 -10- 17, Functional Neurology, 28, 241--243

Date: 2023/05/05
Time: 14:00
Location: online

Will Graph Neural Networks Revolutionise Computational Olfaction?

2023-02-23T08:57:15+00:00

This week on Journal Club session Michael Schmuker will talk about a paper "Will Graph Neural Networks Revolutionise Computational Olfaction?".

How to predict the smell of a molecule given it's chemical structure? A group of researchers around Google's Alex Wiltschko have used Graph Neural Networks (GNN) to develop apparently outperform previous approaches to predict the smell of an odorant [1]. They proposed the "Principal Odor Map" (POM): a latent space which GNN learn from a data set of several thousand odorants. They show how the POM improves prediction of scent [2], how it aligns with metabolic pathways producing odors [3], and how it gives rise to the design of new mosquito repellents [4]. The group has now launched a computational olfaction startup [5]. In my talk I will introduce the GNN method and their approach to produce the POM, summarise their results, and discuss how the performance of their model compares to other established methods.

In my talk I will introduce the GNN method and their approach to produce the POM, summarise their results, and discuss how the performance of their model compares to other established methods.

Papers:

[1] B. Sanchez-Lengeling, J. Wei, B. Lee, R. Gerkin, A. Aspuru-Guzik, A. Wiltschko, "Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules", 2019, arXiv,
[2] B. Lee, E. Mayhew, B. Sanchez-Lengeling, J. Wei, W. Qian, K. Little, M. Andres, B. Nguyen, T. Moloy, J. Parker, R. Gerkin, J. Mainland, A. Wiltschko, "A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception", 2022,
[3] W. Qian, J. Wei, B. Sanchez-Lengeling, B. Lee, Y. Luo, M. Vlot, K. Dechering, J. Peng, R. Gerkin, A. Wiltschko, "Metabolic Activity Organizes Olfactory Representations", 2022,
[4] J. Wei, M. Vlot, B. Sanchez-Lengeling, B. Lee, L. Berning, M. Vos, R. Henderson, W. Qian, D. Ando, K. Groetsch, R. Gerkin, A. Wiltschko, K. Dechering, "A Deep Learning and Digital Archaeology Approach for Mosquito Repellent Discovery", 2022,
[5] "https://osmo.ai/"

Date: 2023/02/24
Time: 14:00
Location: online

Using Head-Mounted Ethanol Sensors to Monitor Olfactory Information and Determine Behavioral Changes Associated with Ethanol-Plume Contact during Mouse Odor-Guided Navigation

2022-02-24T16:10:25+00:00

This week on Journal Club session Michael Schmuker will talk about a paper "Using Head-Mounted Ethanol Sensors to Monitor Olfactory Information and Determine Behavioral Changes Associated with Ethanol-Plume Contact during Mouse Odor-Guided Navigation".

Olfaction guides navigation and decision-making in organisms from multiple animal phyla. Understanding how animals use olfactory cues to guide navigation is a complicated problem for two main reasons. First, the sensory cues used to guide animals to the source of an odor consist of volatile molecules, which form plumes. These plumes are governed by turbulent air currents, resulting in an intermittent and spatiotemporally varying olfactory signal. A second problem is that the technologies for chemical quantification are cumbersome and cannot be used to detect what the freely moving animal senses in real time. Understanding how the olfactory system guides this behavior requires knowing the sensory cues and the accompanying brain signals during navigation. Here, we present a method for real-time monitoring of olfactory information using low-cost, lightweight sensors that robustly detect common solvent molecules, like alcohols, and can be easily mounted on the heads of freely behaving mice engaged in odor- guided navigation. To establish the accuracy and temporal response properties of these sensors we compared their responses with those of a photoionization detector (PID) to precisely controlled ethanol stimuli. Ethanol-sensor recordings, deconvolved using a difference-of- exponentials kernel, showed robust correlations with the PID signal at behaviorally relevant time, frequency, and spatial scales. Additionally, calcium imaging of odor responses from the olfactory bulbs (OBs) of awake, head-fixed mice showed strong correlations with ethanol plume contacts detected by these sensors. Finally, freely behaving mice engaged in odor-guided navigation showed robust behavioral changes such as speed reduction that corresponded to ethanol plume contacts.

Papers:

M. Tariq, S. Lewis, A. Lowell, S. Moore, J. Miles, D. Perkel, D. Gire, "Using Head-Mounted Ethanol Sensors to Monitor Olfactory Information and Determine Behavioral Changes Associated with Ethanol-Plume Contact during Mouse Odor-Guided Navigation", 2021, eneuro, 8, ENEURO.0285-20.2020

Date: 2022/02/25
Time: 14:00
Location: online

Manipulating Synthetic Optogenetic Odors Reveals the Coding Logic of Olfactory Perception

2022-02-02T17:57:48+00:00

This week on Journal Club session Maria Psarrou will talk about a paper "Manipulating Synthetic Optogenetic Odors Reveals the Coding Logic of Olfactory Perception".

How does neural activity generate perception? Finding the combinations of spatial or temporal activityfeatures (such as neuron identity or latency) that are consequential for perception remains challenging. We trained mice to recognize synthetic odors constructed from parametrically defined patterns ofoptogenetic activation, then measured perceptual changes during extensive and controlled perturbationsacross spatiotemporal dimensions. We modeled recognition as the matching of patterns to learnedtemplates. The templates that best predicted recognition were sequences of spatially identified units,ordered by latencies relative to each other (with minimal effects of sniff). Within templates, individualunits contributed additively, with larger contributions from earlier-activated units. Our syntheticapproach reveals the fundamental logic of the olfactory code and provides a general framework fortesting links between sensory activity and perception.

Papers:

E. Chong, M. Moroni, C. Wilson, S. Shoham, S. Panzeri, D. Rinberg, "Manipulating Synthetic Optogenetic Odors Reveals the Coding Logic of Olfactory Perception", 2020, Science, 368, eaba2357

Date: 2022/02/04
Time: 14:00
Location: online

Complementary Codes for Odor Identity and Intensity in Olfactory Cortex

2021-11-17T10:13:44+00:00

This week on Journal Club session Shavika Rastogi will talk about a paper "Complementary Codes for Odor Identity and Intensity in Olfactory Cortex".

The ability to represent both stimulus identity and intensity is fundamental for perception. Using large-scale population recordings in awake mice, we find distinct coding strategies facilitate non- interfering representations of odor identity and intensity in piriform cortex. Simply knowing which neurons were activated is sufficient to accurately represent odor identity, with no additional information about identity provided by spike time or spike count. Decoding analyses indicate that cortical odor representations are not sparse. Odorant concentration had no systematic effect on spike counts, indicating that rate cannot encode intensity. Instead, odor intensity can be encoded by temporal features of the population response. We found a subpopulation of rapid, largely concentration-invariant responses was followed by another population of responses whose latencies systematically decreased at higher concentrations. Cortical inhibition transforms olfactory bulb output to sharpen these dynamics. Our data therefore reveal complementary coding strategies that can selectively represent distinct features of a stimulus.

Papers:

K. Bolding, K. Franks, "Complementary Codes for Odor Identity and Intensity in Olfactory Cortex", 2017, eLife, 6, e22630

Date: 2021/11/19
Time: 14:00
Location: online

Fast Odour Dynamics Are Encoded in the Olfactory System and Guide Behaviour

2021-05-06T11:21:00+01:00

This week on Journal Club session Maria Psarrou will talk about a paper "Fast Odour Dynamics Are Encoded in the Olfactory System and Guide Behaviour".

Odours are transported in turbulent plumes, which result in rapid concentration fluctuations that contain rich information about the olfactory scenery, such as the composition and location of an odour source. However, it is unclear whether the mammalian olfactory system can use the underlying temporal structure to extract information about the environment. Here we show that ten-millisecond odour pulse patterns produce distinct responses in olfactory receptor neurons. In operant conditioning experiments, mice discriminated temporal correlations of rapidly fluctuating odours at frequencies of up to 40 Hz. In imaging and electrophysiological recordings, such correlation information could be readily extracted from the activity of mitral and tufted cells the output neurons of the olfactory bulb. Furthermore, temporal correlation of odour concentrations reliably predicted whether odorants emerged from the same or different sources in naturalistic environments with complex airflow. Experiments in which mice were trained on such tasks and probed using synthetic correlated stimuli at different frequencies suggest that mice can use the temporal structure of odours to extract information about space. Thus, the mammalian olfactory system has access to unexpectedly fast temporal features in odour stimuli. This endows animals with the capacity to overcome key behavioural challenges such as odour source separation, figure ground segregation and odour localization by extracting information about space from temporal odour dynamics.

Papers:

T. Ackels, A. Erskine, D. Dasgupta, A. Marin, T. Warner, S. Tootoonian, I. Fukunaga, J. Harris, A. Schaefer, "Fast Odour Dynamics Are Encoded in the Olfactory System and Guide Behaviour", 2021, Nature

Date: 2021/05/06
Time: 14:00
Location: online

Navigation Along Windborne Plumes of Pheromone and Resource-Linked Odors

2020-11-06T11:23:21+00:00

This week on Journal Club session Samuel Sutton will talk about the paper "Navigation Along Windborne Plumes of Pheromone and Resource-Linked Odors".

Many insects locate resources such as a mate, a host, or food by flying upwind along the odor plumes that these resources emit to their source. A windborne plume has a turbulent structure comprised of odor filaments interspersed with clean air. As it propagates downwind, the plume becomes more dispersed and dilute, but filaments with concentrations above the threshold required to elicit a behavioral response from receiving organisms can persist for long distances. Flying insects orient along plumes by steering upwind, triggered by the optomotor reaction. Sequential measurements of differences in odor concentration are unreliable indicators of distance to or direction of the odor source. Plume intermittency and the plume's fine-scale structure can play a role in setting an insect's upwind course. The prowess of insects in navigating to odor sources has spawned bioinspired virtual models and even odor-seeking robots, although some of these approaches use mechanisms that are unnecessarily complex and probably exceed an insect's processing capabilities.

Papers:

Carde, Ring T. "Navigation Along Windborne Plumes of Pheromone and Resource-Linked Odors" , Annual Review of Entomology 2021 66:1

Date: 06/11/2020 Time: 16:00
Location: online

Extraordinary performance of semiconducting metal oxide gas sensors using dielectric excitation

2020-06-10T13:00:53+01:00

This week on Journal Club session Ritesh Kumar will talk about impedance spectroscopy and its use in the design of electronic tongue and nose systems. Specifically, he will refer to the paper by Radislav A. Potyrailo et al. along with some of his previous works.

Impedance spectroscopy is a powerful technique which has been applied to the design of instruments for characterising liquids and solids. The ‘impedance fingerprints’ obtained at various frequencies can be used to classify, define sensitivity, selectivity, linearity of systems. It uses a sweep of sinusoidal frequencies as perturbation signal at low voltage so as to remain in the linear and causal domain. In this talk, I will be presenting about impedance spectroscopy and its use in the design of electronic tongue and nose systems in general and specifically the paper by Radislav A. Potyrailo et al. along with some of our previous works in the design of Electronic Tongue systems. The paper by Radislav A. Potyrailo et al. shows that the run-of-the mill metal oxide gas sensors can act as high performance sensors using the impedance measurements. They show exemplary performance in terms of linearity, limit of detection, cross-sensitivity etc. This can pave way for the design of low cost and efficient electronic nose systems.

Papers:

Potyrailo, R.A., Go, S., Sexton, D. et al. "Extraordinary performance of semiconducting metal oxide gas sensors using dielectric excitation" ,Nat Electron 3, 280–289 (2020). https://doi.org/10.1038/s41928-020-0402-3
Ritesh Kumar, Amol P. Bhondekar, Rishemjit Kaur, Saru Vig, Anupma Sharma, Pawan Kapur, "A simple electronic tongue" , Sensors and Actuators B: Chemical, Volumes 171–172, 2012, Pages 1046-1053, ISSN 0925-4005, https://doi.org/10.1016/j.snb.2012.06.031.
Amol P. Bhondekar, Mopsy Dhiman, Anupma Sharma, Arindam Bhakta, Abhijit Ganguli, S.S. Bari, Renu Vig, Pawan Kapur, Madan L. Singla, "A novel iTongue for Indian black tea discrimination" , Sensors and Actuators B: Chemical, Volume 148, Issue 2, 2010, Pages 601-609, ISSN 0925-4005, https://doi.org/10.1016/j.snb.2010.05.053.

Date: 12/06/2020
Time: 16:00
Location: online

Hyperbolic geometry of the olfactory space

2020-02-05T12:56:32+00:00

This week on Journal Club session Emil Dmitruk talk about the paper "Hyperbolic geometry of the olfactory space".

In the natural environment, the sense of smell, or olfaction, serves to detect toxins and judge nutritional content by taking advantage of the associations between compoundsas they are created in biochemical reactions. This suggests that the nervous system can classify odors based on statistics of their co-occurrence within natural mixtures rather than from the chemical structures of the ligands themselves. We show that this statistical perspective makes it possible to map odors to points in a hyperbolic space. Hyperbolic coordinates have a long but often underappreciated history of relevance to biology. For example, these coordinates approximate the distance between species computed along dendrograms and, more generally, between points within hierarchical tree–like networks. We find that both natural odors and human perceptual descriptions of smells can be described using a three-dimensional hyperbolic space. This match in geometries can avoid distortions that would otherwise arise when mapping odors to perception.

Papers:

Yuansheng Zhou et al. (2019) "Hyperbolic geometry of the olfactory space" , Science Advances 29 Aug 2018: Vol. 4, no. 8.

Date: 07/02/2020
Time: 16:00
Location: B200

Biocomputation Robots and ArchaeaBot Project

2018-10-31T13:25:13+00:00

Alex May and Anna Dumitriu's journal club session on their latest collaborative robotics artwork projects “ArchaeaBot” and “BioCompuation Bots”.

Anna Dumitriu and Alex May (Visiting Research Fellows: Artists in Residence in Computer Science at the University of Hertfordshire) will discuss their latest projects “ArchaeaBot” and “BioCompuation Bots” and demonstrate some of these new robotic artworks.

“ArchaeaBot: A Post Singularity and Post Climate Change Life-form” takes the form of an underwater robotic installation that explores what ‘life’ might mean in a post singularity, post climate change future. The project is based on new research about archaea (the oldest life forms on Earth) combined with machine learning & artificial intelligence to create the ‘ultimate’ species for the end of the world as we know it. The project has made in collaboration with researcher/cryomicroscopist Amanda Wilson as part of the EU FET Open H2020 funded MARA project based in the Beeby Lab at Imperial College London, and with Professor Daniel Polani from the School of Computer Science at the University of Hertfordshire. The project is supported through an EMAP/EMARE artists’ residency at LABoral Centro de Arte y Creación Industrial in Spain via funding from Creative Europe and with generous support from Arts Council England.

“BioCompuation Bots” are a brand-new series of artworks made collaboration with Professor Volker Steuber from the School of Computer Science at the University of Hertfordshire and artistically respond directly to research projects being undertaken within the university and with international collaborators in order to engage the public and international research community:

Two mouse-like quadruped robots explore research into controlling Petit Mal epilepsy using LEDs embedded in the brain that can ‘reset’ genetically modified cells before a fit occurs. In the research a complex data set was analysed to work out the perfect moment to turn on the fit stopping LED, in the artwork audiences use a blue torch to reset the robot’s ‘brains’ and ‘unfreeze’ them from their virtual fit. Also in collaboration with Dr Freek Hoebeek at Erasmus University in Rotterdam.

Another robot on tracked wheels roams around searching for smells that appeal to it and focusses on a kind of perfume designed to appeal to it. Senses such as smell are widely considered to be uniquely related to biological life and closely related to our understanding of consciousness, an assumption that this artwork throws into question. The robot explores artificial nose research being undertaken at the university with Dr Michael Schmucker.

Date: 30/11/2018
Time: 16:00
Location: D120

Decoding gas source proximity from turbulent plumes

2018-05-31T10:15:31+01:00

Estimating the distance of a gas source is important in many applications of chemical sensing, like e.g. environmental monitoring, or chemically-guided robot navigation. If an estimation of the gas concentration at the source is available, source proximity can be estimated from the time-averaged gas concentration at the sensing site. However, in turbulent environments, where fast concentration fluctuations dominate, comparably long measurements are required to obtain a reliable estimate. A lesser known feature that correlates with source proximity in a turbulent environment is the temporal variance of local gas concentration: Gas encounters become more intermittent farther from the source. However, is has commonly been assumed that exploiting this feature requires gas concentration measurements at the millisecond scale, usually requiring expensive photo-ionisation detectors. We have recently shown that, with appropriate signal processing, off-the-shelf metal-oxide sensors are capable of extracting rapidly fluctuating features of gas plumes that strongly correlate with source distance [1]. We present a straightforward analysis method to decode events of large, consistent changes in the measured signal, which we denote ‘bouts’. The frequency of these bouts predicted the distance of a gas source in wind-tunnel experiments with good accuracy. In addition, we found that the variance of bout counts indicates cross-wind offset to the centre- line of the gas plume. Our results offer an alternative approach to estimating gas source proximity that is largely independent of gas concentration, using off-the-shelf metal-oxide sensors.

[1] Michael Schmuker, Viktor Bahr, and Ramón Huerta, “Exploiting Plume Structure to Decode Gas Source Distance Using Metal-Oxide Gas Sensors,” Sensors and Actuators B: Chemical 235 (November 2016): 636–46. Open access version available at https://arxiv.org/abs/1602.01815.

Date: 01/06/2018
Time: 16:00
Location: LB252

Olfactory coding in the turbulent realm

2018-02-12T14:36:04+00:00

Long-distance olfactory search behaviors depend on odor detection dynamics. Due to turbulence, olfactory signals travel as bursts of variable concentration and spacing and are characterized by long-tail distributions of odor/no-odor events, challenging the computing capacities of olfactory systems. How animals encode complex olfactory scenes to track the plume far from the source remains unclear. Here we focus on the coding of the plume temporal dynamics in moths. We compare responses of olfactory receptor neurons (ORNs) and antennal lobe projection neurons (PNs) to sequences of pheromone stimuli either with white-noise patterns or with realistic turbulent temporal structures simulating a large range of distances (8 to 64 m) from the odor source. For the first time, we analyze what information is extracted by the olfactory system at large distances from the source. Neuronal responses are analyzed using linear–nonlinear models fitted with white-noise stimuli and used for predicting responses to turbulent stimuli.

Date: 16/02/2018
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

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

Open Position: PhD studentships in Computational Neuroscience

2017-05-31T13:20:03+01:00

Applications are invited for PhD positions in the Biocomputation Research Group at the University of Hertfordshire. Projects involve the development and simulation of models of neurons and neuronal networks to study information processing in the cerebellum or olfactory system and/or the application of machine learning techniques for the analysis of neural data. A description of our research interests and a list of publications can be found on our webpage (http://biocomputation.herts.ac.uk/).

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 14,553 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 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

What does the nose know and how does it know it

2016-05-13T10:48:13+01:00

James Bower joins us for a special journal club session where he discusses olfactory processing.

Since Lucretius published his epic poem, De rerum natura (On the Nature of Things) in 66 BC, philosophical and scientific thinking about the sense of smell has been built on the assumption that the olfactory system detects odours through a process of classification based on analytical chemical structures. Somewhat similarly, starting with Linnaeus in the mid 18th century, various efforts have been made to regularize odour perception by identifying different scales or perceptual groupings. For the last 100 years many attempts have been made to correlate chemical characteristics believed important to detection to these classification schemes for perception. All have failed. This talk will describe the origins and implications of the alternative view that the olfactory system, both detection and perception is organized around the biological significance of an odorant molecule rather than its strict chemical form. Evidence in support will be presented from a range of approaches from human psychophysics to receptor ligand binding studies to neuronal modelling. The talk will also consider the possible implications for the function of cerebral cortex as a whole, given the likely olfactory origin of the cerebral cortical processing algorithm.

Date: 20/05/2016
Time: 16:00
Location: LB252

Open Position: PhD studentship in Olfactory Biocomputation

2016-04-28T17:32:53+01:00

Applications are invited for a fully funded PhD position in the Biocomputation Research group at the University of Hertfordshire. Our research on Olfactory Biocomputation encompasses the following topics:

Olfactory computing in insects and vertebrates
The role of stimulus dynamics in olfaction
Chemical “receptive fields” of odorant receptors
Neuromorphic computing and bio-inspired signal processing for chemical sensing

Our spectrum of methods covers data science and machine learning, simulation of spiking networks, cheminformatics, and brain-like computing on neuromorphic hardware. The successful candidate should ideally have previous experience in one or more of these methods, but a keen interest in our research topics and enthusiasm for interdisciplinary research is considered essential. Excellent programming skills are required and should be documented upon application. Most of our code is written in Python.

Depending on the area of work, the successful candidate will join our collaborative research efforts with excellent experimental research groups, as e.g. led by Prof. Andreas Schaefer (Francis Crick Institute, London), Dr. Markus Knaden and Dr. Silke Sachse (Max-Planck Institute for Chemical Ecology, Jena, Germany). For a list of recent projects and publications please refer to the web pages of the BioMachineLearning Project and the Biocomputation Group.

The student will be supervised by Drs. Michael Schmuker (m.schmuker @ biomachinelearning.net) and Volker Steuber (v.steuber @ herts.ac.uk). Informal enquiries by email prior to application are encouraged and very welcome.

Successful candidates are eligible for a research studentship award from the University (approximately GBP 14,250 per annum bursary plus payment of the student fees).

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

Application forms can be obtained from:

Mrs Lorraine Nicholls,
Research Student Administrator,
STRI,
University of Hertfordshire,
College Lane,
Hatfield, Herts,
AL10 9AB,
Tel: +44 01707 286083,
l.nicholls @ herts.ac.uk.

The short-listing process will begin on 30 May, 2016.

Exploring neural computation in odour space

2016-03-09T13:39:34+00:00

This session has been moved to 11th of March.

Michael Schmuker joins us for a special journal club session where he speaks about his research work related to olfactory processing. Abstract below.

Our sense of smell enables us to explore the world of chemical information. Yet, our knowledge on the structure of chemical stimulus space still lacks far behind other modalities like vision or hearing. This lack of knowledge currently presents a major roadblock for understanding how the brain efficiently encodes chemical information. Moreover, a better understanding of odour space, and how it is processed in the brain, may also enable bio-inspired design of efficient technical solutions for chemical sensing.

In this presentation, I will give an overview on our research on how the olfactory systems of insects and vertebrates encode and transform chemical information on its way from the primary sensors to higher brain areas. These investigations inspired us to implement the key concepts of olfactory processing on a neuromorphic hardware system that uses spiking neuronal networks to perform pattern recognition in high-dimensional feature spaces. I will also present our recent findings on how to extract information about source distance from the fine-structure of gas plumes.

Date: ~~12/02/2016~~ 11/03/2016
Time: 16:00
Location: LB252