Publications and Dissemination

Recent NEU-CHiP Publications

  • Wei Wang, Barak Hoffer, Tzofnat Greenberg-Toledo, Yang Li, Minhui Zou, Eric Herbelin, Ronny Ronen, Xiaoxin Xu, Yulin Zhao, Jianguo Yang, and Shahar Kvatinsky, Efficient Training of the Memristive Deep Belief Net Immune to Non-Idealities of the Synaptic Devices, Adv. Intell. Syst. 2022, 4, 210024 LINK
  • Lisa Mapelli , Olivier Dubochet Mariateresa Tedesco, Giacomo Sciacca, Alessandra Ottaviani, Anita Monteverdi, Chiara Battaglia, Simona Tritto, Francis Cardot, Patrick Surbled, Jan Schildknecht, Mauro Gandolfo, Kilian Imfeld, Chiara Cervetto, Manuela Marcoli, Egidio D’Angelo, Alessandro Maccione, Design, implementation, and functional validation of a new generation of microneedle 3D high-density CMOS multi-electrode array for brain tissue and spheroids, Preprint from bioRxiv, 15 Aug 2022 LINK
  • NOLTA Paper ID 6214: 2D and 3D Cortical Network Differentiation and Functional Assessment
    David Jenkins, Adele Ludlam, Eric Hill, Rhein Parri
    ABSTRACT: Investigating iPSC derived cortical cells requires functional neurons and glia. Here, induced pluripotent stem cells are differentiated and engineered into specific, mature neural networks. Here we assess the functional output of these complex cultures through calcium imaging and high-density multi electrode array assessment. Excitatory and inhibitory neurons as well as astrocytes were characterised through functionality and ICC. 
  • NOLTA Paper ID 6134: Engineering Approaches to Control Biological Responses – ‘Fabricating Neural Circuitry’ 
    Daniel Merryweather, Dan Earl, Luis Marcos, David Jenkins, Rhein Parri, Eric Hill, Paul Roach
    ABSTRACT: Using microfabrication techniques we demonstrate the production of a bespoke multi-population living neuronal network. Using bespoke microchannel internal geometry, we show directed cell mobility and guided connectivity through use of micro-channels in polydimethylsiloxane (PDMS) devices. These networks are demonstrated using both a SY-SY5Y cell line and larger cell clusters. 
  • NOLTA Paper ID 6143: Liquid State Computing in Neuronal Cultures: Effects of Noise and Connectivity Modularity on Response Separation and Generalisation in Numerical Simulations 
    Akke Mats Houben, Jordi Garcia-Ojalvo, Jordi Soriano
    ABSTRACT: Using a numerical model of biological neuronal cultures, we show that activity in response to different stimuli separate, while responses to similar inputs coalesce. Moreover, we explore the effects of noise and network connectivity modularity on this separation-generalisation capacity. 
  • NOLTA Paper ID 6148: Experiments on Modular Neuronal Cultures Monitored by High-Density Multielectrode Arrays 
    Anna-Christina Haeb, Akke Mats Houben, Jordi Soriano, Daniel Tornero
    ABSTRACT: Here we studied the spontaneous activity in biological neuronal circuits to gain insight into the relationship between neuronal circuit connectivity and the emerging collective dynamics. For that, a complementary metaloxide-semiconductor (CMOS)–based high–density multielectrode array (HD–MEA) device was used to measure the external field potential of neuronal cultures with high temporal resolution, providing detailed information about the spontaneous activity and effective connectivity of the in vitro circuits. By integrating an engineered polydimethylsiloxane (PDMS) pattern onto the HD–MEA, which shaped a modular–like network, we observed that the repertoire of spontaneous activity patterns in the network was increased, thus revealing a relationship between modularity and the richness of activity patterns. 
  • NOLTA Paper ID 6199: Controlling the Connectivity of Networks of Neurons Through Stimulations: A Protocol for Rate Models 
    Francesco Borra, Sebastien Wolf, Simona Cocco, Rémi Monasson
    ABSTRACT: The manipulation of the connectivity of biological neural networks is important both for in vivo applications and especially for neural computation in vitro. We outline the proof of concept a self-contained procedure which, by exploiting neural plasticity and stimulation (such as optogenetics or electrical stimulation) beyond standard associative learning, is intended at rewiring a network in a desired state. The procedure consists in a sequence of stimulations which are delivered in a self-correcting training loop, which progressively drives the network towards a functional configuration, according to a prescribed metrics. As examples, we provide two idealised implementations in silico: a classic firing rate-base classification task and the construction of a continuous attractor. 
  • NOLTA Paper ID 6110: Effective Structure Inference from Multi-Phase Cortical Neural Activities
    Ho Fai Po, Akke Mats Houben, Jordi Soriano, David Saad 
    ABSTRACT: Effective structure of neuronal networks is in- ferred from firing data using Expectation-Maximization (EM) in conjunction with the inverse Ising model. We demonstrate that our algorithm infers the effective con- nectivity, neuronal cell-type and coupling strength using controllable synthetic and emulator data. Furthermore, we show that the neural activity predictions obtained using the inferred structures have a good fit with the original data. 

Dissemination NEU-CHiP Activities

UKSB2022 – UK Society for Biomaterials, University of Sheffield UK June 2022

TCES – Tissue and Cell Engineering Society, Birmingham June 2022

FIRM2022 – Future Investigators in Regenerative Medicine, May 2022 – news link

Making Pharmaceuticals – Coventry Stadium, April 2022

Organ Modelling Congress – Oxford Global, London, April 2022

Student work experience – St John’s Catholic College Cardiff Summer 2022 – news link

Dementia Researcher Podcast – news link

A number of group members presented work at the International NOLTA conference hosted in Sicily 2023 – more details here.