Nonlinear Dynamics of Neural Circuits
Lead Research Organisation:
University of Bath
Department Name: Physics
Abstract
This research will focus on probing experimentally the chaotic dynamics of networks of competing neurons and on programming the adaptation of their voltage oscillations to time dependent stimuli. This study will find important applications in the development of solid state central pattern generators (CPGs) for bioelectronic medicine.
CPGs are small networks of neurons which make use of intrinsic bursting patterns to produce rhythmic patterned outputs without rhythmic sensory or central input. Locomotion, heart beating and respiration are among the biological functions for which CPGs are responsible. The realisation of artificial CPGs would therefore provide the technology for modulating rhythms in the body which have been lost due to illness. A goal of this research will be the replication in artificial hardware of the respiratory CPG, which produces rhythmic drive for motoneurons controlling respiratory muscles. The firing patterns are locked to different phases of the respiratory cycle, and are responsible for respiratory sinus arrhythmia, a form of heart rate variability, which is lost in cases of heart failure and theorised to optimize efficiency of gas exchange during respiration.
Bursting polyrhythms of CPG networks will be identified, and their respective bifurcations explored with variable synaptic conductances and network size. Nonlinear optimization will be utilized to construct complex models of specialized neuron types in order to explore the characteristic dynamics of unique neural networks.
CPGs are small networks of neurons which make use of intrinsic bursting patterns to produce rhythmic patterned outputs without rhythmic sensory or central input. Locomotion, heart beating and respiration are among the biological functions for which CPGs are responsible. The realisation of artificial CPGs would therefore provide the technology for modulating rhythms in the body which have been lost due to illness. A goal of this research will be the replication in artificial hardware of the respiratory CPG, which produces rhythmic drive for motoneurons controlling respiratory muscles. The firing patterns are locked to different phases of the respiratory cycle, and are responsible for respiratory sinus arrhythmia, a form of heart rate variability, which is lost in cases of heart failure and theorised to optimize efficiency of gas exchange during respiration.
Bursting polyrhythms of CPG networks will be identified, and their respective bifurcations explored with variable synaptic conductances and network size. Nonlinear optimization will be utilized to construct complex models of specialized neuron types in order to explore the characteristic dynamics of unique neural networks.
Organisations
People |
ORCID iD |
| Alain Nogaret (Primary Supervisor) | |
| Joseph Taylor (Student) |
Publications
Chauhan AS
(2018)
Dual Mechanism for the Emergence of Synchronization in Inhibitory Neural Networks.
in Scientific reports
Abu-Hassan K
(2019)
Optimal solid state neurons.
in Nature communications
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509589/1 | 01/10/2016 | 30/09/2021 | |||
| 1790293 | Studentship | EP/N509589/1 | 01/10/2016 | 31/03/2020 | Joseph Taylor |
| Description | During the course of this project, we have studied the nonlinear dynamics of small oscillating neuronal networks known as central pattern generators (or CPGs). These networks typically output periodic rhythmic patterns of activity and underlie biological functions such as respiration, locomotion and heart pacing. By building artificial networks of silicon neurons, we identified the possible sets of partial and fully synchronised patterns of activity that these CPGs can display. This understanding forms the basis of ongoing work that aims to replicate biological respiratory and cardiac CPG networks in-silico, with the goal of implementing these networks as a biomedical pacemaker device that adapts in real-time to physiological feedback. In addition to building general silicon networks, we have developed a suite of nonlinear optimization tools that allow us to automatically build complex models of specific individual neurons using electrophysiological recordings of the cell electrical activity. These tools allow us to build mathematical or solid-state models of any particular neuron, including the component neurons of the respiratory and cardiac CPGs. |
| Exploitation Route | The results and methodologies developed so far during the course of this project pave the way towards the building of synthetic neurons to repair biocircuits of the central nervous system when their regulation of vital functions is lost to disease. Adaptive bio-electronic devices may now be designed and fabricated for the treatment of chronic cardiorespiratory and functional neurological disease. Such a device would provide a novel therapy for cardiac arrhythmias, heart failure and hypertension. |
| Sectors | Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
| URL | https://cordis.europa.eu/project/id/732170/results |
| Description | Results obtained on this project have been used by Ceryx Medical, a spin-out company from the University of Bath which is developing chip-based bioelectronics which aim to mimic neuronal networks within the body called central pattern generators (CPGs) which control a number of rhythmic processes within the body such as respiration, heart rate and locomotion. These artificial CPGs aim to restore normal functional performance in nerve centres that have been affected by disease or injury. |
| First Year Of Impact | 2017 |
| Sector | Electronics,Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |