Communication between neurons and external world

Signalling between neurons allows the storage and processing of information. Further, this information can flow to other living tissues or, ex vivo, to devices which are connected to neurons. The understanding of these processes can be used in two main directions:

1. In vivo:

• To explain how our brains perceives the environment and controls all living activities. For example, how it receives sensory input or controls the movement of muscles [1]
• Improvement of communication between prosthetic devices, such as prosthetic legs, with patient’s nervous system to allow better control of the movement [2].

2. Ex vivo:

• In vitro modelling of the interactions between neurons and external stimuli. This can be used for neural disease modelling to understand the underlying mechanism or for drug testing [3]
• Design of a neurocomputer with a living cells component [4]
• Design of neurorehabilitation tools [5]

One of the basic steps in this direction is to analyse the interactions between:

• External stimuli triggering neuronal spikes, e.g. sound, visual
• Neuronal response, how neuronal spikes generate the ‘response’ to the environment, e.g. a movement of a muscle in response to some external stimuli.

In humans the communication between central nervous system and the external world is organised in a complex bidirectional system:

• Afferent system: receives the information from sensory organs and communicate them to the brain
• Efferent system: communicates the reaction of the brain to the motor system
  • Autonomic
    • e.g. heart
  •  Voluntary
    • e.g. skeletal muscles

A schematic information flow involving afferent, efferent and central neurons in humans:

In vitro, we can build a simplified model of communication between neurons and external stimuli, which allows better understanding of the input-output relationships on a level of a single neuronal spike and a single external electrical impulse. We can first map statistical relationships between external electrical impulse and neuronal spikes.

This can further allow us to design more complex system, as for example the following:

In this case, input device can generate electrical signal which will be processed by neuronal signalling system. The input signal can be designed to generate some specific response from neurons. The response from neurons, in a form of an electrical signal, can be communicated to the output device.

In the case of Bioserver, one central part of the processing includes the broadcasting of the live data directly on the web. We just announced at Intelligent Healthcare September 2020 the availability of our real-time signals, which was a world premiere: https://www.prnewswire.co.uk/news-releases/bioserver-net-computation-on-living-neurons-is-now-available-online-828541992.html and appeared in numerous press publications.

This is a very simple model for testing the interactions between external electrical stimuli and living neurons. Further development of this model will help us to apply living neurons as an efficient computing power in our Biological Neural Networks.

Literature

1. Aljadeff J, Lansdell BJ, Fairhall AL, Kleinfeld D. Analysis of Neuronal Spike Trains, Deconstructed. Neuron. 2016;91(2):221-259. doi:10.1016/j.neuron.2016.05.039 link:https://pubmed.ncbi.nlm.nih.gov/27477016/
2. Ngan CGY, Kapsa RMI, Choong PFM. Strategies for neural control of prosthetic limbs: from electrode interfacing to 3D printing. Materials (Basel). 2019;12(12):1927. Published 2019 Jun 14. doi:10.3390/ma12121927 link: https://pubmed.ncbi.nlm.nih.gov/31207952/
3. Obien ME, Deligkaris K, Bullmann T, Bakkum DJ, Frey U. Revealing neuronal function through microelectrode array recordings. Front Neurosci. 2015;8:423. Published 2015 Jan 6. doi:10.3389/fnins.2014.00423 link: https://pubmed.ncbi.nlm.nih.gov/25610364/
4. Heard M, Ford J, Yene N, Straiton B, Havanas P, Guo L, Advancing the neurocomputer, Neurocomputing, Volume 284, 5 April 2018, Pages 36-51,https://doi.org/10.1016/j.neucom.2018.01.021, link:https://www.sciencedirect.com/science/article/pii/S0925231218300389
5. Samuel GS, Oey NE, Choo M, et al. Combining levodopa and virtual reality-based therapy for rehabilitation of the upper limb after acute stroke: pilot study Part II. Singapore Med J. 2017;58(10):610-617. doi:10.11622/smedj.2016111 link: https://pubmed.ncbi.nlm.nih.gov/27311739/