Researchers have successfully reproduced the electrical properties of biological neurons onto semiconductor implantable chips


Artificial neurons on silicon chips that behave just like the real thing have been invented by scientists – a first-of-its-kind achievement with enormous scope for medical devices to cure chronic diseases, such as heart failure, Alzheimer’s, and other diseases of neuronal degeneration.



Designing artificial neurons that respond to electrical signals from the nervous system like real neurons has been a major goal in medicine for decades, as it opens up the possibility of curing conditions where neurons are not working properly, have had their processes severed as in spinal cord injury, or have died.








Professor Alain Nogaret, from the University of Bath Department of Physics led the project. He said: “Until now neurons have been like black boxes, but we have managed to open the black box and peer inside. Our work is paradigm changing because it provides a robust method to reproduce the electrical properties of real neurons in minute detail.

“But it’s wider than that, because our neurons only need 140 nanoWatts of power.


“For example we’re developing smart pacemakers that won’t just stimulate the heart to pump at a steady rate but use these neurons to respond in real time to demands placed on the heart – which is what happens naturally in a healthy heart.

Other possible applications could be in the treatment of conditions like Alzheimer’s and neuronal degenerative diseases more generally.

This shows the artificial neuron chip

This is one of the artificial neurons in its protective casing on a fingertip. The image is credited to University of Bath.



Professor Giacomo Indiveri, a co-author on the study, from the University of Zurich and ETF Zurich, added: “This work opens new horizons for neuromorphic chip design thanks to its unique approach to identifying crucial analog circuit parameters.”

Another co-author, Professor Julian Paton, a physiologist at the University of Auckland and the University of Bristol, said: “Replicating the response of respiratory neurons in bioelectronics that can be miniaturised and implanted is very exciting and opens up enormous opportunities for smarter medical devices that drive towards personalised medicine approaches to a range of diseases and disabilities”.”



— Ray Kurzweil, TED 2014


The primary driver of this quest relates to the cellular and sub-cellular genesis of human disease, at which scale, nanodevices can directly interact and potentially positively influence disease outcomes or prevent them altogether, particularly in regard to brain disorders (Kandel et al., 2000Kandel, 2001Zigmond et al., 2014Chaudhury et al., 2015Fornito et al., 2015Falk et al., 2016).

The pursuit of ever smaller tools to treat patients is approaching a pivotal juncture in medical history as advanced nanomedicine — specifically, medical nanorobotics — is expected to serve as a dynamic tool toward addressing most human brain disorders. The goal is to finally empower medical professionals to treat diseases at individual cellular and sub-cellular resolution (Freitas, 19981999b20032005a,c20072016Morris, 2001Astier et al., 2005Patel et al., 2006Park et al., 2007Popov et al., 2007Mallouk and Sen, 2009Martel et al., 2009Kostarelos, 2010Mavroides and Ferreira, 2011Boehm, 2013).



Medical neuralnanorobots are expected to have the capacity for real-time, non-destructive monitoring of single-neuron and single-synapse neuroelectric activity, local neuropeptide traffic, and other relevant functional data, while also allowing the acquisition of fundamental structural information from neuron surfaces, to enhance the connectome map of a living human brain (Sporns et al., 2005Lu et al., 2009Anderson et al., 2011Kleinfeld et al., 2011Seung, 2011Martins et al., 201220152016).


Neuralnanorobots are also expected to empower many non-medical paradigm-shifting applications, including significant human cognitive enhancement, by providing a platform for direct access to supercomputing storage and processing capabilities and interfacing with artificial intelligence systems.

Since information-based technologies are consistently improving their price-performance ratios and functional design at an exponential rate, it is likely that once they enter clinical practice or non-medical applications, neuralnanorobotic technologies may work in parallel with powerful artificial intelligence systems, supercomputing, and advanced molecular manufacturing.

Furthermore, autonomous nanomedical devices are expected to be biocompatible, primarily due to their structural materials, which would enable extended residency within the human body (Freitas, 1999a20022003).

Medical neuralnanorobots might also be fabricated in sufficient therapeutic quantities to treat individual patients, using diamondoid materials, as these materials may provide the greatest strength, resilience, and reliability in vivo (Freitas, 2010). An ongoing international “Nanofactory Collaboration” headed by Robert Freitas and Ralph Merkle has the primary objective of constructing the world’s first nanofactory, which will permit the mass manufacture of advanced autonomous diamondoid neuralnanorobots for both medical and non-medical applications (Freitas and Merkle, 20042006Freitas, 20092010).

It is conceivable that within the next 20–30 years, neuralnanorobotics may be developed to enable a safe, secure, instantaneous, real-time interface between the human brain and biological and non-biological computing systems, empowering brain-to-brain interfaces (BTBI), brain-computer interfaces (BCI), and, in particular, sophisticated brain/cloud interfaces (B/CI). Such human B/CI systems may dramatically alter human/machine communications, carrying the promise of significant human cognitive enhancement (Kurzweil, 2014Swan, 2016).

Historically, a fundamental breakthrough toward the possibility of a B/CI was the initial measurement and recording of the electrical activity of the brain via EEG in 1924 (Stone and Hughes, 2013).

At the time, EEG marked a historical advance in neurologic and psychiatric diagnostic tools, as this technology allowed for the measurement of a variety of cerebral diseases, the quantification of deviations induced by different mental states, and detection of oscillatory alpha waves (8–13 Hz), the so-called “Berger’s wave.”

The first EEG measurements required the insertion of silver wires into the scalps of patients, which later evolved to silver foils that were adhered to the head. These rudimentary sensors were initially linked to a Lippmann capillary electrometer. However, significantly improved results were achieved through the use of a Siemens double-coil recording galvanometer, which had an electronic resolution of 0.1 mv (Jung and Berger, 1979).

The first reported scientific instance of the term “brain–computer interface” dates to 1973, ∼50 years following the first EEG recording, when it was envisioned that EEG-reported brain electrical signals might be employed as data carriers in human–computer communications. This suggestion assumed that mental decisions and reactions might be probed by electroencephalographic potential fluctuations measured on the human scalp, and that meaningful EEG phenomena should be viewed as a complex structure of elementary wavelets that reflected individual cortical events (Vidal, 1973).

Currently, invasive1 and non-invasive brain–computer interfaces and non-invasive brain-to-brain communication systems have already been experimentally demonstrated and are the subject of serious research worldwide. Once these existing technologies have matured, they might provide treatments for completely paralyzed patients, eventually permitting the restoration of movement in paralyzed limbs through the transmission of brain signals to muscles or external prosthetic devices (Birbaumer, 2006).

The first reported direct transmission of information between two human brains without intervention of motor or peripheral sensory systems occurred in 2014, using a brain-to-brain communication technique referred to as “hyperinteraction” (Grau et al., 2014).

The most promising long-term future technology for non-destructive, real-time human–brain–computer interfaces and brain-to-brain communications may be neuralnanorobotics (Martins et al., 2016). Neuralnanorobotics, which is the application of medical nanorobots to the human brain, was first envisaged by Freitas, who proposed the use of nanorobots for direct real-time monitoring of neural traffic from in vivo neurons, as well as the translation of messages to neurons (Freitas, 1999b2003).

Other authors have also envisioned B/CI and predicted that in the future, humans will have access to a synthetic non-biological neocortex, which might permit a direct B/CI. Within the next few decades, neuralnanorobotics may enable a non-destructive, real-time, ultrahigh-resolution interface between the human brain and external computing platforms such as the “cloud.”

The term “cloud” refers to cloud computing, an information technology (IT) paradigm and a model for enabling ubiquitous access to shared pools of configurable resources (such as computer networks, servers, storage, applications, and services), that can be rapidly provisioned with minimal management effort, often over the Internet.

For both personal or business applications, the cloud facilitates rapid data access, provides redundancy, and optimizes the global usage of processing and storage resources while enabling access from virtually any location on the planet. However, the primary challenge for worldwide global cloud-based information processing technologies is the speed of access to the system, or latency. For example, the current round-trip latency rate for transatlantic loops between New York and London is ∼90 ms (Verizon, 2014).

Since there are now more than 4 billion Internet users worldwide, its economic impact on the global economy is increasingly significant. The economic impact of IoT (Internet of Things) applications alone has been estimated by the McKinsey Global Institute to range from $3.9 to $11.1 trillion per year by 2025. The global economic impact of cloud-based information processing over the next few decades may be at least an order of magnitude higher once cloud services are combined in previously unimagined ways, disrupting entire industries (Miraz et al., 2015). A neuralnanorobotics-mediated human B/CI, potentially available within 20–30 years, will require broadband Internet access with extremely high upload and download speeds, compared to today’s rates.

Humankind has at its core a potent and ceaseless drive to explore and to challenge itself, to improve its collective condition by relentlessly probing and pushing boundaries while constantly attempting to breach those barriers that tenuously separate the possible from the impossible. The notions of human augmentation and cognitive enhancement are borne of these tenets.

This drive includes an incessant quest for exploration and a constant desire for social interaction and communication — both of which are catalysts for rapidly increasing globalization. Consequently, the development of a non-destructive, real-time human B/CI technology may serve as an intimate, personalized conduit through which individuals would have instantaneous access to virtually any facet of cumulative human knowledge and also the optional specialized capacity to engage in myriad real-time fully immersive experiential and sensory worlds-

The Human Brain

The Quantitative Human Brain

The human brain comprises a remarkable information storage and processing system that possesses an extraordinary computation-per-volume efficiency, with an average weight of 1400 g and a volume of ∼1350 cm3, contained within an “average” intracranial volume of ∼1,700 cm3. A brief quantification of the brain’s constituents and operational parameters includes ∼1,350 cm3 (∼75%) brain cells, ∼200 cm3 (15%) blood, and up to ∼150 cm3 (10%) of cerebrospinal fluid (Rengachary and Ellenbogen, 2005).

The raw computational power of the human brain has been estimated to range from 1013 to 1016 operations/sec (Merkle, 1989).

The human brain’s functional action potential based information is estimated as 5.52 × 1016 bits/sec (Martins et al., 2012), with a brain power output estimated at 15–25 W and a power density of 1.1–1.8 × 104 W/m3 at an operating temperature of 37.3°C (Freitas, 1999b).

When considering the human brain at the regional level, an exceptional component is the neocortex (Tables 1​,2),2), which has a highly organized neural architecture that encompasses sensorimotor, cognitive, and emotional domains (Alexander et al., 1986Fuster and Bressler, 2012).

This cortical structure consists of mini-columnar and laminar arrangements of neurons that are linked via afferent and efferent connections distributed across multiple brain regions (Lorento de Nó, 1938Mountcastle, 1997Shepherd and Grillner, 2010Opris, 2013Opris et al., 2011201320142015). Cortical minicolumns consist of chains of pyramidal neurons that are surrounded by a “curtain of inhibition” formed by interneurons (Szentágothai and Arbib, 1975).

Table 1

Neocortical measures (Pakkenberg and Gundersen, 1997Stark et al., 2007a,b).

Surface (cm2)Thickness (mm)Volume (cm3)Neuron number density (106/cm3)Neurons (N, 109)

Table 2

Enumeration of neurons and synapses in the human neocortex (Tang et al., 2001Sandberg and Bostrom, 2008Karlsen and Pakkenberg, 2011).

Neocortex regionTotal neocortex volume (cm3)Number of synapses (1012)Number of neurons (109)Number of synapses per neuron (103)Glial cell number (109)

At the cellular level, the average human brain is estimated to contain (86.06 ± 8.2) × 109 neurons, with ∼80.2% (69.03 ± 6.65 × 109 neurons) located in the cerebellum, ∼19% (16.34 ± 2.17 × 109 neurons) located in the cerebral cortex, and only ∼0.8% (0.69 ± 0.12 × 109 neurons) located throughout the rest of the brain (Azevedo et al., 2009).

The human cerebellum and cerebral cortex together hold the vast majority (99.2%) of brain neurons (Azevedo et al., 2009).

Another approximation, based on combining estimates for the different brain regions, produced a similar value of 94.2 ± 11.3 × 109 neurons for the whole human brain (Martins et al., 2012).

Glial cells comprise another brain-cell type (Figure 1). The average number of glial cells in the human brain is estimated to be 84.61 ± 9.83 × 109 (Herculano-Houzel, 2009), with the population of glial cells in the neocortex estimated at from 18.2 to 38.6 × 109 (Karlsen and Pakkenberg, 2011).

The ratio of glia to neurons likely has functional relevance (Nedergaard et al., 2003) and varies between different brain regions. While the whole-brain glia/neuron ratio is ∼1:1, there are significant differences between brain domains. For example, the glia/neuron ratio of the cerebral cortex is 3.72:1 (60.84 billion glia; 16.34 billion neurons) but only 0.23:1 (16.04 billion glia; 69.03 billion neurons) in the cerebellum; the basal ganglia, diencephalon, and brainstem have a combined ratio of 11.35:1 (Azevedo et al., 2009).

In addition, synapses, numbering (2.42 ± 0.29) × 1014 in the average human brain, are collectively estimated to process information at spiking rates of (4.31 ± 0.86) × 1015 spikes/sec, empowering the human brain to process data at (5.52 ± 1.13) × 1016 bits/sec (Martins et al., 2012). Synapses are elements of the neural network that play a critical role in processing information in the brain, being involved in learning, long-term and short-term memory storage and deletion, and temporal information processing (Black et al., 1990Bliss and Collingridge, 1993Kandel, 2001Fuhrmann et al., 2002Lee et al., 2008Holtmaat and Svoboda, 2009Liu et al., 2012). Synapses are also key effectors for signal transduction and plasticity in the brain. Proper synapse formation during childhood provides a substrate for cognition, whereas improper formation or functionality leads to neuro-developmental disorders including mental retardation and autism (Rollenhagen and Lübke, 2006Mcallister, 2007Rollenhagen et al., 2007). Synapse loss, as occurs in Alzheimer’s patients, is intimately associated with cognitive decline (Dekosky and Scheff, 1990Terry et al., 1991Scheff and Price, 2006).

Processing Units

Structural cellular or sub-cellular elements of the human brain are considered as information processing units if they are involved in significant functional input/output changes in electrochemically based brain-data storage and/or processing systems.

There is some disagreement in the current scientific literature regarding the quantification of this “significance” metric. This incongruity has led various authors to consider different cellular and subcellular structures as fundamental elements of human brain storage and its computation system, encompassing (aside from neurons and synapses): dendritic trees, axons, proteins, and even neural microtubules (Koch et al., 1983Bialek, 1993Juusola et al., 1996Zador, 1998Manwani and Koch, 2001London and Häusser, 2005Ford, 2010).

Estimates for whole-brain electrical data processing rates range from 1.48 × 1011 bits/sec. to a high of 3.2 × 1029 bits/sec (Sandberg and Bostrom, 2008Martins et al., 2012). The human brain might even have more than 100 times higher computational capacity than previously thought, based on the discovery that dendrites may generate nearly 10 times as many electrochemical spikes as do neuron soma, and are hybrids that process both analog and digital signals (Moore et al., 2017). This finding may challenge the long-held belief that spikes in the soma (body of the neuron) are the primary means through which perception, learning, and memory formation occur. Dendrites comprise more than 90% of neural tissue, so knowing that they are much more active than the soma would fundamentally alter our understanding of how the brain processes information. As dendrites are ∼100 times larger by volume than neuronal bodies, the immense number of firing dendritic spikes would suggest that the brain may indeed possess significantly higher computational power than earlier estimated.

However, there is currently a consensus that neurons and synapses constitute the fundamental electrochemical processing units of the human brain (Gkoupidenis et al., 2017Jackman and Regehr, 2017).

The roles of neurons in electrical information processing include receiving, integrating, generating, and transmitting action-potential-based information (Koch, 1997Koch and Segev, 2000Zhang, 2008). However, several neuronal noise sources influence the reliability and precision of neuronal signaling, so stimulus-response functions are sometimes unreliable and are dissociated from what is being encoded via spike activity (Bialek and Rieke, 1992).

The other fundamental consensual processing units of electrochemical information are synapses. Synapses are a core component of the neuron network that process information and are involved in learning and memory, with synapse dimensions and morphologies reported as playing a fundamental role in long- and short-term memory storage and deletion. Synapses are also engaged in signal transduction and plasticity, ensuring one-way transmission of signals, and are involved in temporal information processing to allow complex system behaviors, along with acting to decelerate electrical signals (Puro et al., 1977Black et al., 1990Bliss and Collingridge, 1993Kandel, 2001Rollenhagen and Lübke, 2006Rollenhagen et al., 2007IBM, 2008Lee et al., 2008Holtmaat and Svoboda, 2009). The role of synapses as processing units of the human brain is reinforced by the results of computational simulation, which indicate that the computational power of a network is increased using dynamic synapses. This suggests that emulation of biological synapses is a prerequisite for the development of brain-like computational systems (Maass and Zador, 1999Fuhrmann et al., 2002Kuzum et al., 2012). A recently developed ultra-low-power artificial synapse for neural computing has demonstrated the capacity to provide 500 distinct states (Van de Burgt et al., 2017).

Real-time monitoring of the whole human brain (by placing neuralnanorobots within each neuron and nearby synaptic connections to record/transmit data from localized neuron and synapse spiking) may provide redundant data that might be employed in the development of validation protocols.

University of Bath
Media Contacts:
Chris Melvin – University of Bath
Image Source:
The image is credited to University of Bath.

Original Research: Open access
“Optimal solid state neurons”. Kamal Abu-Hassan, Joseph D. Taylor, Paul G. Morris, Elisa Donati, Zuner A. Bortolotto, Giacomo Indiveri, Julian F. R. Paton & Alain Nogaret.
Nature Communications doi:10.1038/s41467-019-13177-3.


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