Publications
129 results found
Hübsch T, Minic D, Nikolic K, et al., 2024, On the emergent “Quantum” theory in complex adaptive systems, Annals of Physics, Vol: 464, ISSN: 0003-4916
We explore the concept of emergent quantum-like theory in complex adaptive systems, and examine in particular the concrete example of such an emergent (or “mock”) quantum theory in the Lotka–Volterra system. In general, we investigate the possibility of implementing the mathematical formalism of quantum mechanics on classical systems, and what would be the conditions for using such an approach. We start from a standard description of a classical system via Hamilton–Jacobi (HJ) equation and reduce it to an effective Schrodinger-type equation, with a (mock) Planck constant [Formula presented], which is system-dependent. The condition for this is that the so-called quantum potential VQ, which is state-dependent, is canceled out by some additional term in the HJ equation. We consider this additional term to provide for the coupling of the classical system under consideration to the ‘environment’. We assume that a classical system could cancel out the VQ term (at least approximately) by fine tuning to the environment. This might provide a mechanism for establishing a stable, stationary states in (complex) adaptive systems, such as biological systems. In particular, we present a general argument as to why the non-equilibrium dynamics of a classical system could lead to a mock quantum description that ensures stability compatible with adaptability. In this context we emphasize the state dependent nature of the mock quantum dynamics and we also introduce the new concept of the mock quantum, state dependent, statistical field theory. We also discuss some universal features of the quantum-to-classical as well as the mock-quantum-to-classical transition found in the turbulent phase of the hydrodynamic formulation of our proposal. In this way we re-frame the concept of decoherence into the concept of ‘quantum turbulence’, i.e. that the transition between quantum and classical could be defined in analogy to the transition from lami
Russo N, Huang H, Donati E, et al., 2023, An Interface Platform for Robotic Neuromorphic Systems, Chips, Vol: 2, Pages: 20-30
<jats:p>Neuromorphic computing is promising to become a future standard in low-power AI applications. The integration between new neuromorphic hardware and traditional microcontrollers is an open challenge. In this paper, we present an interface board and a communication protocol that allows communication between different devices, using a microcontroller unit (Arduino Due) in the middle. Our compact printed circuit board (PCB) links different devices as a whole system and provides a power supply for the entire system using batteries as the power supply. Concretely, we have connected a Dynamic Vision Sensor (DVS128), SpiNNaker board and a servo motor, creating a platform for a neuromorphic robotic system controlled by a Spiking Neural Network, which is demonstrated on the task of intercepting incoming objects. The data rate of the implemented interface board is 24.64 k symbols/s and the latency for generating commands is about 11ms. The complete system is run only by batteries, making it very suitable for robotic applications.</jats:p>
Cavallo FR, Toumazou C, Nikolic K, 2022, Unsupervised Classification of Human Activity with Hidden Semi-Markov Models, APPLIED SYSTEM INNOVATION, Vol: 5
Cavallo FR, Mirza KB, de Mateo S, et al., 2022, A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR, BIOSENSORS-BASEL, Vol: 12
Russo N, Huang H, Nikolic K, 2022, Live Demonstration: Neuromorphic Robot Goalie Controlled by Spiking Neural Network
This demonstration shows an implementation of the Robot Goalie using neuromorphic hardware and a Spiking Neural Network (SNN) to control the goalkeeper position. The system consists of four main components: a Dynamic Vision Sensor (DVS128) used as an "eye", a SpiNNaker SpiNN-3 board to run a SNN to predict the final position of the ball and intercept it, an actuator (Futaba S9257) and an Arduino Due microcontroller (MCU). A PCB board was developed to integrate the Arduino and SpiNNaker boards, with power regulators to use a battery pack for the complete setup. The microcontroller acts as the central communication hub which links incoming signals from the DVS, pass them to SpiNNaker, then to receive the signals from SpiNNaker board and generate the instruction to the digital motor where to place the goalkeeper. A simple SNN has been developed to process the visual input and decide where to put the goalie. This solution is different from the classical segmentation and object tracking and is closer to the biological functioning of biological vision. The system represents a real-time and lowpower solution for the task of intercepting incoming objects. This is in particular relevant to autonomous robotic systems which require fast reaction, but at low power consumption.
Cavallo FR, Mirza KB, de Mateo S, et al., 2021, Aptasensor for quantification of leptin through PCR amplification of short DNA-aptamers., ACS Sensors, Vol: 6, Pages: 709-715, ISSN: 2379-3694
Protein quantification is traditionally performed through enzyme-linked immunosorbent assay (ELISA), which involves long preparation times. To overcome this, new approaches use aptamers as an alternative to antibodies. In this paper, we present a new approach to quantify proteins with short DNA aptamers through polymerase chain reaction (PCR) resulting in shorter protocol times with comparatively improved limits of detection. The proposed method includes a novel way to quantify both the target protein and the corresponding short DNA-aptamers simultaneously, which also allows us to fully characterize the performance of aptasensors. Human leptin is used as a target protein to validate this technique, because it is considered an important biomarker for obesity-related studies. In our experiments, we achieved the lowest limit of detection of 100 pg/mL within less than 2 h, a limit affected by the dissociation constant of the leptin aptamer, which could be improved by selecting a more specific aptamer. Because of the simple and inexpensive approach, this technique can be employed for Lab-On-Chip implementations and for rapid "on-site" quantification of proteins.
Han Z, Francesca C, Nikolic K, et al., 2021, Signal identification of DNA amplification curves in custom-PCR platforms, ISSN: 0271-4310
Custom-made, point-of-care PCR platforms are a necessary tool for rapid, point-of-care diagnostics in situations such as the current Covid-19 pandemic. However, a common issue faced by them is noisy fluorescence signals, which consist of a drifting baseline or noisy sigmoidal curve. This makes automated detection difficult and requires human verification. In this paper, we have tried to use nonlinear fitting for automated classification of PCR waveforms to identify whether amplification has taken place or not. We have presented several novel signal reconstruction techniques based on nonlinear fitting which will enable better pre-processing and automated differentiation of a valid or invalid PCR amplification curve. We have also tried to perform this classification at lower PCR cycles to reduce decision times in diagnostic tests.
Han Z, Francesca C, Nikolic K, et al., 2021, Signal Identification of DNA Amplification Curves in Custom-PCR Platforms, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
- Author Web Link
- Cite
- Citations: 6
Cavallo FR, Mirza KB, de Mateo S, et al., 2021, A Point-of-Care Device for Sensitive Protein Quantification, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
- Author Web Link
- Cite
- Citations: 2
Tringali D, Haci D, Mazza F, et al., 2021, Eye Accommodation Sensing for Adaptive Focus Adjustment, 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), Pages: 7460-7464, ISSN: 1557-170X
Mu Z, Nikolic K, Schultz SR, 2021, Quadratic Mutual Information estimation of mouse dLGN receptive fields reveals asymmetry between ON and OFF visual pathways., 10th International IEEE-EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 1134-1139, ISSN: 1948-3546
Mu Z, Nikolic K, Schultz SR, 2020, Quadratic Mutual Information estimation of mouse dLGN receptive fields reveals asymmetry between ON and OFF visual pathways
<jats:title>Abstract</jats:title><jats:p>The longstanding theory of “parallel processing” predicts that, except for a sign reversal, ON and OFF cells are driven by a similar pre-synaptic circuit and have similar visual field coverage, direction/orientation selectivity, visual acuity and other functional properties. However, recent experimental data challenges this view. Here we present an information theory based receptive field (RF) estimation method - quadratic mutual information (QMI) - applied to multi-electrode array electrophysiological recordings from the mouse dorsal lateral geniculate nucleus (dLGN). This estimation method provides more accurate RF estimates than the commonly used Spike-Triggered Average (STA) method, particularly in the presence of spatially correlated inputs. This improved efficiency allowed a larger number of RFs (285 vs 189 cells) to be extracted from a previously published dataset. Fitting a spatial-temporal Difference-of-Gaussians (ST-DoG) model to the RFs revealed that while the structural RF properties of ON and OFF cells are largely symmetric, there were some asymmetries apparent in the functional properties of ON and OFF visual processing streams - with OFF cells preferring higher spatial and temporal frequencies on average, and showing a greater degree of orientation selectivity.</jats:p>
Wildner K, Mirza KB, De La Franier B, et al., 2020, Iridium oxide based potassium sensitive microprobe with anti-fouling properties, IEEE Sensors Journal, Vol: 20, Pages: 12610-12619, ISSN: 1530-437X
Here, we present a new type of potassium sensor which possesses a combination of potassium sensing and anti-biofouling properties. Two major advancements were required to be developed with respect to the current technology; Firstly, design of surface linkers for this type of coating that would allow deposition of the potassiumselective coating on Iridium (Ir) wire or micro-spike surface for chronic monitoring for the first time. As this has never been done before, even for flat Ir surfaces, the material’s small dimensions and surface area render this challenging. Secondly, the task of transformation of the coated wire into a sensor. Here we develop and bench-test the electrode sensitivity to potassium and determine its specificity to potassium versus sodium interference. For this purpose we also present a novel characterisation platform which enables dynamic characterization of the sensor including step and sinusoidal response to analyte changes. The developed sensor shows good sensitivity (<1 mM concentrations of K+ ions) and selectivity (up to approximately 10 times more sensitive to K+ than Na+ concentration changes, depending on concentrations and ionic environment). In addition, the sensor displays very good mechanical properties for the small diameter involved (sub 150 μm), which in combination with anti-biofouling properties, renders it an excellent potential tool for the chemical monitoring of neural and other physiological activities using implantable devices.
Roever P, Mirza KB, Nikolic K, et al., 2020, Convolutional neural network for classification of nerve activity based on action potential induced neurochemical signatures, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1-5, ISSN: 0271-4302
Neural activity results in chemical changes in theextracellular environment such as variation in pH or potassium/sodium ion concentration. Higher signal to noise ratio makeneurochemical signals an interesting biomarker for closed-loopneuromodulation systems. For such applications, it is importantto reliably classify pH signatures to control stimulationtiming and possibly dosage. For example, the activity of thesubdiaphragmatic vagus nerve (sVN) branch can be monitoredby measuring extracellular neural pH. More importantly, guthormone cholecystokinin (CCK)-specific activity on the sVN canbe used for controllably activating sVN, in order to mimic thegut-brain neural response to food intake. In this paper, we presenta convolutional neural network (CNN) based classification systemto identify CCK-specific neurochemical changes on the sVN,from non-linear background activity. Here we present a novelfeature engineering approach which enables, after training, ahigh accuracy classification of neurochemical signals using CNN.
Cheng R, Mirza KB, Nikolic K, 2020, Neuromorphic robotic platform with visual input, processor and actuator, based on spiking neural networks, Applied System Innovation, Vol: 3, Pages: 1-16, ISSN: 2571-5577
This paper describes the design and modus of operation of a neuromorphic robotic platform based on SpiNNaker, and its implementation on the goalkeeper task. The robotic system utilises an address event representation (AER) type of camera (dynamic vision sensor (DVS)) to capture features of a moving ball, and a servo motor to position the goalkeeper to intercept the incoming ball. At the backbone of the system is a microcontroller (Arduino Due) which facilitates communication and control between different robot parts. A spiking neuronal network (SNN), which is running on SpiNNaker, predicts the location of arrival of the moving ball and decides where to place the goalkeeper. In our setup, the maximum data transmission speed of the closed-loop system is approximately 3000 packets per second for both uplink and downlink, and the robot can intercept balls whose speed is up to 1 m/s starting from the distance of about 0.8 m. The interception accuracy is up to 85%, the response latency is 6.5 ms and the maximum power consumption is 7.15 W. This is better than previous implementations based on PC. Here, a simplified version of an SNN has been developed for the ‘interception of a moving object’ task, for the purpose of demonstrating the platform, however a generalised SNN for this problem is a nontrivial problem. A demo video of the robot goalie is available on YouTube.
Najafizadeh L, Zhang M, Nikolic K, 2019, Guest Editorial: Special Issue on Selected Papers From IEEE BioCAS 2018, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 13, Pages: 793-794, ISSN: 1932-4545
Mirza KB, Golden C, Nikolic K, et al., 2019, Closed-loop implantable therapeutic neuromodulation systems based on neurochemical monitoring, Frontiers in Neuroscience, Vol: 13, ISSN: 1662-4548
Closed-loop or intelligent neuromodulation allows adjustable, personalised neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algori5 thm which decides the timing (when ?) and strength (how much ?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodu lation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. How ever, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K+), sodium (Na+ 12 ), calcium(Ca2+), chloride (Cl−), hydrogen (H+ 13 ) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemi cal, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.
Mirza KB, Kulasekeram N, Liu Y, et al., 2019, System on chip for closed loop neuromodulation based on dual mode biosignals, 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: Institute of Electrical and Electronics Engineers (IEEE), ISSN: 2158-1525
Closed loop neuromodulation, where the stimulation is controlled autonomously based on physiological events, has been more effective than open loop techniques. In the few existing closed loop implementations which have a feedback, indirect non-neurophysiological biomarkers have been typically used (e.g. heart rate, stomach distension). Although these biomarkers enable automatic initiation of neural stimulation, they do not enable intelligent control of stimulation dosage. In this paper, we present a novel closed loop neuromodulation System-on-Chip (SoC) based on a dual signal mode that is detecting both electrical and chemical signatures of neural activity. We use vagus nerve stimulation (VNS) as a design case here. Vagal chemical (pH) signal is detected and used for initiating VNS and vagal compound nerve action potential (CNAP) signals are used to determine the stimulation dosage and pattern. Although we used the paradigm of appetite control and neurometabolic therapies for developing the algorithms for neurostimulation control, the SoC described here can be utilised for other types of closed loop neuromodulation implants.
Troiani F, Nikolic K, Constandinou TG, 2019, Correction: Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model, PLoS ONE, Vol: 14, ISSN: 1932-6203
[This corrects the article DOI: 10.1371/journal.pone.0200392.].
Luo JW, Nikolic K, Degenaar P, 2019, Modelling Optogenetic Subthreshold Effects, 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Pages: 6136-6140, ISSN: 1557-170X
- Author Web Link
- Cite
- Citations: 1
Haci D, Liu Y, Nikolic K, et al., 2018, Thermally controlled lab-on-PCB for biomedical applications, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 655-658
This paper reports on the implementation andcharacterisation of a thermally controlled device forin vitrobiomedical applications, based on standard Printed Circuit Board(PCB) technology. This is proposed as a low cost alternativeto state-of-the-art microfluidic devices and Lab-on-Chip (LoC)platforms, which we refer to as the thermal Lab-on-PCB concept.In total, six different prototype boards have been manufacturedto implement as many mini-hotplate arrays. 3D multiphysicssoftware simulations show the thermal response of the modelledmini-hotplate boards to electrical current stimulation, highlight-ing their versatile heating capability. A comparison with theresults obtained by the characterisation of the fabricated PCBsdemonstrates the dual temperature sensing/heating property ofthe mini-hotplate, exploitable in a larger range of temperaturewith respect to the typical operating range of LoC devices. Thethermal system is controllable by means of external off-the-shelfcircuitry designed and implemented on a single-channel controlboard prototype.
Perra E, Rapeaux A, Nikolic K, 2018, The crucial role of nerve depolarisation in high frequency conduction block in mammalian nerves: simulation study, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Pages: 2214-2217, ISSN: 1557-170X
Neurostimulations which use High Frequency Alternating Current (HFAC) block show great promise for neuromodulatory therapies. Treatments have been developed for various health conditions including obesity and obesity related health risks, and now even stomach cancer treatments are being considered. However the mechanism of the block is still not completely clear, as well as how various neural and electrode parameters affect it. In order to study conduction block during HF stimulation in mammalian axons, we describe a detailed computational model and perform comprehensive simulations. We establish relationships between the blocking frequency and amplitude versus fibre diameter and the distance between the electrode and fibre. We found that only a certain level of depolarisation will universally create a block irrespective of the fibre size, and it is in the range 24-30mV depending on the stimulus frequency. Our study crucially improves our knowledge about this important technique which is rapidly emerging as a commercially available therapy.
Troiani F, Nikolic K, Constandinou TG, 2018, Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model
We present a finite difference time domain (FDTD) model for computation of Aline scans in time domain optical coherence tomography (OCT). By simulatingonly the end of the two arms of the interferometer and computing theinterference signal in post processing, it is possible to reduce thecomputational time required by the simulations and, thus, to simulate muchbigger environments. Moreover, it is possible to simulate successive A linesand thus obtaining a cross section of the sample considered. In this paper wepresent the model applied to two different samples: a glass rod filled withwater-sucrose solution at different concentrations and a peripheral nerve. Thiswork demonstrates the feasibility of using OCT for non-invasive, direct opticalmonitoring of peripheral nerve activity, which is a long-sought goal ofneuroscience.
Troiani F, Nikolic K, Constandinou TG, 2018, Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model, PLoS ONE, Vol: 13, Pages: 1-14, ISSN: 1932-6203
We present a finite difference time domain (FDTD) model for computation of A line scans in time domain optical coherence tomography (OCT). The OCT output signal is created using two different simulations for the reference and sample arms, with a successive computation of the interference signal with external software. In this paper we present the model applied to two different samples: a glass rod filled with water-sucrose solution at different concentrations and a peripheral nerve. This work aims to understand to what extent time domain OCT can be used for non-invasive, direct optical monitoring of peripheral nerve activity.
Markar S, Wiggins T, Antonowicz S, et al., 2018, Assessment of a noninvasive exhaled breath test for the diagnosis of oesophagogastric cancer, JAMA Oncology, Vol: 4, Pages: 970-976, ISSN: 2374-2445
Importance Early esophagogastric cancer (OGC) stage presents with nonspecific symptoms.Objective The aim of this study was to determine the accuracy of a breath test for the diagnosis of OGC in a multicenter validation study.Design, Setting, and Participants Patient recruitment for this diagnostic validation study was conducted at 3 London hospital sites, with breath samples returned to a central laboratory for selected ion flow tube mass spectrometry (SIFT-MS) analysis. Based on a 1:1 cancer:control ratio, and maintaining a sensitivity and specificity of 80%, the sample size required was 325 patients. All patients with cancer were on a curative treatment pathway, and patients were recruited consecutively. Among the 335 patients included; 172 were in the control group and 163 had OGC.Interventions Breath samples were collected using secure 500-mL steel breath bags and analyzed by SIFT-MS. Quality assurance measures included sampling room air, training all researchers in breath sampling, regular instrument calibration, and unambiguous volatile organic compounds (VOCs) identification by gas chromatography mass spectrometry.Main Outcomes and Measures The risk of cancer was identified based on a previously generated 5-VOCs model and compared with histopathology-proven diagnosis.Results Patients in the OGC group were older (median [IQR] age 68 [60-75] vs 55 [41-69] years) and had a greater proportion of men (134 [82.2%]) vs women (81 [47.4%]) compared with the control group. Of the 163 patients with OGC, 123 (69%) had tumor stage T3/4, and 106 (65%) had nodal metastasis on clinical staging. The predictive probabilities generated by this 5-VOCs diagnostic model were used to generate a receiver operator characteristic curve, with good diagnostic accuracy, area under the curve of 0.85. This translated to a sensitivity of 80% and specificity of 81% for the diagnosis of OGC.Conclusions and Relevance This study shows the potential of breath analysis in noninvasive diagn
Ahmed T, Mirza KB, Nikolic K, 2018, Resource efficient pre-processor for drift removal in neurochemical signals, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
A necessary requirement for chemometric platforms is pre-processing of the acquired chemical signals to remove baseline drift in the signal. The drift could originate from sensor characteristics or from background chemical activity in the surrounding environment. A recent emerging field is neurochemical monitoring to detect and quantify neural activity. In this paper, a resource efficient pre-processing system is presented to remove drift from the acquired neurochemical signal. The drift removal technique is based on baseline manipulation without requiring window based processing. The target application, for demonstration purposes, is the recording of vagal pH signals to enable closed-loop Vagus Nerve Stimulation (VNS). The final design is multiplier-free and results in an Application Specific Integrated Circuit (ASIC) that is 640 μm by 625 μm in area.
Wildner K, Kulasekeram N, Mirza KB, et al., 2018, Live Demo: Reconfigurable Low-noise Multichannel Amplifier for Neurochemical Recording, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Kulasekeram N, Wildner K, Mirza KB, et al., 2018, Reconfigurable Low-noise Multichannel Amplifier for Neurochemical Recording, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Mirza K, Alenda A, Eftekhar A, et al., 2018, Influence of cholecystokinin-8 on compound nerve action potentials from ventral gastric vagus in rats, International Journal of Neural Systems, Vol: 28, ISSN: 0129-0657
Objective:Vagus Nerve Stimulation (VNS) has shown great promise as a potential therapy for anumber of conditions, such as epilepsy, depression and forNeurometabolic Therapies, especially fortreating obesity. The objective of this study was to characterise the left ventral subdiaphragmaticgastric trunk of vagus nerve (SubDiaGVN) and to analyse the influence of intravenous injection of guthormone cholecystokinin octapeptide (CCK-8) on compound nerve action potential (CNAP) observedon the same branch, with the aim of understanding the impact of hormones on VNS and incorporatingthe methods and results into closed loop implant design.Methods:The cervical region of the left vagus nerve (CerVN) of male Wistar rats was stimulatedwith electric current and the elicited CNAPs were recorded on the SubDiaGVN under four differentconditions:Control(no injection),Saline,CCK1(100 pmol/kg) andCCK2(1000 pmol/kg) injections.Results:We identified the presence of Aδ, B, C1, C2, C3 and C4 fibres with their respective velocityranges. Intravenous administration of CCKin vivoresults in selective, statistically significant reductionof CNAP components originating from A and B fibres, but with no discernible effect on the C fibresinn=7animals. The affected CNAP components exhibit statistically significant (pSaline−CCK1= 0.02andpSaline−CCK2= 0.007) higher normalized stimulation thresholds.Conclusion:This approach of characterising the vagus nerve can be used in closed loop systems todeterminewhento initiate VNS and also to tune the stimulation dose, which is patient specific andchanges over time.
Jarvis S, Nikolic K, Schultz SR, 2018, Neuronal gain modulability is determined by dendritic morphology: a computational optogenetic study, PLoS Computational Biology, Vol: 14, ISSN: 1553-734X
The mechanisms by which the gain of the neuronal input-output function may be modulated have been the subject of much investigation. However, little is known of the role of dendrites in neuronal gain control. New optogenetic experimental paradigms based on spatial profiles or patterns of light stimulation offer the prospect of elucidating many aspects of single cell function, including the role of dendrites in gain control. We thus developed a model to investigate how competing excitatory and inhibitory input within the dendritic arbor alters neuronal gain, incorporating kinetic models of opsins into our modeling to ensure it is experimentally testable. To investigate how different topologies of the neuronal dendritic tree affect the neuron’s input-output characteristics we generate branching geometries which replicate morphological features of most common neurons, but keep the number of branches and overall area of dendrites approximately constant. We found a relationship between a neuron’s gain modulability and its dendritic morphology, with neurons with bipolar dendrites with a moderate degree of branching being most receptive to control of the gain of their input-output relationship. The theory was then tested and confirmed on two examples of realistic neurons: 1) layer V pyramidal cells—confirming their role in neural circuits as a regulator of the gain in the circuit in addition to acting as the primary excitatory neurons, and 2) stellate cells. In addition to providing testable predictions and a novel application of dual-opsins, our model suggests that innervation of all dendritic subdomains is required for full gain modulation, revealing the importance of dendritic targeting in the generation of neuronal gain control and the functions that it subserves. Finally, our study also demonstrates that neurophysiological investigations which use direct current injection into the soma and bypass the dendrites may miss some important neuronal functio
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.