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arxiv: 2512.01133 · v2 · submitted 2025-11-30 · 📡 eess.SY · cs.SY

A Neuromodulable Current-Mode Silicon Neuron for Robust and Adaptive Neuromorphic Systems

Loris Mendolia , Chenxi Wen , Elisabetta Chicca , Giacomo Indiveri , Rodolphe Sepulchre , Jean-Michel Redout\'e , Alessio Franci This is my paper

Pith reviewed 2026-05-17 02:15 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords neuromorphic systemssilicon neuronscurrent-mode circuitsneuromodulationanalog feedbackCMOS designadaptive neurons
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The pith

This current-mode silicon neuron adapts its spiking through neuromodulation while staying robust to current and temperature changes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors present a novel current-mode neuron that incorporates neuromodulation to adapt its input responses and spiking patterns. They develop a mathematical model to analyze and tune this behavior and validate it with experiments on a 180 nm CMOS chip. The design leverages an analog feedback structure to achieve robustness, flexibility, and scalability over wide operating ranges. A reader would care because this brings biological-like adaptability to neuromorphic hardware, which is essential for practical applications in varying real-world conditions such as robotics and edge computing.

Core claim

The paper claims that due to the analog underlying feedback structure, the proposed adaptive neuromodulable neuron exhibits a high degree of robustness, flexibility, and scalability across operating ranges of currents and temperatures. This is demonstrated through a mathematical model and experimental verification on a low-power 180 nm CMOS implementation, showing biologically plausible neuromodulation adaptation capabilities with minimal model complexity.

What carries the argument

The neuromodulable current-mode silicon neuron circuit, which uses analog feedback to enable adaptation of spiking patterns in response to context.

Load-bearing premise

The circuit's physical behavior matches the mathematical model closely enough that the observed adaptation can be considered biologically plausible neuromodulation.

What would settle it

Fabricated chip measurements that fail to show the predicted adaptation in spiking patterns when parameters like current levels or temperature are varied within the claimed ranges.

Figures

Figures reproduced from arXiv: 2512.01133 by Alessio Franci, Chenxi Wen, Elisabetta Chicca, Giacomo Indiveri, Jean-Michel Redout\'e, Loris Mendolia, Rodolphe Sepulchre.

Figure 1
Figure 1. Figure 1: Neuromodulation in thalamocortical relay neurons (top, adapted from [7]) vs. neuromodulation recorded in our silicon neuron (bottom). For the same input current step (middle trace), neuromodulation enables a switch between tonic spiking (left) and a single transient burst (right), corresponding respectively to a linear rate-based encoding or a nonlinear "wake-up call" response to changes. Subthreshold inte… view at source ↗
Figure 2
Figure 2. Figure 2: Mixed-feedback neuromodulable neuron structure. Red (resp. blue) highlighted arrows indicate positive (resp. negative) feedback paths. The symbols and circuit implementations of the two types of blocks are detailed in Figs. 4 and 5. The low-pass filter blocks are denoted using their transfer function, and the sigmoid blocks using the shape of their steady-state response. The dashed arrows represent the pos… view at source ↗
Figure 3
Figure 3. Figure 3: If is the current-mode analog of the membrane potential. Is and Iu respectively provide slow repolarization and ultraslow spike-frequency adaptation currents. The nonlinear sigmoid blocks provide positive feedback to create the fast spike upstroke and slow regenerative dynamics. 0.01 s 100 pA A 0.01 s 100 pA B [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Differential-pair integrator (DPI) circuit (from [10, 38, 44, 45]). Q2-3 are the differential pair receiving the input current Iin. With Q1, Q2 sets the gain G = Ith Iτ of the circuit by diverting part of the input current depending on Ith, while Q3 sets the voltage VC of the capacitor. Q4 provides the leakage current Iτ that discharges the capacitor over time. Q5 provides an output current Iout depending … view at source ↗
Figure 5
Figure 5. Figure 5: Current-mode sigmoid circuit with inactivation mechanism. Q1-2 form a simple current comparator circuit: when Iin > Ithr, the voltage Vcmp starts to increase. Q3-5 control the rate of increase of Vcmp: the higher Vlin is, the more current is drawn by this branch, increasing the response range of the comparator. Q6-7 set the gain of the circuit, defining the maximum output current Igain at which the sigmoid… view at source ↗
Figure 6
Figure 6. Figure 6: Current-mode sigmoid circuit response for different bias parameter values. The bias currents Ithr, Ilin, and IG accurately set the input threshold, the width of the monotonically increasing range, and the maximum output current respectively, for any combination of subthreshold bias parameters. provide full control over the sigmoid input-output characteristic. Vthr sets the input threshold current Ithr at w… view at source ↗
Figure 7
Figure 7. Figure 7: Examples of steady-state curves (left) of our mixed-feedback model and the corresponding neuron responses (right). The red (resp. cyan) regions are the bistability region of the fast (resp. slow) subsystems. When the slow bistability region is entirely contained within the fast one (top), the neuron can only exhibit tonic spiking. When the slow bistability region threshold comes before the fast one (bottom… view at source ↗
Figure 8
Figure 8. Figure 8: Micrograph of the prototype chip (2023) containing the test mixed-feedback neurons. Blue: array of the 16 mixed-feedback neurons. Green: output buffer stage. Red: shift registers. Inset: zoom on 4 mixed-feedback neurons stacked on top of one another. Yellow: a single neuron [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Custom PCB used for characterization of the on-chip mixed-feedback neurons. The board hosts the fabricated CMOS die (center), an external 12-bit DAC (AD5674) for bias generation, and an operational amplifier configured as a feedback ammeter for on-board current-to-voltage conversion. A microcontroller (Arduino Nano Every) controls the on-chip multiplexer for output selection and the programming of the DAC … view at source ↗
Figure 10
Figure 10. Figure 10: Measured neuron responses to increasing input current (bottom), in spiking (top) and bursting (middle) configurations. The neuron exhibits tonic excitability, showing firing frequencies increasing with input current in both modes, starting as low as a few Hz. 3.2 Measured dynamics in silicon To validate and characterize our neuron’s behavior in silicon, we performed various measurements on the fabricated … view at source ↗
Figure 11
Figure 11. Figure 11: Measured neuromodulation from spiking to bursting (top) through an increase in slow positive feedback IGs,0 (bottom). The neuron undergoes a smooth transition from spiking to bursting, with a gradual increase in the number of spikes per burst and a consistent inter-burst interval. neuromodulatory pathways that can be explored and achieved in the proposed current-mode neuromorphic design. As already remark… view at source ↗
Figure 12
Figure 12. Figure 12: Measured variation in spiking (top) and bursting frequency (inverse of the period between the start of two bursts, bottom) of two different neurons as a function of ambient temperature, with representative output traces at two extreme temperatures. Frequency increases with temperature and follows a comparable trajectory across neurons and firing regimes. The recorded waveforms compress in time and speed u… view at source ↗
Figure 13
Figure 13. Figure 13: Full current-mode mixed-feedback neuron schematic, implementing the structure detailed in Section 2.1 and in [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
read the original abstract

Neuromorphic engineering makes use of mixed-signal analog and digital circuits to directly emulate the computational principles of biological brains. Such electronic systems offer a high degree of adaptability, robustness, and energy efficiency across a wide range of tasks, from edge computing to robotics. Within this context, we investigate a key feature of biological neurons: their ability to carry out robust and reliable computation by adapting their input responses and spiking patterns to context through neuromodulation. Achieving analogous levels of robustness and adaptation in neuromorphic circuits through modulatory mechanisms is a largely unexplored path. We present a novel current-mode neuron design that supports robust neuromodulation with minimal model complexity, compatible with standard CMOS technologies. We first introduce a mathematical model of the circuit and provide tools to analyze and tune the neuron behavior; we then demonstrate both theoretically and experimentally the biologically plausible neuromodulation adaptation capabilities of the circuit over a wide range of parameters. All theoretical predictions were verified in experiments on a low-power 180 nm CMOS implementation of the proposed neuron circuit. Due to the analog underlying feedback structure, the proposed adaptive neuromodulable neuron exhibits a high degree of robustness, flexibility, and scalability across operating ranges of currents and temperatures, making it a perfect candidate for real-world neuromorphic applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces a current-mode silicon neuron circuit supporting neuromodulation via a single tunable parameter, accompanied by a mathematical model for analysis and tuning. Theoretical predictions of adaptive spiking behavior and robustness are validated against measurements from a fabricated 180 nm CMOS chip. The authors attribute high robustness, flexibility, and scalability to the analog feedback structure and position the design as a candidate for real-world neuromorphic systems with biologically plausible adaptation.

Significance. If the central claims hold, the work supplies direct hardware evidence from a low-power CMOS implementation that a neuromodulable neuron can maintain consistent behavior across current and temperature ranges. The combination of a compact mathematical model with chip-level verification is a concrete strength that supports practical deployment in adaptive neuromorphic hardware.

major comments (2)
  1. [Neuromodulation results] Section describing neuromodulation results: the claim that observed shifts in spiking patterns constitute 'biologically plausible neuromodulation' is load-bearing for the candidacy argument, yet the manuscript provides no quantitative comparison (e.g., matching of firing-threshold shifts, gain changes, or adaptation time constants) to specific biological data or mechanisms such as neuromodulator effects on ion channels.
  2. [Mathematical model] Mathematical model section: the neuromodulation tuning parameters are treated as free parameters whose selection enables the reported robustness; the text should clarify whether the robustness holds for arbitrary choices within the stated operating ranges or only for specific tuned values, as this directly affects the 'minimal model complexity' and scalability claims.
minor comments (2)
  1. [Circuit description] Circuit schematic figure: component labels and the neuromodulation current path could be annotated more explicitly to aid readers in reproducing the topology.
  2. [Experimental results] Experimental plots: temperature and current-range axes should include explicit units and indicate the nominal operating point used for the robustness measurements.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below and indicate the planned revisions.

read point-by-point responses

  1. Referee: [Neuromodulation results] Section describing neuromodulation results: the claim that observed shifts in spiking patterns constitute 'biologically plausible neuromodulation' is load-bearing for the candidacy argument, yet the manuscript provides no quantitative comparison (e.g., matching of firing-threshold shifts, gain changes, or adaptation time constants) to specific biological data or mechanisms such as neuromodulator effects on ion channels.

    Authors: We acknowledge that the manuscript currently lacks explicit quantitative comparisons to biological neuromodulation data. The observed shifts in spiking patterns were designed to emulate key aspects of biological adaptation, such as changes in excitability and firing rate. To address this, we will revise the neuromodulation results section to include a brief discussion and table referencing literature values for neuromodulator-induced shifts in firing threshold and gain (e.g., from studies on serotonin and dopamine effects on ion channels), highlighting qualitative and approximate quantitative alignments with our measurements. This will better substantiate the biological plausibility claim without altering the core experimental results. revision: yes

  2. Referee: [Mathematical model] Mathematical model section: the neuromodulation tuning parameters are treated as free parameters whose selection enables the reported robustness; the text should clarify whether the robustness holds for arbitrary choices within the stated operating ranges or only for specific tuned values, as this directly affects the 'minimal model complexity' and scalability claims.

    Authors: The robustness to current and temperature variations arises from the underlying analog feedback structure and is maintained across the full operating ranges of the neuromodulation parameter, not solely for specific tuned values. The parameters function as free variables to select different spiking regimes while preserving this robustness property. We will revise the mathematical model section to explicitly clarify this point, including a statement that the robustness holds for arbitrary choices within the stated ranges. This clarification will strengthen the claims regarding minimal complexity and scalability. revision: yes

Circularity Check

0 steps flagged

No circularity: model and hardware validation are independent of target claims

full rationale

The paper derives a mathematical model of the current-mode neuron from circuit topology, supplies analysis and tuning tools based on that model, and then verifies the resulting predictions of neuromodulation behavior through direct experimental measurements on a fabricated 180 nm CMOS prototype. No equation or prediction is shown to reduce by construction to a fitted parameter or to a self-citation whose content is itself unverified; the robustness and adaptation results are obtained from physical silicon behavior across current and temperature ranges rather than from re-labeling of inputs. The biological-plausibility analogy is presented as an interpretive claim supported by observed adaptation signatures, not as a mathematical identity forced by the model definition.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The work relies on standard assumptions of CMOS fabrication and biological neuron adaptation principles without introducing new physical entities or heavily fitted parameters beyond design tuning.

free parameters (1)
  • neuromodulation tuning parameters
    Parameters chosen to achieve desired adaptation ranges in the model and circuit.
axioms (2)
  • domain assumption Standard 180 nm CMOS fabrication processes behave as expected for analog current-mode circuits
    Invoked for the physical implementation and temperature/current scalability claims.
  • domain assumption Biological neurons achieve robustness through neuromodulation of input responses and spiking patterns
    Used to frame the target behavior for the silicon design.

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Forward citations

Cited by 3 Pith papers

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Reference graph

Works this paper leans on

57 extracted references · 57 canonical work pages · cited by 2 Pith papers · 1 internal anchor

  1. [1]

    Beyond the connectome: how neuromodulators shape neural circuits,

    C. I. Bargmann, “Beyond the connectome: how neuromodulators shape neural circuits,” BioEssays: News and Reviews in Molecular, Cellular and Developmental Biology, vol. 34, pp. 458–465, June 2012

    work page 2012

  2. [2]

    Neuromodulation of Neuronal Circuits: Back to the Future,

    E. Marder, “Neuromodulation of Neuronal Circuits: Back to the Future,”Neuron, vol. 76, pp. 1–11, Oct. 2012

    work page 2012

  3. [3]

    Neuromodulatory Systems and Their Interactions,

    M. C. Avery and J. L. Krichmar, “Neuromodulatory Systems and Their Interactions,” Frontiers in Neural Circuits, vol. 11, p. 108, Dec. 2017

    work page 2017

  4. [4]

    Cellular switches orchestrate rhythmic circuits,

    G. Drion, A. Franci, and R. Sepulchre, “Cellular switches orchestrate rhythmic circuits,” Biological Cybernetics, vol. 113, pp. 71–82, Apr. 2019. 15 November 30, 2025 A Preprint - L Mendoliaet al

    work page 2019

  5. [5]

    Editorial overview: Neuromodulation: Tuning the properties of neurons, networks and behavior,

    D. A. McCormick and M. P. Nusbaum, “Editorial overview: Neuromodulation: Tuning the properties of neurons, networks and behavior,”Current opinion in neurobiology, vol. 29, pp. iv–vii, Dec. 2014

    work page 2014

  6. [6]

    Embodied neuromorphic intelligence,

    C. Bartolozzi, G. Indiveri, and E. Donati, “Embodied neuromorphic intelligence,”Nature Communications, vol. 13, p. 1024, Feb. 2022

    work page 2022

  7. [7]

    Tonic and burst firing: dual modes of thalamocortical relay,

    S. Sherman, “Tonic and burst firing: dual modes of thalamocortical relay,”Trends in Neurosciences, vol. 24, pp. 122–126, Feb. 2001

    work page 2001

  8. [8]

    Neuromorphic electronic systems,

    C. Mead, “Neuromorphic electronic systems,”Proceedings of the IEEE, vol. 78, pp. 1629–1636, Oct. 1990

    work page 1990

  9. [9]

    Neuromorphic is dead. long live neuromorphic,

    G. Indiveri, “Neuromorphic is dead. long live neuromorphic,”Neuron, pp. 1–4, Oct. 2025

    work page 2025

  10. [10]

    Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems,

    E. Chicca, F. Stefanini, C. Bartolozzi, and G. Indiveri, “Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems,”Proceedings of the IEEE, vol. 102, pp. 1367–1388, Sept. 2014

    work page 2014

  11. [11]

    A Survey of Neuromorphic Computing and Neural Networks in Hardware

    C. D. Schuman, T. E. Potok, R. M. Patton, J. D. Birdwell, M. E. Dean, G. S. Rose, and J. S. Plank, “A Survey of Neuromorphic Computing and Neural Networks in Hardware,” May 2017. arXiv:1705.06963 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  12. [12]

    Data and Power Efficient Intelligence with Neuromorphic Learning Machines,

    E. O. Neftci, “Data and Power Efficient Intelligence with Neuromorphic Learning Machines,” iScience, vol. 5, pp. 52–68, July 2018

    work page 2018

  13. [13]

    Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems,

    J.-Q. Yang, R. Wang, Y. Ren, J.-Y. Mao, Z.-P. Wang, Y. Zhou, and S.-T. Han, “Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems,” Advanced Materials, vol. 32, no. 52, p. 2003610, 2020

    work page 2020

  14. [14]

    Neuromorphic Silicon Neurons and Large-Scale Neural Networks,

    C.-S. Poon and K. Zhou, “Neuromorphic Silicon Neurons and Large-Scale Neural Networks,” Frontiers in Neuroscience, vol. 5, p. 108, Sept. 2011

    work page 2011

  15. [15]

    Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain,

    C. S. Thakur, J. L. Molin, G. Cauwenberghs, G. Indiveri, K. Kumar, N. Qiao, J. Schemmel, R. Wang, E. Chicca, J. Olson Hasler, J.-s. Seo, S. Yu, Y. Cao, A. van Schaik, and R. Etienne-Cummings, “Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain,”Frontiers in Neuroscience, vol. 12, p. 891, Dec. 2018

    work page 2018

  16. [16]

    Demonstrating advantages of neuromorphic computation: A pilot study,

    T. Wunderlich, A. F. Kungl, E. Müller, A. Hartel, Y. Stradmann, S. A. Aamir, A. Grübl, A. Heimbrecht, K. Schreiber, D. Stöckel, C. Pehle, S. Billaudelle, G. Kiene, C. Mauch, J. Schemmel, K. Meier, and M. A. Petrovici, “Demonstrating advantages of neuromorphic computation: A pilot study,”Frontiers in Neuroscience, vol. 13, 2019

    work page 2019

  17. [17]

    Silicon neurons that burst when primed,

    K. M. Hynna and K. Boahen, “Silicon neurons that burst when primed,” in2007 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 3363–3366, May 2007. ISSN: 2158-1525

    work page 2007

  18. [18]

    Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI,

    T. Yu, T. J. Sejnowski, and G. Cauwenberghs, “Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI,”IEEE Transactions on Biomedical Circuits and Systems, vol. 5, pp. 420–429, Oct. 2011

    work page 2011

  19. [19]

    BioNN: Bio-Mimetic Neural Networks on Hardware Using Nonlinear Multi-Timescale Mixed-Feedback Control for Neuromodulatory Bursting Rhythms,

    K. Liu, S. Hashemkhani, J. Rubin, and R. Kubendran, “BioNN: Bio-Mimetic Neural Networks on Hardware Using Nonlinear Multi-Timescale Mixed-Feedback Control for Neuromodulatory Bursting Rhythms,”IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 13, pp. 914–926, Dec. 2023

    work page 2023

  20. [20]

    Bursting dynamics remarkably improve the performance of neural networks on liquid computing,

    X. Li, Q. Chen, and F. Xue, “Bursting dynamics remarkably improve the performance of neural networks on liquid computing,”Cognitive Neurodynamics, vol. 10, pp. 415–421, Oct. 2016

    work page 2016

  21. [21]

    Artificial Spiking Neuron with Bursting Dynamics for Noise-Resistant Neuromorphic Coding,

    H. Liu, X. Zhu, Z. Guo, R. He, X. Li, Q. Sun, X. Ye, C. Sun, Y. Tian, and R.-W. Li, “Artificial Spiking Neuron with Bursting Dynamics for Noise-Resistant Neuromorphic Coding,”ACS Applied Electronic Materials, vol. 5, pp. 3454–3461, June 2023

    work page 2023

  22. [22]

    A burst-dependent algorithm for neuromorphic on-chip learning of spiking neural networks,

    M. Stuck, X. Wang, and R. Naud, “A burst-dependent algorithm for neuromorphic on-chip learning of spiking neural networks,”Neuromorphic Computing and Engineering, vol. 5, p. 014010, Feb. 2025. 16 November 30, 2025 A Preprint - L Mendoliaet al

    work page 2025

  23. [23]

    Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics,

    J. Mei, A. Rodriguez-Garcia, D. Takeuchi, G. Wainstein, N. Hubig, Y. Mohsenzadeh, and S. Ramaswamy, “Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics,” Jan. 2025. arXiv:2501.06762 [q-bio]

    work page arXiv 2025

  24. [24]

    Central pattern generators for locomotion control in animals and robots,

    A. J. Ijspeert, “Central pattern generators for locomotion control in animals and robots,” Neural Networks, vol. 21, pp. 642–653, May 2008

    work page 2008

  25. [25]

    Neuromorphic Pattern Generation Circuits for Bioelectronic Medicine,

    E. Donati, R. Krause, and G. Indiveri, “Neuromorphic Pattern Generation Circuits for Bioelectronic Medicine,” Feb. 2021. arXiv:2102.09630 [cs]

    work page arXiv 2021

  26. [26]

    Neuromorphic Instantiation of Spiking Half-Centered Oscillator Models for Central Pattern Generation,

    A. Athota, B. Caccam, R. Kochis, A. Ray, G. Cauwenberghs, and F. D. Broccard, “Neuromorphic Instantiation of Spiking Half-Centered Oscillator Models for Central Pattern Generation,” in2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 6703–6706, Nov. 2021

    work page 2021

  27. [27]

    Neuromorphic adaptive spiking CPG towards bio-inspired locomotion,

    P. Lopez-Osorio, A. Patiño-Saucedo, J. P. Dominguez-Morales, H. Rostro-Gonzalez, and F. Perez-Peña, “Neuromorphic adaptive spiking CPG towards bio-inspired locomotion,” Neurocomputing, vol. 502, pp. 57–70, Sept. 2022

    work page 2022

  28. [28]

    The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity,

    C. Pehle, S. Billaudelle, B. Cramer, J. Kaiser, K. Schreiber, Y. Stradmann, J. Weis, A. Leibfried, E. Müller, and J. Schemmel, “The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity,” Feb. 2022. arXiv:2201.11063 [cs]

    work page arXiv 2022

  29. [29]

    An Accelerated LIF Neuronal Network Array for a Large-Scale Mixed-Signal Neuromorphic Architecture,

    S. A. Aamir, Y. Stradmann, P. Müller, C. Pehle, A. Hartel, A. Grübl, J. Schemmel, and K. Meier, “An Accelerated LIF Neuronal Network Array for a Large-Scale Mixed-Signal Neuromorphic Architecture,”IEEE Transactions on Circuits and Systems—Part I: Regular Papers, vol. 65, pp. 4299–4312, Dec. 2018

    work page 2018

  30. [30]

    DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor,

    O. Richter, C. Wu, A. M. Whatley, G. Köstinger, C. Nielsen, N. Qiao, and G. Indiveri, “DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor,”Neuromorphic Computing and Engineering, vol. 4, p. 014003, Jan. 2024. Publisher: IOP Publishing

    work page 2024

  31. [31]

    A Balance Equation Determines a Switch in Neuronal Excitability,

    A. Franci, G. Drion, V. Seutin, and R. Sepulchre, “A Balance Equation Determines a Switch in Neuronal Excitability,”PLOS Computational Biology, vol. 9, p. e1003040, May 2013

    work page 2013

  32. [32]

    Dynamic Input Conductances Shape Neuronal Spiking,

    G. Drion, A. Franci, J. Dethier, and R. Sepulchre, “Dynamic Input Conductances Shape Neuronal Spiking,”eneuro, vol. 2, pp. ENEURO.0031–14.2015, Jan. 2015

    work page 2015

  33. [33]

    Implementing robust neuromodulation in neuromorphic circuits,

    F. Castaños and A. Franci, “Implementing robust neuromodulation in neuromorphic circuits,” Neurocomputing, vol. 233, pp. 3–13, Apr. 2017

    work page 2017

  34. [34]

    Neuromodulation of Neuromorphic Circuits,

    L. Ribar and R. Sepulchre, “Neuromodulation of Neuromorphic Circuits,”IEEE Transactions on Circuits and Systems—Part I: Regular Papers, vol. 66, pp. 3028–3040, Aug. 2019

    work page 2019

  35. [35]

    Control Across Scales by Positive and Negative Feedback,

    R. Sepulchre, G. Drion, and A. Franci, “Control Across Scales by Positive and Negative Feedback,”Annual Review of Control, Robotics, and Autonomous Systems, vol. 2, pp. 89–113, May 2019

    work page 2019

  36. [36]

    Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence,

    A. Rubino, C. Livanelioglu, N. Qiao, M. Payvand, and G. Indiveri, “Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence,”IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, pp. 45–56, Jan. 2021

    work page 2021

  37. [37]

    Rubino,Mixed-Signal Neuromorphic Circuits and Systems for Extreme-Edge Computing

    A. Rubino,Mixed-Signal Neuromorphic Circuits and Systems for Extreme-Edge Computing. PhD thesis, University of Zurich, Zürich, Mar. 2025

    work page 2025

  38. [38]

    An ultra low power current-mode filter for neuromorphic systems and biomedical signal processing,

    C. Bartolozzi, S. Mitra, and G. Indiveri, “An ultra low power current-mode filter for neuromorphic systems and biomedical signal processing,” in2006 IEEE Biomedical Circuits and Systems Conference, pp. 130–133, Nov. 2006. ISSN: 2163-4025

    work page 2006

  39. [39]

    A quantitative description of membrane current and its application to conduction and excitation in nerve,

    A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve,”The Journal of Physiology, vol. 117, pp. 500–544, Aug. 1952

    work page 1952

  40. [40]

    E. M. Izhikevich,Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT Press, July 2006. 17 November 30, 2025 A Preprint - L Mendoliaet al

    work page 2006

  41. [41]

    A simple neuron servo,

    S. DeWeerth, L. Nielsen, C. Mead, and K. Astrom, “A simple neuron servo,”IEEE Transactions on Neural Networks, vol. 2, pp. 248–251, Mar. 1991

    work page 1991

  42. [42]

    Hille,Ion Channels of Excitable Membranes

    B. Hille,Ion Channels of Excitable Membranes. Sinauer Associates Inc.,U.S., 2001

    work page 2001

  43. [43]

    Sodium channel slow inactivation normalizes firing in axons with uneven conductance distributions,

    Y. Zang, E. Marder, and S. Marom, “Sodium channel slow inactivation normalizes firing in axons with uneven conductance distributions,”Current Biology, vol. 33, pp. 1818–1824.e3, May 2023

    work page 2023

  44. [44]

    Synaptic dynamics in analog VLSI,

    C. Bartolozzi and G. Indiveri, “Synaptic dynamics in analog VLSI,”Neural Computation, vol. 19, pp. 2581–2603, Oct. 2007

    work page 2007

  45. [45]

    A current-mode conductance-based silicon neuron for address-event neuromorphic systems,

    P. Livi and G. Indiveri, “A current-mode conductance-based silicon neuron for address-event neuromorphic systems,” in2009 IEEE International Symposium on Circuits and Systems, pp. 2898–2901, May 2009. ISSN: 2158-1525

    work page 2009

  46. [46]

    Robust and tunable bursting requires slow positive feedback,

    A. Franci, G. Drion, and R. Sepulchre, “Robust and tunable bursting requires slow positive feedback,”Journal of Neurophysiology, vol. 119, pp. 1222–1234, Mar. 2018

    work page 2018

  47. [47]

    Robustness of a Rhythmic Circuit to Short- and Long-Term Temperature Changes,

    L. S. Tang, A. L. Taylor, A. Rinberg, and E. Marder, “Robustness of a Rhythmic Circuit to Short- and Long-Term Temperature Changes,”Journal of Neuroscience, vol. 32, pp. 10075–10085, July 2012

    work page 2012

  48. [48]

    Neuronal oscillator robustness to multiple global perturbations,

    J. Ratliff, A. Franci, E. Marder, and T. O’Leary, “Neuronal oscillator robustness to multiple global perturbations,”Biophysical Journal, vol. 120, pp. 1454–1468, Apr. 2021. Publisher: Elsevier

    work page 2021

  49. [49]

    Compact silicon neuron circuit with spiking and bursting behaviour,

    J. H. B. Wijekoon and P. Dudek, “Compact silicon neuron circuit with spiking and bursting behaviour,”Neural Networks, vol. 21, pp. 524–534, Mar. 2008

    work page 2008

  50. [50]

    A Novel Phase Portrait for Neuronal Excitability,

    G. Drion, A. Franci, V. Seutin, and R. Sepulchre, “A Novel Phase Portrait for Neuronal Excitability,”PLOS ONE, vol. 7, no. 8, p. e41806, 2012

    work page 2012

  51. [51]

    Robust switches in thalamic network activity require a timescale separation between sodium and T-type calcium channel activations,

    K. Jacquerie and G. Drion, “Robust switches in thalamic network activity require a timescale separation between sodium and T-type calcium channel activations,”PLOS Computational Biology, vol. 17, no. 5, p. e1008997, 2021

    work page 2021

  52. [52]

    Emulating short-term synaptic dynamics with memristive devices,

    R. Berdan, E. Vasilaki, A. Khiat, G. Indiveri, A. Serb, and T. Prodromakis, “Emulating short-term synaptic dynamics with memristive devices,”Scientific Reports, vol. 6, no. 18639, pp. 1–9, 2016

    work page 2016

  53. [53]

    Leveraging volatile memristors in neuromorphic computing: from materials to system implementation,

    T. Moon, K. Soh, J. S. Kim, J. E. Kim, S. Y. Chun, K. Cho, J. J. Yang, and J. H. Yoon, “Leveraging volatile memristors in neuromorphic computing: from materials to system implementation,”Materials Horizons, vol. 11, no. 20, pp. 4840–4866, 2024

    work page 2024

  54. [54]

    Silicon Modeling of the Mihalaş–Niebur Neuron,

    F. Folowosele, T. J. Hamilton, and R. Etienne-Cummings, “Silicon Modeling of the Mihalaş–Niebur Neuron,”IEEE Transactions on Neural Networks, vol. 22, pp. 1915–1927, Dec. 2011

    work page 1915

  55. [55]

    A compact aVLSI conductance-based silicon neuron,

    R. Wang, C. S. Thakur, T. J. Hamilton, J. Tapson, and A. Van Schaik, “A compact aVLSI conductance-based silicon neuron,” in2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), (Atlanta, GA, USA), pp. 1–4, IEEE, Oct. 2015

    work page 2015

  56. [56]

    Bio-plausible reconfigurable spiking neuron for neuromorphic computing,

    Y. Xiao, Y. Liu, B. Zhang, P. Chen, H. Zhu, E. He, J. Zhao, W. Huo, X. Jin, X. Zhang, H. Jiang, D. Ma, Q. Zheng, H. Tang, P. Lin, W. Kong, and G. Pan, “Bio-plausible reconfigurable spiking neuron for neuromorphic computing,”Science Advances, vol. 11, p. eadr6733, Feb. 2025

    work page 2025

  57. [57]

    Reliable neuromodulation from adaptive control of ion channel expression,

    A. Fyon, P. Sacré, A. Franci, and G. Drion, “Reliable neuromodulation from adaptive control of ion channel expression,”IFAC-PapersOnLine, vol. 56, pp. 458–463, Jan. 2023. 18

    work page 2023