A neural simulation system based on biologically realistic electronic neurons

Masson, Sylvie Renaud-Le; Masson, Gwendal Le; Alvado, Ludovic; Saïghi, Sylvain; Tomas, Jean

doi:10.1016/j.ins.2003.03.007

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…In the present study, we followed the formulation of the Hodgkin–Huxley equations as described in Renaud‐Le Masson et al . () and Buhry et al . ().…”

Section: Methodsmentioning

confidence: 86%

Differential processing in modality‐specific Mauthner cell dendrites

Medan

Mäki‐Marttunen

Sztarker

et al. 2017

The Journal of Physiology

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Animals process multimodal information for adaptive behavioural decisions. In fish, evasion of a diving bird that breaks the water surface depends on integrating visual and auditory stimuli with very different characteristics. How do neurons process such differential sensory inputs at the dendritic level? For that, we studied the Mauthner cells (M-cells) in the goldfish startle circuit, which receive visual and auditory inputs via two separate dendrites, both accessible for in vivo recordings. We investigated whether electrophysiological membrane properties and dendrite morphology, studied in vivo, play a role in selective sensory processing in the M-cell. The results obtained show that anatomical and electrophysiological differences between the dendrites combine to produce stronger attenuation of visually evoked postsynaptic potentials (PSPs) than to auditory evoked PSPs. Interestingly, our recordings showed also cross-modal dendritic interaction because auditory evoked PSPs invade the ventral dendrite (VD), as well as the opposite where visual PSPs invade the lateral dendrite (LD). However, these interactions were asymmetrical, with auditory PSPs being more prominent in the VD than visual PSPs in the LD. Modelling experiments imply that this asymmetry is caused by active conductances expressed in the proximal segments of the VD. The results obtained in the present study suggest modality-dependent membrane specialization in M-cell dendrites suited for processing stimuli of different time domains and, more broadly, provide a compelling example of information processing in single neurons.

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“…In the present study, we followed the formulation of the Hodgkin–Huxley equations as described in Renaud‐Le Masson et al . () and Buhry et al . ().…”

Section: Methodsmentioning

confidence: 86%

Differential processing in modality‐specific Mauthner cell dendrites

Medan

Mäki‐Marttunen

Sztarker

et al. 2017

The Journal of Physiology

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“…A promising approach to significantly improve on the computational power of today's computers typically based on von‐Neumann architectures is the modeling of real neuronal networks by electrical circuits in the context of bio‐inspired computing and neuromorphic engineering. This is because the design of close‐to‐biology circuits with the aim to mimic measured neuronal activity of neurons 1–3 might allow us to interpret the principles behind the energy‐efficiency and information processing speed of for example, the human brain from a circuit theoretical point of view. Hence, this can enable the derivation of novel design principles for electrical circuits.…”

Section: Introductionmentioning

confidence: 99%

Wave digital model of calcium‐imaging‐based neuronal activity of mice

Jenderny

Ochs

2022

Int J Numerical Modelling

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The modeling of real neuronal networks by close‐to‐biology circuits can contribute to a better understanding of these networks from a circuit theoretical point of view. This can potentially lead to novel design principles for electrical circuits. This modeling process involves the mimicking of real measured neuronal activity, which is often only available as calcium imaging recordings. In this work, we develop a wave digital model as a circuit emulator capable of mimicking the calcium‐imaging‐based neuronal activity of mouse neurons. For this purpose, we design an electrical reference circuit of a mouse neuron based on an extended Morris–Lecar model in combination with an RC circuit. The circuit parameters are determined by a parameter scaling without requiring any parameter optimization. The input signal of the resulting wave digital model is designed based on estimating spike times with the spike inference software MLspike. Wave digital emulations show the successful mimicking of calcium‐based neuronal activity for mouse neurons located in the visual, motor and somatosensory cortex as well the possibility to predict ion currents occurring during this activity. This proves the suitability of the reference circuit as a model for the considered types of mouse neurons. It also implies that the circuit can act as an observer for electrical signals of mouse neurons. This is achieved despite the lack of electrophysiological measurements in most cases. Hence, the proposed wave digital model can be seen as an important step toward being able to verify close‐to‐biology electrical circuits as a model of real neuronal networks without electrophysiology.

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“…It is studied for some different purposes. One of them is to construct hybrid system of silicon and biological neurons [l]- [6], which can be utihzed to validate a neuron model by replacing a target neuron in biological neural network with silicon one and examining if the network can operate correctly. It also can be applied to implantable biomedical devices and neural network simulators.…”

Section: Introductionmentioning

confidence: 99%