Extending the integrate‐and‐fire model to account for metabolic dependencies

Jaras, Ismael; Harada, Taiki; Orchard, Marcos E.; Maldonado, Pedro E.; Vergara, Rodrigo C.

doi:10.1111/ejn.15326

Cited by 7 publications

(6 citation statements)

References 38 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The implementation of the BOLD monitor is flexible enough so that the source variables for the BOLD calculation can be any of the variables present in the neuron models (e.g., a combination of different synaptic currents). Recently, an energy-dependent leaky integrate-and-fire neuron model has been developed that accounts for the neuron's energy consumption by calculating adenosine triphosphate (ATP) dynamics (Jaras et al, 2021 ). The variables involved there, which are associated with the brain's metabolism, could be of great interest for calculating the BOLD signal and could be easily linked to BOLD models in ANNarchy using the BOLD monitor.…”

Section: Discussionmentioning

confidence: 99%

BOLD Monitoring in the Neural Simulator ANNarchy

Maith

Dinkelbach

Baladron

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

Multi-scale network models that simultaneously simulate different measurable signals at different spatial and temporal scales, such as membrane potentials of single neurons, population firing rates, local field potentials, and blood-oxygen-level-dependent (BOLD) signals, are becoming increasingly popular in computational neuroscience. The transformation of the underlying simulated neuronal activity of these models to simulated non-invasive measurements, such as BOLD signals, is particularly relevant. The present work describes the implementation of a BOLD monitor within the neural simulator ANNarchy to allow an on-line computation of simulated BOLD signals from neural network models. An active research topic regarding the simulation of BOLD signals is the coupling of neural processes to cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2). The flexibility of ANNarchy allows users to define this coupling with a high degree of freedom and thus, not only allows to relate mesoscopic network models of populations of spiking neurons to experimental BOLD data, but also to investigate different hypotheses regarding the coupling between neural processes, CBF and CMRO2 with these models. In this study, we demonstrate how simulated BOLD signals can be obtained from a network model consisting of multiple spiking neuron populations. We first demonstrate the use of the Balloon model, the predominant model for simulating BOLD signals, as well as the possibility of using novel user-defined models, such as a variant of the Balloon model with separately driven CBF and CMRO2 signals. We emphasize how different hypotheses about the coupling between neural processes, CBF and CMRO2 can be implemented and how these different couplings affect the simulated BOLD signals. With the BOLD monitor presented here, ANNarchy provides a tool for modelers who want to relate their network models to experimental MRI data and for scientists who want to extend their studies of the coupling between neural processes and the BOLD signal by using modeling approaches. This facilitates the investigation and model-based analysis of experimental BOLD data and thus improves multi-scale understanding of neural processes in humans.

show abstract

Section: Discussionmentioning

confidence: 99%

BOLD Monitoring in the Neural Simulator ANNarchy

Maith

Dinkelbach

Baladron

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

show abstract

“…Consequently, neuronal behavior depends on the adequate balance between energy production and expenditure. The main objective of the EDLIF [19] model is to include the energy dependence in the neuronal dynamics while maintaining the LIF model's simplicity. To accomplish that, the EDLIF model makes the neuronal behavior explicitly dependent on the available neuronal energy through the energy-dependent partial repolarization mechanism.…”

Section: Methodsmentioning

confidence: 99%

“…Plugging Eqn. (19) into Eqn. (17), it is possible to describe weight's update accounting for pre-and postsynaptic spike-time and postsynaptic energy level:…”

Section: Energy Dependent Spike-timing-dependent Plasticity Modelmentioning

confidence: 99%

“…This work fills the gap by committing to the previous approach. Here we create an energy-dependent Spike-timing-dependent plasticity rule and, jointly with a previously introduced energy-dependent neuronal model [19], we study the role of local metabolic constraints on the structure and activity of spiking neural networks, particularly in Excitatory-Inhibitory (E-I) balanced networks. Our exploration employs two complementary methods to assess the impact of metabolic constraints at the network level.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unveiling the role of local metabolic constraints on the structure and activity of spiking neural networks

Jaras,

Orchard,

Maldonado

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Understanding the intricate interplay between neural dynamics and metabolic constraints is crucial for unraveling the mysteries of the brain. Despite the significance of this relationship, specific details concerning the impact of metabolism on neuronal dynamics and neural network architecture remain elusive, creating a notable gap in the existing literature. This study employs an energy-dependent neuron and plasticity model to analyze the role of local metabolic constraints in shaping both the dynamics and structure of Spiking Neural Networks (SNN). Specifically, an energy-dependent version of the leaky integrate-and-fire model is utilized, along with a three-factor learning rule that incorporates postsynaptic available energy as the third factor. These models allow for fine-tuning sensitivity in the presence of energy imbalances. Analytical expressions predicting the network's activity and structure are derived, and a fixed-point analysis reveals the emergence of attractor states characterized by neuronal and synaptic sensitivity to energy imbalances. Analytical findings are validated through numerical simulations using an excitatory-inhibitory balanced network. Furthermore, these simulations enable the study of SNN activity and structure under conditions simulating metabolic impairment. In conclusion, by employing energy-dependent models with adjustable sensitivity to energy imbalances, our study advances the understanding of how metabolic constraints shape SNN dynamics and structure. Moreover, in light of compelling evidence linking neuronal metabolic impairment to neurodegenerative diseases, the incorporation of local metabolic constraints into the investigation of neuronal network structure and activity opens an intriguing avenue for inspiring the development of therapeutic interventions.

show abstract

“…Stochastic Integrate and Fire (IF) models describe the interspike intervals (ISIs) as first passage times of a stochastic process through a boundary and own their success to their capability of conjugating some sort of biological realism with a reasonable mathematical tractability [12,13,22,37,44]. Since the seminal paper [21], many modifications of such model were proposed with the aim to improve the realism without losing the actual tractability [17,18,24,25]. Generally, these stochastic IF models are Markovian thanks to the assumptions about the input from the surrounding network that is hypothesized Poissonian [28,36] and to the independence assumption on superimposed inputs [1,4,36,40] (see [5] for an example of semi-Markov IF model).…”

Section: Introductionmentioning

confidence: 99%

Input-output consistency in integrate and fire interconnected neurons

Lánský¹,

Polito²,

Sacerdote³

2022

Preprint

View full text Add to dashboard Cite

Interspike intervals describe the output of neurons. Information transmission in a neuronal network implies that the output of some neurons becomes the input of others. The output should reproduce the main features of the input to avoid a distortion when it becomes the input of other neurons, that is input and output should exhibit some sort of consistency. In this paper, we consider the question: how should we mathematically characterize the input in order to get a consistent output? Here we interpret the consistency by requiring the reproducibility of the input tail behaviour of the interspike intervals distributions in the output. Our answer refers to a neuronal network with integrate and fire units. In particular, we show that the class of regularly-varying vectors is a possible choice to obtain such consistency. Some further necessary technical hypotheses are added.

show abstract

Extending the integrate‐and‐fire model to account for metabolic dependencies

Cited by 7 publications

References 38 publications

BOLD Monitoring in the Neural Simulator ANNarchy

BOLD Monitoring in the Neural Simulator ANNarchy

Unveiling the role of local metabolic constraints on the structure and activity of spiking neural networks

Input-output consistency in integrate and fire interconnected neurons

Contact Info

Product

Resources

About