P196 Kernel-based LFP estimation in detailed large-scale spiking network model of visual cortex
Nicolò Meneghetti1,2,*, Atle E. Rimehaug3, Gaute T. Einevoll4,5, Alberto Mazzoni1,2, Torbjørn V. Ness4
1The Biorobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
2Department of Excellence for Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
3Department of Informatics, University of Oslo, Oslo, Norway
4Department of Physics, Norwegian University of Life Sciences, Ås, Norway
5Department of Physics, University of Oslo, Oslo, Norway
*Email: nicolo.meneghetti@santannapisa.it
Introduction
Large-scale neuronal networks are fundamental tools in computational neuroscience. A key challenge in this domain is simulating measurable signals like local field potentials (LFPs), which bridge the gap between in silico model predictions and experimental data. Simulating LFPs in large-scale models, however, requires biologically detailed multicompartmental (MC) neuron models, which impose significant computational demands. To address this, multiple simplified approaches have been developed. In our work [1] we extended a kernel-based method to enable accurate LFP estimation in a state-of-the-art MC model of the mouse primary visual cortex (V1) from the Allen Institute [2], [3] while significantly reducing computational costs.
Methods
This V1 model features extensive biological detail, with over 50,000 MC neurons across six cortical layers[2], as well as experimentally recorded afferent inputs from both thalamic and lateromedial visual areas[3].
Instead of direct MC simulations, our method estimates the LFP by convolving population firing rates with precomputed spatiotemporal kernels (Fig. 1A), which represent the average postsynaptic LFP response to a presynaptic spike (see e.g., Fig.1B). This drastically reduced computational cost while maintaining estimation accuracy.
Results
The kernel method accurately estimated LFPs in both superficial (Fig. 1C) and deep layers (Fig. 1D). By treating LFPs as the sum of convolutions of neuronal firing rates and LFP-kernels, the method also enabled disentangling the contributions of different neuronal populations to the overall LFP. We found that V1 LFPs are primarily driven by external inputs, with thalamic afferents dominating in layer 4 (Fig. 1F) and lateromedial feedback influencing L2/3 layers (Fig. 1E). In contrast, local synaptic activity contributed minimally, challenging the conventional view that PV neurons are primary LFP drivers [4]. In fact, we showed that PV apparent influence on LFP reflects their correlation with external inputs rather than direct contribution.
Discussion
Our findings establish the kernel-based method as a robust and efficient tool for LFP estimation in large-scale network models. By significantly reducing computational costs, this approach makes detailed LFP simulations more practical while also providing insights into cortical LFP generation. Our results highlight the predominant role of external synaptic inputs, while challenging the conventional view that local network activity, including inhibitory interneurons, is a primary LFP driver. This methodology provides a useful framework for studying sensory processing and network dynamics in large-scale models, helping to clarify the contributions of different neuronal populations to cortical LFPs.
Figure 1. (A) Schematic of the kernel-based LFP estimation. (B) Set of kernels for computing L2/3 LFPs for different presynaptic families. (C) L2/3 LFPs computed with both MC simulations (red) and kernel convolution (black). (D) Same as C, for layer 4. (E) Cross-R² matrix between the total L2/3 LFPs and the LFP generated by the synaptic activity of each population in the model. (F) Same as C, for layer 4.
Acknowledgements
This work was supported by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MAD-2022-12376927 (“The etiopathological basis of gait derangement in Parkinson’s disease: decoding locomotor network dynamics”).
References
[1]https://doi.org/10.1101/2024.11.29.626029
[2]https://doi.org/10.1016/j.neuron.2020.01.040
[3]https://doi.org/10.7554/eLife.87169
[4]https://doi.org/10.1038/srep40211
