Modelling unitary fields and the single‐neuron contribution to local field potentials in the hippocampus

Abstract

Key points We simulate the unitary local field potential (uLFP) generated in the hippocampus CA3, using morphologically detailed models. The model suggests that cancelling effects between apical and basal dendritic synapses explain the low amplitude of excitatory uLFPs. Inhibitory synapses around the soma do not cancel and could explain the high‐amplitude inhibitory uLFPs. These results suggest that somatic inhibition constitutes a strong component of LFPs, which may explain a number of experimental observations. Abstract Synaptic currents represent a major contribution to the local field potential (LFP) in brain tissue, but the respective contribution of excitatory and inhibitory synapses is not known. Here, we provide estimates of this contribution by using computational models of hippocampal pyramidal neurons, constrained by in vitro recordings. We focus on the unitary LFP (uLFP) generated by single neurons in the CA3 region of the hippocampus. We first reproduce experimental results for hippocampal basket cells, and in particular how inhibitory uLFP are distributed within hippocampal layers. Next, we calculate the uLFP generated by pyramidal neurons, using morphologically reconstructed CA3 pyramidal cells. The model shows that the excitatory uLFP is of small amplitude, smaller than inhibitory uLFPs. Indeed, when the two are simulated together, inhibitory uLFPs mask excitatory uLFPs, which might create the illusion that the inhibitory field is generated by pyramidal cells. These results provide an explanation for the observation that excitatory and inhibitory uLFPs are of the same polarity, in vivo and in vitro. These results suggest that somatic inhibitory currents are large contributors to the LFP, which is important information for interpreting this signal. Finally, the results of our model might form the basis of a simple method to compute the LFP, which could be applied to point neurons for each cell type, thus providing a simple biologically grounded method for calculating LFPs from neural networks. In conclusion, computational models constrained by in vitro recordings suggest that: (1) Excitatory uLFPs are of smaller amplitude than inhibitory uLFPs. (2) Inhibitory uLFPs form the major contribution to LFPs. (3) uLFPs can be used as a simple model to generate LFPs from spiking networks.

Brain signal predictions from multi-scale networks using a linearized framework

“…This so-called monosynaptic, also referred to as unitary (e.g., by Bazelot et al 2010) extracellular response is recently modeled in detail (Hagen et al 2017), then using conductance-based synapses but with passive membrane time constants fitted to available experimental and published data. A similar effort to compute such responses in the hippocampus was recently published by Teleńczuk et al 2020b. Teleńczuk et al 2020a proposed using such responses then fitted to spatiotemporal kernel shape functions for excitatory and inhibitory presynaptic units in order to compute LFP signals in point-neuron network models.…”

“…Same as Figure9, but with the excitatory cell model being replaced by a biophysically detailed pyramidal cell model(Hay et al 2011), and accounting only for contributions by transmembrane currents of this updated excitatory population.with passive membrane time constants fitted to available experimental and published data. A similar effort to compute such responses in hippocampus was recently published byTeleńczuk et al 2020b. Teleńczuk et al 2020a proposed using such responses then fitted to spatiotemporal kernel shape functions for excitatory and inhibitory presynaptic units in order to compute LFP signals in point-neuron network models.…”

Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations

“…The amplitude, duration, and polarity of uLFPs can vary significantly depending on the type of presynaptic neuron, the location of its axons on the postsynatic target and anatomical connectivity through postsynaptic receptors ( Swadlow et al, 2002 ; Bereshpolova et al, 2006 ; Stoelzel et al, 2008 ; Glickfeld et al, 2009 ; Bazelot et al, 2010 ). Computational modeling has been utilized to study the possible mechanisms underlying the uLFP signatures generated by fast-spiking inhibitory vs regular-spiking excitatory presynaptic single neurons ( Hagen et al, 2017 ; Telenczuk et al, 2020b ). These studies employed anatomically-constrained virtual slices/neuronal columns comprising morphologically-realistic post-synaptic neurons with experimentally determined synapse localization ( Hagen et al, 2017 ), and trimmed axonal arborization of unitary presynaptic neuron to match the realistic size of an in vitro slice ( Telenczuk et al, 2020b ).…”

“…Computational modeling has been utilized to study the possible mechanisms underlying the uLFP signatures generated by fast-spiking inhibitory vs regular-spiking excitatory presynaptic single neurons ( Hagen et al, 2017 ; Telenczuk et al, 2020b ). These studies employed anatomically-constrained virtual slices/neuronal columns comprising morphologically-realistic post-synaptic neurons with experimentally determined synapse localization ( Hagen et al, 2017 ), and trimmed axonal arborization of unitary presynaptic neuron to match the realistic size of an in vitro slice ( Telenczuk et al, 2020b ). They have provided an explanation for how a disynaptic excitatory uLFP can sometimes look like an inhibitory uLFP ( Bazelot et al, 2010 ), highlighting the latter’s dominance when the recording electrode is located inside a predominantly inhibitory population ( Telenczuk et al, 2020b ).…”

Early alterations of the neuronal network processing whisker‐related sensory signal during absence epileptogenesis

“…Because the relation between LFP signal and spiking activity is not always linear, 28,29 we then further explored the neuronal sensory response in the different wS1 cortical layers using MUA evoked by stimulation (Figure 3A,F). The peristimulus time histogram maps allowed 3B,G).…”