Neuronal gain modulability is determined by dendritic morphology: a computational optogenetic study

Abstract

Gain modulability indicated by dendritic morphology• Pyramidal cell-like shapes optimally receptive to modulation • All dendritic subdomains required for gain modulation, partial illumination is insufficient• Computational optogenetic models improve and refine experimental protocols PLOS 1/19 39 the balance of excitatory and inhibitory currents locally in dendrites, to act as a 40 synthetic substitute for the effect of excitatory and inhibitory presynaptic input. This 41 raises the prospect of a viable experimental method with which to investigate the 42 mechanisms of neuronal gain modulation in the whole cell, as opposed to studying 43 somatic effects alone. 44Here we demonstrate through a computational model that neuronal gain modulation 45 can be determined by cell morphology, by means of a set of dendritic morphological 46 features which mediate between attenuation and shunting to modulate neuronal output. 47 The local interaction of competing excitatory and inhibitory inputs is sensitive to the 48 placement of dendritic sections. This indicates that gain modulation can be achieved by 49 altering the overall balance of excitation and inhibition that a neuron receives, rather 50 than being dependent on the statistical properties of the synaptic input. As 51 experimental validation of our work would require optogenetics, we tested our 52 PLOS 2/19 hypothesis in detailed, biophysical models of opsin-transfected neurons, using 53 experimentally fitted models of channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR), 54 which when activated produced excitatory and inhibitory photocurrents. 55This study proposes a new perspective on the contribution of dendritic morphology 56 to the characteristics of neurons, relating the shape of their dendritic arbors directly to 57 their functional role within the neural circuit. We show that all dendritic subdomains 58 are required for gain modulation to occur, suggesting that distinct innervation through 59 synaptic targeting by discrete presynaptic populations could be an effective mechanism 60 by which a neuron's output can be quickly gated between high gain and low gain modes. 61 By incorporating kinetic opsin models within our detailed, compartmental neuron 62 modeling approach, we make predictions which are directly testable by optogenetic 63 experiments. In particular, our model leads to the proposal of a new illumination 64 protocol for a more naturalistic method of neuronal photostimulation -in which rather 65 than simply imposing spikes or shutting down neuronal activity entirely, it is possible to 66 increase or decrease the gain of a neuron's response to existing inputs. 67 We would also like to clarify the meaning of our terminology for the "in vitro" and 68 "in vivo" simulations: "in vitro" refers to "a single point injection, as per patch clamp 69 experiments", and "in vivo" refers to "multiple synaptic-like activity". In this way we 70 emphasize the significant difference whether the driving input was a single point or 71 multiple synaptic sites. 72 Results 73De...

Treatment with Mesenchymal-Derived Extracellular Vesicles Reduces Injury-Related Pathology in Pyramidal Neurons of Monkey Perilesional Ventral Premotor Cortex

“…At the structural level, vPMC EV neurons exhibited significantly greater apical dendrite complexity, MAP2 expression, and densities of spines and inhibitory inputs. Increased dendritic inhibition and greater dendritic complexity allow for greater dendritic filtering in vPMC EV neurons (Magee and Johnston, 2005;Capaday and van Vreeswijk, 2006;Muller et al, 2012;Ferrante et al, 2013;Pouille et al, 2013;Jarvis et al, 2018), providing a mechanism to maintain the E:I balance of synaptic events recorded at the soma. Further, EV-treated brains had significantly greater c-fos 1 activation of CB 1 neurons, the major source of inhibitory inputs on distal dendrites and spines in the primate cortex (for review, see DeFelipe, 1997).…”

Intrinsic temporal tuning of neurons in the optic tectum is shaped by multisensory experience

“…And yet, with the possible exception of oscillatory networks (Marder and Taylor, 2011;Picton et al, 2018), the exact ways in which intrinsic plasticity contributes to network tuning, remain unclear. We know that neurons adjust their spikiness to match the levels of synaptic activation they experience (Aizenman et al, 2003;Titley et al, 2017), but we also know that intrinsic properties can affect more subtle neuronal tuning to different temporal patterns of activation (Azouz and Gray, 2000;Branco et al, 2010;Fontaine et al, 2014;Jarvis et al, 2018;Ohtsuki and Hansel, 2018;Zbili et al, 2019). This begs the question: do neurons use this type of tuning in practice, dynamically adjusting it to the temporal dynamics of their inputs?…”

The Input-Output Relation of Primary Nociceptive Neurons is Determined by the Morphology of the Peripheral Nociceptive Terminals

“…To further validate this conclusion, we increased the complexity of the balanced binary tree type of the nociceptive terminal (Figure 3). We systematically examined the activation of a single terminal branch, the simultaneous stimulation of two adjacent (sisters) terminal branches and all terminal branches at the terminal trees composed of a different number of terminal branches (nTB) defined by the number of the "bifurcation stages" (nl) according to nTB = 2 nl-1 (Figure 3A, see Methods, Jarvis et al, 2018). Activation of a single terminal branch by a capsaicin-like current in nl = 2 tree with two terminals, evoked one AP at the central terminal (Figure 3C, Figure 2) and two APs in more complex examined trees (Figure 3B, C, yellow).…”

“…All terminal tree branches, unless otherwise mentioned, were identical in length (50 m). Binary tree structures were changed according to the number of their bifurcation stages (nl), which controlled the total number of the terminal branches (nTB) according to nTB = 2 nl-1 (Jarvis et al, 2018).…”