Abstract
Intrinsic plasticity (IP) is a ubiquitous activity-dependent process regulating neuronal excitability and a cellular correlate of behavioral learning and neuronal homeostasis. Because IP is induced rapidly and maintained long-term, it likely represents a major determinant of adaptive collective neuronal dynamics. However, assessing the exact impact of IP has remained elusive. Indeed, it is extremely difficult disentangling the complex non-linear interaction between IP effects, by which conductance changes alter neuronal activity, and IP rules, whereby activity modifies conductance via signaling pathways. Moreover, the two major IP effects on firing rate, threshold and gain modulation, remain unknown in their very mechanisms. Here, using extensive simulations and sensitivity analysis of Hodgkin-Huxley models, we show that threshold and gain modulation are accounted for by maximal conductance plasticity of conductance that situate in two separate domains of the parameter space corresponding to sub- and supra-threshold conductance (i.e. activating below or above the spike onset threshold potential). Analyzing equivalent integrate-and-fire models, we provide formal expressions of sensitivities relating to conductance parameters, unraveling unprecedented mechanisms governing IP effects. Our results generalize to the IP of other conductance parameters and allow strong inference for calcium-gated conductance, yielding a general picture that accounts for a large repertoire of experimental observations. The expressions we provide can be combined with IP rules in rate or spiking models, offering a general framework to systematically assess the computational consequences of IP of pharmacologically identified conductance with both fine grain description and mathematical tractability. We provide an example of such IP loop model addressing the important issue of the homeostatic regulation of spontaneous discharge. Because we do not formulate any assumptions on modification rules, the present theory is also relevant to other neural processes involving excitability changes, such as neuromodulation, development, aging and neural disorders.
Highlights
Ion channels of neuron membranes undergo long-term experience-dependent modifications of their biochemical and biophysical state induced by on-going neuronal activity, a process called intrinsic plasticity (IP; [1,2])
Behavioral learning has been correlated with experience-dependent changes of non-synaptic voltage-dependent ion channels
We further provide analytical descriptions that enlighten the dynamical mechanisms underlying these effects and propose a concise and realistic framework for assessing the computational impact of intrinsic plasticity in neuron network models
Summary
Ion channels of neuron membranes undergo long-term experience-dependent modifications of their biochemical and biophysical state induced by on-going neuronal activity, a process called intrinsic plasticity (IP; [1,2]). Regulating channels’ state changes neuron excitability, i.e. its propensity to discharge in response to synaptic inputs. IP continuously modifies collective neuronal dynamics, taking part to the adaptive and learning abilities of neural networks, as do synaptic and structural plasticity [3,4]. IP has proved a ubiquitous cellular correlate of behavioral learning [5,6,7,8] and of neural network homeostatic regulation [9,10,11]. Channels’ state sets the activity in response to synaptic inputs, a dependency we name IP effects hereafter
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