[Paper Review] Enhanced responsiveness in asynchronous irregular neuronal networks

Zahara Girones, Alain Destexhe|arXiv (Cornell University)|Nov 28, 2016

Neural dynamics and brain function26 references3 citations

TL;DR

This paper demonstrates that asynchronous irregular (AI) neuronal networks exhibit enhanced network-level responsiveness only when neurons operate in a physiologically realistic high-conductance state with experimentally measured fluctuation regimes. Using biophysically detailed models, the authors show that such networks globally respond to external inputs with high sensitivity, suggesting this state supports a low-level form of sensory awareness through dynamic, non-synchronous network-wide responsiveness.

ABSTRACT

Networks of excitatory and inhibitory neurons display asynchronous irregular (AI) states, where the activities of the two populations are balanced. At the single cell level, it was shown that neurons subject to balanced and noisy synaptic inputs can display enhanced responsiveness. We show here that this enhanced responsiveness is also present at the network level, but only when single neurons are in a conductance state and fluctuation regime consistent with experimental measurements. In such states, the entire population of neurons is globally influenced by the external input. We suggest that this network-level enhanced responsiveness constitute a low-level form of sensory awareness.

Motivation & Objective

• To investigate whether asynchronous irregular (AI) neuronal networks display enhanced responsiveness at the network level, similar to single neurons in high-conductance states.
• To determine whether this enhanced responsiveness depends critically on the network's conductance state and membrane potential fluctuations.
• To bridge single-neuron responsiveness mechanisms with network-level dynamics by using biophysically realistic models.
• To test whether AI states in conductance-based networks—matching in vivo measurements—support a form of global, transient network sensitivity to inputs.
• To propose that such network-level responsiveness in AI states may constitute a low-level neural correlate of sensory awareness.

Proposed method

• Developed a biophysically realistic network model using adaptive exponential integrate-and-fire neurons (AdEx) for excitatory (RS) and inhibitory (FS) populations.
• Simulated networks with 8,000 RS and 2,000 FS cells, tuned to reproduce experimentally observed membrane potential fluctuations and conductance levels.
• Used conductance-based synaptic inputs rather than current-based inputs to ensure biophysical realism and test responsiveness under physiological conditions.
• Quantified network responsiveness by measuring the population response to a transient excitatory input (1 nS EPSP on 40 randomly selected cells) across multiple trials.
• Compared responsiveness across different network models: one with aberrant conductance states and one with physiologically plausible conductance distributions.
• Mapped responsiveness as a function of total synaptic conductance to identify the conductance regime where responsiveness peaks, aligned with in vivo measurements.

Experimental results

Research questions

• RQ1 Does the asynchronous irregular (AI) state in recurrent neuronal networks support enhanced responsiveness to external inputs, and if so, under what conditions?
• RQ2 Is network-level responsiveness dependent on the specific conductance state and membrane potential fluctuation regime of individual neurons?
• RQ3 Can biophysically realistic network models reproduce the conductance levels and fluctuations observed in awake cortical neurons in vivo?
• RQ4 How does the responsiveness of a network with aberrant conductance states compare to that of a network with physiologically accurate conductance states?
• RQ5 Does the network-level responsiveness in AI states resemble a low-level form of sensory awareness, given its global, transient, and non-synchronous nature?

Key findings

• Networks with physiologically realistic conductance states—matching in vivo measurements in awake cats—exhibited strong, transient population responses to external inputs, indicating enhanced responsiveness.
• In contrast, networks with aberrant conductance states (e.g., 20× higher than physiological levels) showed no significant response to the same input, highlighting the critical role of conductance level.
• The peak responsiveness occurred within the physiological conductance range (gray area in Fig. 4), with responsiveness dropping sharply outside this range.
• Responsiveness was strongly correlated with the total synaptic conductance and membrane potential fluctuations, and was absent in quiescent or oscillatory states.
• The network responded globally and non-synchronously to inputs, with different neurons firing on different trials, suggesting a distributed, dynamic form of network-level sensitivity.
• The results support the hypothesis that high-conductance states in AI networks may underlie a low-level form of sensory awareness, linking biophysical properties to functional network dynamics.