Abstract
Rhythmic activity of the brain often depends on synchronized spiking of interneuronal networks interacting with principal neurons. The quest for physiological mechanisms regulating network synchronization has therefore been firmly focused on synaptic circuits. However, it has recently emerged that synaptic efficacy could be influenced by astrocytes that release signalling molecules into their macroscopic vicinity. To understand how this volume-limited synaptic regulation can affect oscillations in neural populations, here we explore an established artificial neural network mimicking hippocampal basket cells receiving inputs from pyramidal cells. We find that network oscillation frequencies and average cell firing rates are resilient to changes in excitatory input even when such changes occur in a significant proportion of participating interneurons, be they randomly distributed or clustered in space. The astroglia-like, volume-limited regulation of excitatory synaptic input appears to better preserve network synchronization (compared with a similar action evenly spread across the network) while leading to a structural segmentation of the network into cell subgroups with distinct firing patterns. These observations provide us with some previously unknown insights into the basic principles of neural network control by astroglia.
Highlights
Synchronized oscillations in neural circuits could provide, at least theoretically, a universal device for pattern recognition and information handling in the brain [1,2]. Such periodic activities are in many cases driven by selfsustained rhythms generated by interconnected populations of inhibitory gamma-aminobutyric acid (GABA)ergic interneurons, which receive multiple excitatory inputs from principal cells [3,4,5]
The neural network design was generally based on the well-tested hippocampal basket cell (BC) network incorporating excitatory inputs from principal neurons [34]
In our simulations, we have found that excitatory postsynaptic currents (EPSCs) generated at individual interneurons at, on average, approximately 300 Hz lead to the network oscillation and synchronization behaviours that were fully compatible with those under ‘baseline’ tonic current conditions used in the classical BC network models [31,34]
Summary
Synchronized oscillations in neural circuits could provide, at least theoretically, a universal device for pattern recognition and information handling in the brain [1,2]. The neural network design was generally based on the well-tested hippocampal basket cell (BC) network incorporating excitatory inputs from principal neurons ( pyramidal cells; figure 1a depicts the characteristic BC arrangement in hippocampal area CA1) [34] This cell population has classically been associated with experimentally documented high-frequency oscillations of local field potential (and the corresponding synchronizations of cell spiking) that appear to be related causally to certain behaviour traits or memory-forming tasks [31,38,39,40]. To improve physiological compatibility of the model, the present simulated network has incorporated realistic barrages of excitatory postsynaptic currents (EPSCs; NEURON Exp2Syn function: t1 1⁄4 0.5 ms rise time and t2 1⁄4 5 ms decay time, respectively) generated in individual BCs by synaptic inputs from simulated networks of pyramidal cells (figure 1b), as described recently [36]. The mean coefficient of synchronization k(t) was calculated as the global average of kij(t) for all neuronal pairs
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