The brain can display self-sustained activity (SSA), which is the persistent firing of neurons in the absence of external stimuli . This spontaneous activity shows low neuronal firing rates and is observed in diverse in vitro and in vivo situations . In this work, we study the influence of excitatory/inhibitory balance, connection density, and network size on the self-sustained activity of a neuronal network model. We build a random network of adaptive exponential integrate-and-fire (AdEx) neuron models connected through inhibitory and excitatory chemical synapses. The AdEx model mimics several behaviours of biological neurons, such as spike initiation, adaptation, and bursting patterns. In an excitation/inhibition balanced state, if the mean connection degree (K) is fixed, the firing rate does not depend on the network size (N) , whereas for fixed N, the firing rate decreases when K increases. However, for large K, SSA states can appear only for large N. We show the existence of SSA states with similar behaviours to those observed in experimental recordings, such as very low and irregular neuronal firing rates, and spike-train power spectra with slow fluctuations, only for balanced networks of large size .
Self-sustained activity in the brain is observed in the absence of external stimuli and contributes to signal propagation, neural coding, and dynamic stability. It also plays an important role in cognitive processes . In this work, by means of studying intracellular recordings from CA1 neurons in rats and results from numerical simulations, we demonstrate that self-sustained activity presents high variability of patterns, such as low neural firing rates and activity in the form of small-bursts in distinct neurons. In our numerical simulations, we consider random networks composed of coupled, adaptive exponential integrate-and-fire neurons. The neural dynamics in the random networks simulate regular spiking (excitatory) and fast-spiking (inhibitory) neurons. We show that both the connection probability and network size are fundamental properties that give rise to self-sustained activity in qualitative agreement with our experimental results. Finally, we provide a more detailed description of the self-sustained activity in terms of lifetime distributions, synaptic conductances, and synaptic currents .
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The brain can display self-sustained activity (SSA), which is the persistent firing of neurons
Self-sustained activity in the brain is observed
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. This spontaneous activity shows low neuronal firing rates and is observed in diverse in vitro and in vivo situations
and contributes to signal propagation, neural coding, and dynamic stability. It also plays an important role in cognitive processes
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we study the influence of excitatory/inhibitory balance, connection density, and network size on the self-sustained activity of a neuronal network model. We build a random network of
by means of studying intracellular recordings from CA1 neurons in rats and results from numerical simulations, we demonstrate that self-sustained activity presents high variability of patterns, such as low neural firing rates and activity in the form of small-bursts in distinct neurons. In our numerical simulations, we consider random networks composed of coupled,
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(AdEx) neuron models connected through inhibitory and excitatory chemical synapses. The AdEx model mimics several behaviours of biological neurons, such as spike initiation, adaptation, and bursting patterns. In an excitation/inhibition balanced state, if the mean connection degree (K) is fixed, the firing rate does not depend on the network size (N) , whereas for fixed N, the firing rate decreases when K increases. However, for large K, SSA states can appear only for large N. We show the existence of SSA states with similar behaviours to those observed in experimental recordings, such as very low and irregular neuronal firing rates,
neurons. The neural dynamics in the random networks simulate regular spiking (excitatory)
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spike-train power spectra with slow fluctuations, only for balanced networks of large size
fast-spiking (inhibitory) neurons. We show that both the connection probability and network size are fundamental properties that give rise to self-sustained activity in qualitative agreement with our experimental results. Finally, we provide a more detailed description of the self-sustained activity in terms of lifetime distributions, synaptic conductances, and synaptic currents