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Review
. 2018 Feb 2:12:46.
doi: 10.3389/fnins.2018.00046. eCollection 2018.

Synaptic E-I Balance Underlies Efficient Neural Coding

Shanglin Zhou  1 , Yuguo Yu  1
Affiliations
Review

Synaptic E-I Balance Underlies Efficient Neural Coding

Shanglin Zhou et al. Front Neurosci. .

Abstract

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Keywords: energy efficiency; excitatory-inhibitory balance; information propagation; sparse coding; stimulus representation.

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Figures

Figure 1
Figure 1
Experimental evidence of the E/I balance. (A) Average currents during the up state in recordings clamped at different membrane potentials from in vitro brain slices (top, red, and blule curves showing the average currents, the green curves showing the raw traces at +30 mV), the reversal potential of the average synaptic currents (middle), and additional conductances during the up state (bottom). Adapted from Shu et al. (2003). (B) Simultaneous in vivo recordings from two cortical cells. One cell (red) was continuously recorded in a hyperpolarized mode, and the other cell (blue) was switched between depolarized and hyperpolarized modes (current depicted below the traces). Dashed lines mark the onset of synaptic events. Insets show examples of two events (marked by asterisks). Adapted with permission from Okun and Lampl (2008). (C) Recordings in humans during awake (left), slow-wave sleep (SWS) (middle), and rapid-eye movement (REM) (right) states. Top row shows 60-s windows; bottom row shows a 10-s window of the same state. Putative inhibitory neurons (FS cells) are shown in red. Putative excitatory neurons (RS) are depicted in blue. At the top of each panel, a sample LFP trace (in blue) accompanies the spiking activity. Histograms show the overall activity of the RS (blue) and FS (red) cells. Adapted with permission from Dehghani et al. (2016).
Figure 2
Figure 2
The correlation of firing sparseness and mitral cell spiking in a large-scale olfactory bulb model. (A) Schematic representation of balanced and unbalanced excitation and inhibition in the MC–GC circuit. Three activated middle MCs (solid black triangles) receive strong input from glomeruli (solid deep green color); through back-propagation of APs in their lateral dendrites, they distribute the excitation (red) through reciprocal synapses, activating lateral inhibition in the surrounding MCs through the reciprocal inhibitory synapses. This mode of excitation and inhibition is balanced, and these MCs are called MC type I. The activated GCs (small blue spheres) deliver lateral inhibition to other surrounding MCs with weak or no excitatory inputs, making their reciprocal synapses unbalanced. These MCs are called MC type II. MCs that do not receive lateral inhibition are MC type III. (B) The MC network sparseness level as a function of reciprocal inhibitory weight to excitation weight ratio ginh/gex for the cases of different ginhMax with a fixed gexMax = 0.5 nS and different gexMax with a fixed ginhMax = 0.3 nS. (C) Same in B but shows the mitral cell spiking correlation. Adapted from Yu et al. (2014).
Figure 3
Figure 3
Propagation of firing rate in a multilayer feedforward network. (A) Average firing rate of different layers in the precisely balanced network. (B) Average firing rate of different layers in the feedforward network with small deviations from the precise balance. (C) Raster plot showing the firing pattern of excitatory neurons in different layers in a feedforward network with balanced firing rates between excitation and inhibition. Adapted with permission from Litvak et al. (2003).
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