Top-Down Modulation on Perceptual Decision with Balanced Inhibition through Feedforward and Feedback Inhibitory Neurons

Abstract

Recent physiological studies have shown that neurons in various regions of the central nervous systems continuously receive noisy excitatory and inhibitory synaptic inputs in a balanced and covaried fashion. While this balanced synaptic input (BSI) is typically described in terms of maintaining the stability of neural circuits, a number of experimental and theoretical studies have suggested that BSI plays a proactive role in brain functions such as top-down modulation for executive control. Two issues have remained unclear in this picture. First, given the noisy nature of neuronal activities in neural circuits, how do the modulatory effects change if the top-down control implements BSI with different ratios between inhibition and excitation? Second, how is a top-down BSI realized via only excitatory long-range projections in the neocortex? To address the first issue, we systematically tested how the inhibition/excitation ratio affects the accuracy and reaction times of a spiking neural circuit model of perceptual decision. We defined an energy function to characterize the network dynamics, and found that different ratios modulate the energy function of the circuit differently and form two distinct functional modes. To address the second issue, we tested BSI with long-distance projection to inhibitory neurons that are either feedforward or feedback, depending on whether these inhibitory neurons do or do not receive inputs from local excitatory cells, respectively. We found that BSI occurs in both cases. Furthermore, when relying on feedback inhibitory neurons, through the recurrent interactions inside the circuit, BSI dynamically and automatically speeds up the decision by gradually reducing its inhibitory component in the course of a trial when a decision process takes too long.

Speed-accuracy tradeoff by a control signal with balanced excitation and inhibition

“…Recent studies suggested that, when considering the biological constraints, the attractor-based circuit models out-perform an integrator when accuracy is more important than speed (Miller and Katz 2013), and that robust and optimal decision-making can be realized by neuronal gain modulation in the attractor network model (Niyogi and Wong-Lin 2013). Furthermore, we have shown that the attractor dynamics and the decision behavior of the spiking neural network model can be rapidly changed by a top-down signal with balanced excitation and inhibition Wang et al 2013). This balanced synaptic input (BSI) has been observed in vivo in various nervous systems, including the frontal cortex (Haider et al 2006;Shu et al 2003), primary visual cortex (Mariño et al 2005) and spinal cord (Berg et al 2007).…”

“…Our decision-making model of spiking neurons was described previously (Hsiao and Lo 2013;Lo and Wang 2006;Wang 2002;Wang et al 2013;Wong et al 2007;Wong and Wang 2006) and has been applied to different types of decision processes (Deco et al 2009;Wang 2008). For the sake of concreteness, here we will focus on model simulations of a visual directiondiscrimination task, the random-dot motion task (Roitman and Shadlen 2002).…”

“…On the other hand, if time is at a premium (e.g., when detecting a predator), we make a quick judgment at the potential cost of accuracy. Speed-accuracy tradeoff (SAT) can result from a trial-by-trial learning process, which is believed to depend on synaptic plasticity and reward information in the cortex and basal ganglia (Balci et al 2010; Gold and Shadlen 2002;Lo and Wang 2006;Wang et al 2013). However, adjustment can often be made quickly, "on the fly," based on either task instruction, a changing environment, or a subject's own will (Edwards 1965;Forstmann et al 2008;Luce 1986;Palmer et al 2005;Wickelgren 1977).…”

Micro-, Meso- and Macro-Connectomics of the Brain

“…Thus, when Cre driver lines became available to separately target gene expression to PV, SST, VIP, or CR neurons (Taniguchi et al 2011), VIP was favored over CR because of the ability to target a population that was separate from PV or SST neurons. Studies of VIP neurons have so far proceeded without concern for the known diversity of VIP cell types, and the grouping of these cells into a monolithic population has appeared to be justified by the striking differences in the connectivity and functional impact of these cells when compared to PV or SST cells (Lee et al 2013;Pfeffer et al 2013). …”

“…Thus, it is apparent that mouse CR bipolar cells preferentially target SST neurons in layer 2/3. In this context, it is not entirely surprising that later studies systematically investigating the connectivity of PV, SST and VIP neurons found strong connections from VIP neurons onto SST neurons in layer 2/3 and not in layer 5 (Lee et al 2013;Pfeffer et al 2013). It remains unclear whether this is a feature of all VIP interneurons or only of the CR-expressing subpopulation.…”