A decision-making model based on a spiking neural circuit and synaptic plasticity

Abstract

To adapt to the environment and survive, most animals can control their behaviors by making decisions. The process of decision-making and responding according to cues in the environment is stable, sustainable, and learnable. Understanding how behaviors are regulated by neural circuits and the encoding and decoding mechanisms from stimuli to responses are important goals in neuroscience. From results observed in Drosophila experiments, the underlying decision-making process is discussed, and a neural circuit that implements a two-choice decision-making model is proposed to explain and reproduce the observations. Compared with previous two-choice decision making models, our model uses synaptic plasticity to explain changes in decision output given the same environment. Moreover, biological meanings of parameters of our decision-making model are discussed. In this paper, we explain at the micro-level (i.e., neurons and synapses) how observable decision-making behavior at the macro-level is acquired and achieved.

A neural algorithm for Drosophila linear and nonlinear decision-making

“…Previous related studies on the construction of Drosophila decision-making neural circuits can be divided into two categories. Wei et al 7 and 8 mainly focused on the connections and interactions among excitatory and inhibitory spiking neurons during the training phase. The network circuit is shown in Fig.…”

“…Punishment regulates learning by inhibiting input neurons and activating output neurons. Wei et al 8 considered conflicting cues learning compared with 7 , but the network structure of nonlinear decision-making has no change. For conflicting cues learning, the input cues are encoded by a linear or nonlinear probability form, and then transmit to the decision-making network.…”

“…For conflicting cues learning experiments, we do a deep comparison and analysis of our proposed model and the existing models (including 8 – 10 ). To make sure that the comparison is feasible, we unify the curve of nonlinear decision-making results of different models in the same dimension space and mainly observe the change trend of PI.…”

“…Nevertheless, the network architecture and learning process need to be refined to capture important biological details and it could not solve the conflicting cues learning. Based on the decision-making neural circuit and learning process in 7 , Wei et al 8 further added conflicting cues learning during the choice phase. With the same training network, different input cues were entered into a certain decision-making unit with a probability determined by color-shape selection ability (linear or sigmoid selection ability) and current color intensity, then the decision-making unit triggered an action according to the learned stimulus-action association.…”

Memory Based Decision Making: A Spiking Neural Circuit Model

“…Brain decision making is one of the most important neural activities in human beings. Automatic response and conscious decision making are two important representations of brain decision-making [1,2]. Automatic response is a rapid decisionmaking process, such as touching a hot object and retracting your hand quickly.…”