A simple neural circuit model explains diverse types of integration kernels in perceptual decision-making

The ability to accumulate evidence over time for deliberate decision is essential for both humans and animals. Decades of decision-making research have documented various types of integration kernels that characterize how evidence is temporally weighted. While numerous normative models have been proposed to explain these kernels, there remains a gap in circuit models that account for the complexity and heterogeneity of single neuron activities. In this study, we sought to address this gap by using low-rank neural network modeling in the context of a perceptual decision-making task. Firstly, we demonstrated that even a simple rank-one neural network model yields diverse types of integration kernels observed in human data--including primacy, recency, and non-monotonic kernels--with a performance comparable to state-of-the-art normative models such as the drift diffusion model and the divisive normalization model. Moreover, going beyond the previous normative models, this model enabled us to gain insights at two levels. At the collective level, we derived a novel explicit mechanistic expression that explains how these kernels emerge from a neural circuit. At the single neuron level, this model exhibited heterogenous single neuron response kernels, resembling the diversity observed in neurophysiological recordings. In sum, we present a simple rank-one neural circuit that reproduces diverse types of integration kernels at the collective level while simultaneously capturing complexity of single neuron responses observed experimentally. Author SummaryThis study introduces a simple rank-one neural network model that replicates diverse integration kernels--such as primacy and recency--observed in human decision-making tasks. The model performs comparably to normative models like the drift diffusion model but offers novel insights by linking neural circuit dynamics to these kernels. Additionally, it captures the heterogeneity of single neuron responses, resembling diversity observed in experimental data. This work bridges the gap between decision-making models and the complexity of neural activity, offering a new perspective on how evidence is integrated in the brain.