The Arousal-Regulated Filter: Modulating Feedforward and Recurrent Dynamics for Adaptive Neural Tracking
Classic behavioral studies have identified an inverted-U relationship between arousal and performance, while neurophysiology studies have shown that the arousal-related neuromodulator norepinephrine (NE) increases the signal-to-noise ratio (SNR) of neural responses to extrinsic inputs. More recently, abstract computational models suggest that arousal signals uncertainty in predictive internal models and increases the influence of new observations. Here, I present the arousal-regulated filter (ARF), a novel computational model of neural state estimation designed to bridge abstract algorithms, behavioral findings, and basic neural mechanisms. According to the ARF, arousal selectively amplifies feedforward synapses relative to recurrent synapses, consistent with findings from neurophysiology. Computationally, the ARF integrates predictions of an internal model, implemented in recurrent connections, with extrinsic observations, relayed by feedforward projections, to adaptively track dynamic systems. The ARF resembles a Kalman filter but replaces dynamic updating of the Kalman gain matrix with modulation of a scalar gain parameter influenced by arousal, and incorporates a potentially nonlinear activation function. Computational simulations demonstrate ARFs versatility across binary, multi-unit categorical, and continuous neural network architectures that track diffusion and drift-diffusion processes. When the internal model is aligned with environmental dynamics, arousal exhibits an inverted-U relationship with accuracy due to a bias-variance tradeoff, consistent with behavioral results. However, when the internal model is misaligned with the true dynamics (i.e. the true state is unpredictable), increased arousal monotonically improves accuracy. Optimal arousal levels vary systematically, being lower in noisy sensory contexts and higher in volatile environments or when unmodeled dynamics exist. Thus, the ARF provides a unifying framework that links abstract computational algorithms, behavioral phenomena, and neurophysiological mechanisms. The model also offers potential insights into the computational effects of altered arousal states in mental health conditions, with potential implications for new approaches to assessment and treatment.