Perception involves two types of decisions about the sensory world: identification of stimulus features as analog quantities, or discrimination of the same stimulus features among a set of discrete alternatives. Veridical judgment and categorical discrimination have traditionally been conceptualized as two distinct computational problems. Here, we found that these two types of decision making can be subserved by a shared cortical circuit mechanism. We used a continuous recurrent network model to simulate two monkey experiments in which subjects were required to make either a two-alternative forced choice or a veridical judgment about the direction of random-dot motion. The model network is endowed with a continuum of bell-shaped population activity patterns, each representing a possible motion direction. Slow recurrent excitation underlies accumulation of sensory evidence, and its interplay with strong recurrent inhibition leads to decision behaviors. The model reproduced the monkey's performance as well as single-neuron activity in the categorical discrimination task. Furthermore, we examined how direction identification is determined by a combination of sensory stimulation and microstimulation. Using a population-vector measure, we found that direction judgments instantiate winner-take-all (with the population vector coinciding with either the coherent motion direction or the electrically elicited motion direction) when two stimuli are far apart, or vector averaging (with the population vector falling between the two directions) when two stimuli are close to each other. Interestingly, for a broad range of intermediate angular distances between the two stimuli, the network displays a mixed strategy in the sense that direction estimates are stochastically produced by winner-take-all on some trials and by vector averaging on the other trials, a model prediction that is experimentally testable. This work thus lends support to a common neurodynamic framework for both veridical judgment and categorical discrimination in perceptual decision making.

Propagation of the firing rate and synchronous firings in a 10-layer feed-forward neuronal network are studied. When neurons in layer 1 are subject to white noise, synchrony can be built up in deep layers and the firing rate can be propagated. A network with 6 layers is found to be enough for such behavior. A periodic signal with frequencies of 30-80Hz can be selectively transmitted through the network. These abilities in information processing due to synchrony can be modulated by noise and the operating mode of neurons, and our results are relevant to experimental findings.

Cellular memory is a ubiquitous phenomenon in cell biology. Using numerical simulation and theoretical analysis, we explored the robustness of cellular memory to intrinsic noise in a transcriptional positive feedback system. Without noise, the system could create two stable steady states and function as a memory module. Memory robustness index and mean first-passage time were used to quantify the robustness of memory. Large cell size and strong cooperativity in binding enhanced memory storage remarkably. Adding a second positive feedback loop improved persistent memory significantly, whereas including a negative one destabilized memory storage. These are consistent with experimental observations. We interpret why positive feedback loops are actively involved in epigenetic memory from a dynamical systems perspective.

Positive and negative feedback loops are often coupled to perform various functions in gene regulatory networks, acting as bistable switches, oscillators, and excitable devices. It is implied that such a system with interlinked positive and negative feedback loops is a flexible motif that can modulate itself among various functions. Here, we developed a minimal model for the system and systematically explored its dynamics and performance advantage in response to stimuli in a unifying framework. The system indeed displays diverse behaviors when the strength of feedback loops is changed. First, the system can be tunable from monostability to bistability by increasing the strength of positive feedback, and the bistability regime is modulated by the strength of negative feedback. Second, the system undergoes transitions from bistability to excitability and to oscillation with increasing the strength of negative feedback, and the reverse conversion occurs by enhancing the strength of positive feedback. Third, the system is more flexible than a single feedback loop; it can produce robust larger-amplitude oscillations over a wider stimulus regime compared with a single time-delayed negative feedback loop. Furthermore, the tunability of the system depends mainly on the topology of coupled feedback loops but less on the exact parameter values or the mode of interactions between model components. Thus, our results interpret why such a system represents a tunable motif and can accomplish various functions. These also suggest that coupled feedback loops can act as toolboxes for engineering diverse functional circuits in synthetic biology.

Interlinked positive feedback loops are frequently found in biological signaling pathways. It is intriguing to study the dynamics, functions, and robustness of these motifs. Using numerical simulations and theoretical analysis, here we explore the sensitiveness and robustness of positive feedback loops with various time scales. Both single and dual loops can behave as a bistable switch. We study the responses of five types of bistable switches to noisy stimuli. The noise-induced transitions between two states are discussed in detail by using energy landscape. The dual-time switch, consisting of interconnected fast and slow loops, is both sensitive to stimuli and resistant to fluctuations in stimulus. This provides a novel mechanism for creating optimal bistable switches and memory modules. Our results also suggest that the dual-time switch can act as a ubiquitous motif with sensitive robustness in biological systems.