Neural dynamics in the orbitofrontal cortex reveal cognitive strategies.

Behavior is sloppy: a multitude of cognitive strategies can produce similar behavioral read-outs. An underutilized approach is to combine multifaceted behavioral analyses with neural recordings to resolve cognitive strategies. Here we show that rats performing a decision-making task exhibit distinct strategies over training, and these cognitive strategies are decipherable from orbitofrontal cortex (OFC) neural dynamics.

Compositional pretraining improves computational efficiency and matches animal behavior on complex tasks.

1Recurrent neural networks (RNN) are ubiquitously used in neuroscience to capture both neural dynamics and behaviors of living systems. However, when it comes to complex cognitive tasks, training RNNs with traditional methods can prove difficult and fall short of capturing crucial aspects of animal behavior. Here we propose a principled approach for identifying and incorporating compositional tasks as part of RNN training.

Transformation of acoustic information to sensory decision variables in the parietal cortex.

The process by which sensory evidence contributes to perceptual choices requires an understanding of its transformation into decision variables. Here, we address this issue by evaluating the neural representation of acoustic information in the auditory cortex-recipient parietal cortex, while gerbils either performed a two-alternative forced-choice auditory discrimination task or while they passively listened to identical acoustic stimuli. During task engagement, stimulus identity decoding performance from simultaneously recorded parietal neurons significantly correlated with psychometric sensitivity.

Subpopulations of neurons in lOFC encode previous and current rewards at time of choice.

Studies of neural dynamics in lateral orbitofrontal cortex (lOFC) have shown that subsets of neurons that encode distinct aspects of behavior, such as value, may project to common downstream targets. However, it is unclear whether reward history, which may subserve lOFC's well-documented role in learning, is represented by functional subpopulations in lOFC. Previously, we analyzed neural recordings from rats performing a value-based decision-making task, and we documented trial-by-trial learning that required lOFC (Constantinople et al.

Myopic control of neural dynamics.

Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment "input" to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely.

Shannon Entropy Based Time-Dependent Deterministic Sampling for Efficient "On-the-Fly" Quantum Dynamics and Electronic Structure.

A new set of time-dependent deterministic sampling (TDDS) measures, based on local Shannon entropy, are presented to adaptively gauge the importance of various regions on a potential energy surface and to be employed in "on-the-fly" quantum dynamics. Shannon sampling and Shannon entropy are known constructs that have been used to analyze the information content in functions: for example, time-series data and discrete data sets such as amino acid sequences in a protein structure. Here the Shannon entropy, when combined with dynamical parameters such as the instantaneous potential, gradient and wavepacket density provides a reliable probe on active regions of a quantum mechanical potential surface.