Topic: neural population dynamics

Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon calcium imaging enables the measurement of ongoing and induced activity across the neural population. Despite the enormous space of potential photostimulation patterns and the time-consuming nature of photostimulation experiments, very little algorithmic work has been done to dete

An open question in neuroscience is how the brain controls naturally generated movements. An increasing number of studies have shown that high-dimensional neural population dynamics lie within low-dimensional manifolds, which may be integral to neural control of behavior. However, many of these studies have focused on neural activity recorded during experimentally driven movements with little variability, leaving open the question of whether low-dimensional neural manifolds persist during naturalistic movements. In this paper, we investigate neural population dynamics in naturalistic reaching

According to the theory of predictive processing, expectations modulate neural activity so as to optimize the processing of sensory inputs expected in the current environment. While there is accumulating evidence that the brain indeed operates under this principle, most of the attention has been placed on mechanisms that rely on static coding properties of neurons. The potential contribution of dynamical features, such as those reflected in the evolution of neural population dynamics, has thus far been overlooked. In this talk, I will present evidence for a novel mechanism for predictive proce

Neural population dynamics are jointly shaped by numerous factors at the cellular, synaptic, and network levels. Mechanistic models, especially spiking neural networks, are crucial for understanding how these interacting variables influence neural activity. However, the process of adjusting model parameters to match experimental recordings, i.e., inverse modeling, is challenging, and often requires a trade-off between model complexity and tractability. Consequently, models often use fixed or manually tuned parameters, limiting their dynamic range and explanatory power while making cross-study

Artificial recurrent neural networks (RNNs) have become a customary research tool in theoretical neuroscience, being used as models of biological neural networks performing cognitive tasks. While RNNs are not generally intended as faithful, biologically realistic models of neuronal activity, there is a broad expectation that RNN dynamics may provide good models at the level of neural population dynamics. According to several authors, in both cases population activity should organize into low-dimensional manifolds encoding the relevant task variables (1). However, strong tests of this hypothesi

We adapt nonlinear optimal control theory (OCT) to control oscillations and network synchrony and apply it to models of neural population dynamics. Conventionally, OCT requires a target trajectory. This requirement may be overly restrictive for oscillatory targets since the exact trajectory shape including amplitude and phase might not be relevant. To overcome this limitation, we introduce three alternative cost functionals to target oscillations and synchrony without specifying a reference trajectory. A controlled nonlinear dynamical system with state $x$ and control $u$ is defined by $$\dot