Reconstructing computational system dynamics from neural data with recurrent neural networks
Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …
equations. The behaviour of such systems is the subject of dynamical systems theory …
Modeling the correlated activity of neural populations: A review
The principles of neural encoding and computations are inherently collective and usually
involve large populations of interacting neurons with highly correlated activities. While …
involve large populations of interacting neurons with highly correlated activities. While …
Coexistence of negative and positive electrocaloric effect in lead-free 0.9 (K0. 5Na0. 5) NbO3-0.1 SrTiO3 nanocrystalline ceramics
The coexistence of negative and positive electrocaloric effect have been experimentally
demonstrated in lead-free 0.9 (K 0.5 Na 0.5) NbO 3-0.1 SrTiO 3 nanocrystalline ceramics …
demonstrated in lead-free 0.9 (K 0.5 Na 0.5) NbO 3-0.1 SrTiO 3 nanocrystalline ceramics …
A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements
D Durstewitz - PLoS computational biology, 2017 - journals.plos.org
The computational and cognitive properties of neural systems are often thought to be
implemented in terms of their (stochastic) network dynamics. Hence, recovering the system …
implemented in terms of their (stochastic) network dynamics. Hence, recovering the system …
Can serial dependencies in choices and neural activity explain choice probabilities?
JM Lueckmann, JH Macke… - Journal of Neuroscience, 2018 - Soc Neuroscience
During perceptual decisions the activity of sensory neurons covaries with choice, a
covariation often quantified as “choice-probability”. Moreover, choices are influenced by a …
covariation often quantified as “choice-probability”. Moreover, choices are influenced by a …
Advanced data analysis in neuroscience
D Durstewitz - Bernstein Series in Computational Neuroscience …, 2017 - Springer
Bernstein Series in Computational Neuroscience reflects the Bernstein Network's broad
research and teaching activities, including models of neural circuits and higher brain …
research and teaching activities, including models of neural circuits and higher brain …
Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural Networks
Mechanistic and computational models in neuroscience usually take the form of systems of
differential or time-recursive equations. The spatio-temporal behavior of such systems is the …
differential or time-recursive equations. The spatio-temporal behavior of such systems is the …
Developing a nonstationary computational framework with application to modeling dynamic modulations in neural spiking responses
Objective: This paper aims to develop a computational model that incorporates the
functional effects of modulatory covariates (such as context, task, or behavior), which …
functional effects of modulatory covariates (such as context, task, or behavior), which …
SIMPL: Scalable and hassle-free optimization of neural representations from behaviour
High-dimensional neural activity in the brain is known to encode low-dimensional, time-
evolving, behaviour-related variables. A fundamental goal of neural data analysis consists of …
evolving, behaviour-related variables. A fundamental goal of neural data analysis consists of …
Characterizing retinal ganglion cell responses to electrical stimulation using generalized linear models
The ability to preferentially stimulate different retinal pathways is an important area of
research for improving visual prosthetics. Recent work has shown that different classes of …
research for improving visual prosthetics. Recent work has shown that different classes of …