Topic: spike rates

Research in the brain and cognitive sciences attempts to uncover the neural mechanisms underlying intelligent behavior in domains such as vision. Due to the complexities of brain processing, studies necessarily had to start with a narrow scope of experimental investigation and computational modeling. I argue that it is time for our field to take the next step: build system models that capture a range of visual intelligence behaviors along with the underlying neural mechanisms. To make progress on system models, we propose integrative benchmarking – integrating experimental results from many la

Neural activity in response to sensory stimuli leads to a reorganization of neuronal ensemble dynamics, which can be captured by lower-dimensional latent dynamics. Recurrent Neural Networks (RNN) have been proven successful at extracting such latent representations of neural dynamics. Here, we applied an RNN to the data available in the Steinmetz Neuropixels dataset, containing electrophysiological and behavioral data from mice engaged in a visual go/no go contrast detection task. First, we chose as our input (‘seed’) region a subset of spike rates recorded from mouse primary visual cortex (VI

Drift-diffusion models have been widely used to study the neural mechanisms of decisions guided by noisy stimuli. In this framework, momentary evidence is integrated through a scalar decision variable, which has been found to correlate with spike rates in many brain areas. However, the relationship between the decision variable (i.e., accumulated evidence) and spike rate is widely assumed to be fixed over the course of a single trial, even though sensory and motor weights on spiking activity vary over time. Allowing the weight of the decision variable to be temporally inhomogeneous may capture

Information in the nervous system is encoded by the spiking patterns of large populations of neurons. The analysis of such high-dimensional data is typically restricted to simple, arbitrarily defined features like spike rates, which discards information in the temporal structure of spike trains. Here, we use a recently developed method called SpikeShip based on optimal transport theory, which captures information from all the relative spike-timing relations among neurons. We compared spike-rate and spike-timing codes in neural ensembles from six visual areas during natural video presentations.