Training deep neural density estimators to identify mechanistic models of neural dynamics
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of
underlying causes. However, determining which model parameters agree with complex and …
underlying causes. However, determining which model parameters agree with complex and …
Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey
Objective. Spike sorting is a set of techniques used to analyze extracellular neural
recordings, attributing individual spikes to individual neurons. This field has gained …
recordings, attributing individual spikes to individual neurons. This field has gained …
Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task
Primates can richly parse sensory inputs to infer latent information. This ability is
hypothesized to rely on establishing mental models of the external world and running mental …
hypothesized to rely on establishing mental models of the external world and running mental …
Estimating transfer entropy in continuous time between neural spike trains or other event-based data
Transfer entropy (TE) is a widely used measure of directed information flows in a number of
domains including neuroscience. Many real-world time series for which we are interested in …
domains including neuroscience. Many real-world time series for which we are interested in …
Interrogating theoretical models of neural computation with emergent property inference
A cornerstone of theoretical neuroscience is the circuit model: a system of equations that
captures a hypothesized neural mechanism. Such models are valuable when they give rise …
captures a hypothesized neural mechanism. Such models are valuable when they give rise …
Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories
Many complex systems operating far from the equilibrium exhibit stochastic dynamics that
can be described by a Langevin equation. Inferring Langevin equations from data can …
can be described by a Langevin equation. Inferring Langevin equations from data can …
Deep inverse modeling reveals dynamic-dependent invariances in neural circuit mechanisms
Neural population dynamics are shaped by many cellular, synaptic, and network properties.
Not only is it important to understand how coordinated changes in circuit parameters alter …
Not only is it important to understand how coordinated changes in circuit parameters alter …
Circumstantial evidence and explanatory models for synapses in large-scale spike recordings
IH Stevenson - arxiv preprint arxiv:2304.09699, 2023 - arxiv.org
Whether, when, and how causal interactions between neurons can be meaningfully studied
from observations of neural activity alone are vital questions in neural data analysis. Here …
from observations of neural activity alone are vital questions in neural data analysis. Here …
Resting-state neural firing rate is linked to cardiac-cycle duration in the human cingulate and parahippocampal cortices
Stimulation and functional imaging studies have revealed the existence of a large network of
cortical regions involved in the regulation of heart rate. However, very little is known about …
cortical regions involved in the regulation of heart rate. However, very little is known about …
Model-based detection of putative synaptic connections from spike recordings with latency and type constraints
Detecting synaptic connections using large-scale extracellular spike recordings presents a
statistical challenge. Although previous methods often treat the detection of each putative …
statistical challenge. Although previous methods often treat the detection of each putative …