Membrane potential

Recurrent networks with balanced excitation-inhibition explain a wide range of neurophysiological observations, but can only implement a limited set of transformations on their input. On the other hand networks of firing-rate units with low-rank connectivity have universal computational capabilities, but do not work with spikes or generate noise self-consistently. Although empirical approaches to merge these two computational frameworks have been constructed, there is no established theory describing their unification. Here we develop such a theory. We study analytically and numerically networ

Contrastive Hebbian Learning (CHL) has recently emerged as a new biologically plausible paradigm to explain the mechanisms of plasticity in the brain. In classical CHL, developed for artificial networks, neurons learn in a two-step procedure where their free response to a given input is contrasted to their clamped response given the same input and an imposed target output. This two-step procedure, which is unlikely to be implementable in biological systems, has recently been made more plausible by exploiting the fundamental idea that neurons can learn by predicting their own future activity [1

…stochastic fluctuations in membrane potential and synaptic strength $^{4

…recently been engineered to measure membrane potential, neurotransmitter release, pH, or various cellular metabolites, such as ATP or cAMP. Due to the spectral characteristics of these tools, different assemblies of optical hardware are usually needed to reveal the full potential of various biosensors. In a

…from random fluctuations within the membrane potential (V) that leads to random spike times. Prior analysis has shown that the variability of V, in response to stimuli, became quenched, where the variance (σ2) decreased as the mean (μ) increased. However, recent in-vivo primate data has shown that σ2 also can increase with μ. Here we propose a new analytical and computational framework to show that conductance-based integrate-and-fire (C-IF) neurons can accurately capture μ an

…the synaptic input dynamics in their membrane potential. Neurons thereby amplify those synapses that maximally predict o

Dr. Looger will discuss reagents for tracking Ca2+, membrane potential ("voltage"), glutamate, GABA, acetylcholine, serotonin, dopamine, etc. He will also cover optogenetics tools and methods for correlative light/electron microscopy. They make all tools freely available to everyone and work to get them in the hands of people that have limited resources.

…for recording spiking activity and membrane potential from a population of neurons during behavior. We identified putative motor command signals and internal copy signals that encode millisec

A wealth of experimental studies show that the trial-to-trial variability of neuronal activity is quenched during stimulus evoked responses. This fact has helped ground a popular view that the variability of spiking activity can be decomposed into two components. The first is due to irregular spike timing conditioned on the firing rate of a neuron (i.e. a Poisson process), and the second is the trial-to-trial variability of the firing rate itself. Quenching of the variability of the overall response is assumed to be a reflection of a suppression of firing rate variability. Network models have