Biology subjects

Ringach, D.

Publications and source records attributed to Ringach, D..

3 recordsLinked to original sources

bioRxiv2018

On the Subspace Invariance of Population Responses

The response of a neural population in cat primary visual cortex to the linear combination of two sinusoidal gratings (a plaid) can be well approximated by a weighted sum of the population responses to the individual gratings - a property we refer to as subspace invariance. We tested subspace invariance in mouse primary visual cortex by measuring the angle between the population response to a plaid and the plane spanned by the population responses to its individual components. We found robust violations of subspace invariance, represented by a median angular deviation of ~55 deg. The cause of this departure is a strong, negative correlation between the mean responses a neuron to the individual gratings and its response to the plaid. We suggest that an early nonlinearity may distort the power distribution of grating and plaid stimuli such that plaids have a prominent power component at {+/-}45 deg off the fundamental orientations. We conclude that subspace invariance does not hold in mouse V1. This finding rules out a large class of possible models of population coding, including vector averaging and gain control.

neuroscience↗

bioRxiv2018

Active Learning of Cortical Connectivity from Two-Photon Imaging Data

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network.. Unlike existing system identification techniques, this \"active learning\" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

neuroscience↗

bioRxiv2017

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

neuroscience↗