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eLife Inhibitory control of shared variability in cortical networks (in press, preprint available) Neuron Learning enhances sensory and multiple non-sensory representations in primary visual cortex Pachitariu, Lyamzin, Sahani and Lesica J Neuroscience State-dependent population coding in primary auditory cortex 2013 Pachitariu, Petreska and Sahani NIPS(More)
Sensory function is mediated by interactions between external stimuli and intrinsic cortical dynamics that are evident in the modulation of evoked responses by cortical state. A number of recent studies across different modalities have demonstrated that the patterns of activity in neuronal populations can vary strongly between synchronized and(More)
Neural language models (LMs) based on recurrent neural networks (RNN) are some of the most successful word and character-level LMs. Why do they work so well, in particular better than linear neural LMs? Possible explanations are that RNNs have an implicitly better regularization or that RNNs have a higher capacity for storing patterns due to their(More)
We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in(More)
The combination of two-photon microscopy recordings and powerful calcium-dependent fluorescent sensors enables simultaneous recording of unprecedentedly large populations of neurons. While these sensors have matured over several generations of development, computational methods to process their fluorescence remain inefficient and the results hard to(More)
We present a dynamic nonlinear generative model for visual motion based on a latent representation of binary-gated Gaussian variables. Trained on sequences of images, the model learns to represent different movement directions in different variables. We use an online approximate inference scheme that can be mapped to the dynamics of networks of neurons.(More)
Advances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Here we introduce Kilosort, a spike sorting framework that meets(More)
Locomotion profoundly changes sensory responses in mouse primary visual cortex. These changes are thought to arise from a disinhibitory circuit, whereby interneurons expressing vasoactive intestinal peptide (Vip) inhibit those expressing somatostatin (Sst), thus disinhibiting pyramidal cells (Pyr). We studied the effect of locomotion on these cell types and(More)
Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to(More)
Population neural recordings with long-range temporal structure are often best understood in terms of a common underlying low-dimensional dynamical process. Advances in recording technology provide access to an ever-larger fraction of the population, but the standard computational approaches available to identify the collective dynamics scale poorly with(More)