Guclu, U. | Search bioRxiv

bioRxiv2018

Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity

Estimating causal connectivity between spiking neurons from measured spike sequences is one of the main challenges of systems neuroscience. In this paper we introduce two nonparametric Bayesian methods for spike-membrane and spikespike causal connectivity based on Gaussian process regression. For spike-spike connectivity, we derive a new semi-analytic variational approximation of the response functions of a non-linear dynamical model of interconnected neurons. This semi-analytic method exploits the tractability of GP regression when the membrane potential is observed. The resulting posterior is then marginalized analytically in order to obtain the posterior of the response functions given the spike sequences alone. We validate our methods on both simulated data and real neuronal recordings.

neuroscience↗

bioRxiv2017

CNN-based Encoding and Decoding of Visual Object Recognition in Space and Time

Deep convolutional neural networks (CNNs) have been put forward as neurobiologically plausible models of the visual hierarchy. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively. Here, we addressed this question by combining CNN-based encoding models with magnetoencephalography (MEG). Human participants passively viewed 1000 images of objects while MEG signals were acquired. We modelled their high temporal resolution source-reconstructed cortical activity with CNNs, and observed a feedforward sweep across the visual hierarchy between 75-200 ms after stimulus onset. This spatiotemporal cascade was captured by the network layer representations, where the increasingly abstract stimulus representation in the hierarchical network model was reflected in different parts of the visual cortex, following the visual ventral stream. We further validated the accuracy of our encoding model by decoding stimulus identity in a left-out validation set of viewed objects, achieving state-of-the-art decoding accuracy.

neuroscience↗