|
|
@@ -1,2 +1,30 @@
|
|
|
-# Spectral-Signatures-of-Perceptual-Categorization-in-Human-Cortex
|
|
|
+Activations of Deep Convolutional Neural Network are Aligned with Gamma Band Activity of Human Visual Cortex
|
|
|
+============================================================================================================
|
|
|
+Research article by Ilya Kuzovkin, Raul Vicente, Mathilde Petton, Jean-Philippe Lachaux, Monica Baciu, Philippe Kahane, Sylvain Rheims, Juan R. Vidal and Jaan Aru.
|
|
|
+https://www.biorxiv.org/content/10.1101/483487v1
|
|
|
+http://neuro.cs.ut.ee
|
|
|
|
|
|
+
|
|
|
+Abstract
|
|
|
+--------
|
|
|
+Human brain has developed mechanisms to efficiently decode sensory information according to perceptual categories of high prevalence in the environment, such as faces, symbols, objects. Neural activity produced within localized brain networks has been associated with the process that integrates both sensory bottom-up and cognitive top-down information processing. Yet, how specifically the different types and components of neural responses reflect the local networks selectivity for categorical information processing is still unknown. By mimicking the decoding of the sensory information with machine learning we can obtain accurate artificial decoding models. Having the artificial system functionally on par with the biological one we can analyze the mechanics of the artificial system to gain insights into the inner workings of its biological counterpart. In this work we train a Random Forest classification model to decode eight perceptual categories from visual stimuli given a broad spectrum of human intracranial signals (4 – 150 Hz) obtained during a visual perception task, and analyze which of the spectral features the algorithm deemed relevant to the perceptual decoding. We show that network selectivity for a single or multiple categories in sensory and non-sensory cortices is related to specific patterns of power increases and decreases in both low (4 – 50 Hz) and high (50 – 150 Hz) frequency bands. We demonstrate that the locations and patterns of activity that are identified by the algorithm not only coincide with the known spectro-spatial signatures, but extend our knowledge by uncovering additional spectral signatures describing neural mechanisms of visual category perception in human brain.
|
|
|
+
|
|
|
+
|
|
|
+The Code
|
|
|
+--------
|
|
|
+The code is available under MIT license from https://github.com/kuz/Spectral-signatures-of-perceptual-categorization-in-human-cortex
|
|
|
+
|
|
|
+
|
|
|
+The Data
|
|
|
+--------
|
|
|
+Our data consists of two big chunks: activations and derivatives from human brain responses and activations and derivatives from DCNN responses. We made most of it publicly available with exception, due to the restrictions imposed by the third party, to the raw LFP responses from the implanted electrodes. But already from the the very next step of analysis -- spectral decomposition of the LFPs -- the data is available:
|
|
|
+
|
|
|
+#### Images
|
|
|
+Full set of 419 stimuli presented to the subjects: [images.zip | 3.4 Mb](http://neuro.cs.ut.ee/downloads/intracranial-dcnn/stimuli/images.zip)
|
|
|
+
|
|
|
+#### Human Responses to the Images
|
|
|
+Human brain responses to those images from 11,293 electrodes across 100 subjects were recorded, resulting in 2,823,250 LFP recordings: [not available publicly]
|
|
|
+Recordings were preprocessed using detrending, artifact rejection, bipolar rereferencing and dropping non-responposive electrodes: [not available publicly]
|
|
|
+
|
|
|
+###### Spectrotemporal representation of LFP responses
|
|
|
+The final processed dataset can be downloaded here. The full size is 237 Gb.
|
