Evaluation code used in the brain parcellation survey "Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex".

Some of the most successful deep learning methods involve artificial neural networks. Artificial neural networks are inspired by the 1959 biological model proposed by Nobel laureates David H. Hubel & Torsten Wiesel, who found two types of cells in the primary visual cortex: simple cells and complex cells. Deep learning (deep structured learning or hierarchical learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. cs231n.github.io, theano tutorial and ufldl.stanford.edu has a reference.

This repository contains the easiest method of installation, configuration and integration of TheHive and Cortex.

As observed machine learning, computer vision techniques and other computer science algorithms cannot compete the human level of intelligence in pattern recognition such as hand written digits and traffic signs. But here we have reviewed a biologically plausible deep neural network architecture which can make it possible using a fully parameterizable GPU implementation deep neural network independent of the pre-wired feature extractors designing, which are rather learned in a supervised way. In this method tiny fields of winner neurons gives sparsely connected neural layers which leads to huge network depth as found in human like species between retina and visual cortex. The winning neurons are trained on many columns of deep neurons to attain expertise on pre-processed inputs in many different ways after which their predictions are averaged. Also GPU used, enables the models to be trained faster than usual. Upon testing the proposed method over MNIST handwriting data it achieves a near-human performance. Upon considering traffic sign recognition, our architecture has an upper hand by a factor of two. We also tried to improve the state-of-theart on a huge amount of common image classification benchmarks.

Get Tissue Thickness (GTT) is a systematic, automated and fast method to quantify the thickness of tissues in the head and measure scalp-to-cortex distance.

L. Perronnet, M.E. Vilarchao, G. Hucher, D.E. Shulz, G. Peyré, I. Ferezou. An automated workflow for the anatomo-functional mapping of the barrel cortex. Journal of Neuroscience Methods, 263(1), pp. 145–154, 2016.

A paradigm shift in prompt engineering for massive context LLMs (Gemini PRO). Zero-code multi-agent orchestration.

HVAC demand forecasting lab comparing Cortex ML, XGBoost, and Snowpark ML methods for the manufacturing space

Supplementary Materials for Waldren et al. (2024). Unpacking the Overlap between Autism and ADHD in Adults: A Multi-Method Approach. Cortex

This is a lab course in which C & Assembly programming languages are used to understand and implement I/O interface methods for the ARM Cortex microcomputer and to demonstrate knowledge of memory maps, polling, interrupts, and internal structure of a microprocessor.

The Zynq-7000 All Programmable SoC contains two ARM Cortex-A9 processors that can be configured to concurrently run independent software stacks or executables. This demo software shows a method of starting up both processors, each running its own operating system and application, and allowing each processor to communicate with the other through shared memory

Deep Learning is a technique for machine learning that creates artificial neural networks to simulate the human brain's structure and function. In fact, deep learning uses a large number of hidden nonlinear processing layers to extract data characteristics or features and turn the data into various levels of representation. deep learning is distinct from conventional "shallow learning" because hierarchical abstraction and representations are learned at much deeper levels. this learning technique is a pioneering method for processing large amounts of data, since as it evaluates more data, the efficiency of the model improves. the model becomes more expert at identifying even unknown patterns among the data as the data increases. significant progress has been made in recent years of deep learning in the areas of computer vision and natural language processing. a deep learning neural network built to process organized arrays of data such as images is a convolutional neural network, or CNN. In computer vision, conventional neural networks are commonly used and have become the state of the art for many visual applications, such as image classification, and have also been popular in the processing of natural language for text classification. a convolutional neural network, mostly with more than 8 layers, is a feed-forward neural network. convolutional neural networks include several convolution layers, each able to recognize more sophisticated shapes, laying on top of each other. the use of convolutional layers in a CNN represents the human visual cortex structure, where a sequence of layers analyze a received object and gradually define more complex characteristics and features.

Methods for analyzing monkey single cell multiomics datasets in the prefrontal cortex.

Utilizing deep learning-based methods for functional or structural modeling of the human visual cortex.

EEG signal denoising and frequency-domain analysis using computational methods, inspired by dopamine modulation in the cerebral cortex.

Code for results in "Coding odor modality in piriform cortex efficiently with low-dimensional subspaces with the Shared Covariance Decoding method"

Dynamical dimensionality reduction method to uncover rotational dynamics in the motor cortex project. Part of Brain Machine Interface coursework at CUED.

Humans and animals integrate visual inputs from both eyes in the primary visual cortex (V1) to construct a three-dimensional perception of the world. This code implements the Spike-Triggered Average (STA) method to calculate and visualize binocular receptive fields from real neuronal electrophysiological data.

Local Field potential (LFP) in the basal ganglia (BG) nuclei in the brain have attracted much research and clinical interest. However, the origin of this signal is still under debate throughout the last decades. The question is whether it is a local subthreshold phenomenon, synaptic input to neurons or it is a flow of electrical signals merged as volume conduction which are generated from simultaneous firing neurons in the cerebral cortex and obeys the Maxwell equations. In this study, we recorded in a monkey brain simultaneously LFP's from the cerebral cortex, in the frontal lobe and primary motor cortex (M1) and in sites in all BG nuclei: the striatum, globus pallidus, and subthalamic nucleus. All the records were taken from human primate model (vervet monkey), during spontaneous activity. Developing and applying a novel method to identify significant cross-correlations (potential links) while removing "spurious" correlations, we found a tool that may discriminate between the two major phenomena of synaptic inputs (as we define as information flow) and volume conduction. We find mainly two major paths flows of field potential, that propagates with two different time delays, from the primary motor cortex, and from the frontal cortex. Our results indicate that the two path flows may represent the two mechanisms of volume conduction and information flow.

This repo. will experience the evolution and development of the "Unfolding the Subcortex" project. We will unfold the subcortical nuclei one by one with great attention and utmost precision using relevant methods. The codes/workflows/scripts will be provided here. We will also provide all the cut-points, and any other supporting files necessary to reproduce the same results or improve the existing unfoldings or create new results. Our work will help all us, the way we look at the sub-cortex and understand it. You should contribute.