Seeing Lightness, Darkness and Color | Visual Perception and the Brain | Coursera

Hi everyone,

A couple of weeks ago, I started the “Visual Perception and the Brain” course, at, lectured by Dr. Dales Purves, M.D. from Duke University.

The purpose of the course is to consider how what we see is generated by the visual system. Thus the objectives of the course are:

  • To introduce perceptual phenomenology;
  • To brainstorm about how phenomenology can be explained;
  • To consider possible explanations about brain function.

During the first lecture, Dr. Dales Purves discussed about “What We Actually See” and introduced us to The Inverse Problem, followed by the “Visual Stimuli” and the “Organisation of the Human Visual System”. A lot of topics were addressed, like the eye, the retina, the primary visual pathway, the visual cortex and receptive fields.

Last week’s lecture was about “Seeing Lightness, Darkness and Colour”, and these topics were elaborated with an emphasis on the discrepancies between luminance and lightness, light and colour, and how our visual system works to allows us to perceive colour.

It was a fascinating lecture with a lot of new information about how we see and what do we see. It was enlightening to understand how human evolution developed our visual sense to adapt to different lights and colours. My question for this topic is: what is the correlation between our colour perception and the colour theory lectured in design courses? If we all perceive colour in different ways, why does some make us feel different emotions? What part of the visual system connects these? And for fun: what colour was the dress?





Phyisiology, Signal, and Noise | Principles of fMRI 1 | Coursera

Hi everyone,

This week, I continued the “Principles of fMRI 1” course, on The lecture discussed “Phyisiology, Signal, and Noise”.

On this lecture, Martin Lindquist and Tor Wager tackled “Signal, noise, and BOLD physiology”, “fMRI Artifacts & Noise”, “Spatial and Temporal Resolution of BOLD fMRI”, “Experimental Design – Kinds of Designs” and “Pre-processing”.

It started with some definitions and differences between MRI, fMRI and BOLD fMRI, and how BOLD fMRI allows us to measure the metabolic demands of active neurons. It also pointed out that not all BOLD signals reflect neuronal activity, and that BOLD fMRI require some acquisition, analysis and pre-processing techniques to be valid for a specific context.

One of the biggest problems in acquiring, analysing and pre-processing data is spatial and temporal resolutions, which need to be taken into consideration prior to the experiments, so it can be included in their design in order to validate the outcomes.

Another common issues is group experiments, which require more complex alignment and normalisation techniques that allows us to compare and match the outcomes with the expected results.

Finally, they discussed the kinds of designs that we can create and how data is handled by the General(ized) Linear Model, the trade-offs between each design, the characteristics variables, and how all comes down together with pre-processing techniques that correct for noise and errors (i.e., design errors, experiment errors, result deviations).

Without a doubt, it was a somewhat difficult lecture to understand, mostly, because the examples are too abstract for me to understand the underlying processes that undergo these three phases (i.e., acquisition, analysis, pre-processing). However, it was enlightening and clear enough for me to understand better how fMRI is modelled to each experiment and how to design for general or specific results.



Cable Theory and Dendritic Computations | Synapses, Neurons and Brains | Coursera

Hi everyone,

I had started several courses on related to neurobiology or computational neuroscience. They are getting more and more complicated yet more and more interesting.

Today I finished another lecture from “Synapses, Neurons and Brains” that discussed the Wilfrid Rall’s Cable Theory and Dendritic Computations. After so many week studying how the neurons work, how they pass electrical signals from axons to dendrites, from spikes to post-synaptic potentials, I started to dive into the computational aspect of the brain.

This lesson discussed several aspects of the brain computation, first at the level of a single neuron, and then, at the level of electric-distributed trees; from the theories that support today’s computational models to complex breakthroughs that develop our understanding of the neural network.

Recognition algorithms have been implemented with artificial neural networks. I have been intrigued by them as well as genetic algorithms; I have thought of the concept Genetic Neural Networks. Now, I am presented with electrical-distributed dendrites and I am starting to think about distributed neural networks – if one network takes care of the auditory system (the audio input in a computer), another may take care of the visual system (the visual input from a camera). The question is: would a genetic neural network be able to change itself to adapt sub unit networks to perform and communicate better with mutation in the blueprints of the network itself? Can we create a neural network with self-awareness?