Siamese Cats and the Optic Chiasm

the sensitive motor

Anatomy constrains function | Function drives anatomy

As a general rule of thumb, information about the right side of the body is represented in the left side of the brain, and information about the left side is represented on the right.  This is called lateralization.  While that’s probably only mildly interesting to most people, it can help neurologists determine where a nervous system injury (eg, stroke) has occurred.

But to me, even though I study the motor and somatosensory systems where this holds (mostly) true, I think a cooler example of decussation (crossing from one side to the other) is the optic chiasm.   More than that, it’s a great example of what neuroanatomy can tell us about the body and how it functions in its environment.  Similarly, the anatomy and behavior of an animal tell us about its brain.

Before I get in to the details, I should cover a few terms: visual fields, monocular/binocular…

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In what way can philosophy, or philosophical thinking contribute to the physical sciences?

The MOOC's Essays

86px-richard_feynman_nobel Richard P. Feynman

Physicist hero and Nobel laureate, Richard Feynman, was known for not being particularily fond of philosophy. In his Auckland lecture on Quantum Mechanics, he addresses philosophy with the polemic challenge that “if you don’t like the universe as it is, go somewhere else, to another universe where the rules are simpler” [1]. As much as this statement reflects a clear-cut scientific realism, criticizing what he disdained as wishful thinking, this essay takes a more differentiated approach. It is trying to investigate the question how much philosophy, from which physics had emanated, can make contributions to the physical sciences. In trying to argue that science without philosophy runs the risk of being disoriented, it investigates the following question: How could philosophical thinking help avoid physical sciences drifting off into the wrong direction?

Albeit it is the obvious objection that science has to be free to investigate in whatever…

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Breaking: Fatal Courtroom Act Ruins Michael ‘hockey stick’ Mann


By Paul Homewood

Is the Michael Mann/Tim Ball case coming to a head?

John Sullivan writes:

Penn State climate scientist, Michael ‘hockey stick’ Mann commits contempt of court in the ‘climate science trial of the century.’ Prominent alarmist shockingly defies judge and refuses to surrender data for open court examination. Only possible outcome: Mann’s humiliation, defeat and likely criminal investigation in the U.S.

The defendant in the libel trial, the 79-year-old Canadian climatologist, Dr Tim Ball (above, right) is expected to instruct his British Columbia attorneys to trigger mandatory punitive court sanctions, including a ruling that Mann did act with criminal intent when using public funds to commit climate data fraud. Mann’s imminent defeat is set to send shock waves worldwide within the climate science community as the outcome will be both a legal and scientific vindication of U.S. President Donald Trump’s claims that climate scare stories are a “hoax.”

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Donde Encontrar Componentes Electrónicos (en Monterrey)

Tal vez no sorprendente-mente el centro de Monterrey tiene una variedad de tiendas de componentes de electronica que le pueden servir a cualquier maker, hacedor, cacharrero, Inventor y demás gente inquieta interesada en construir o arreglar algún aparato electrónico

El centro de toda ciudad es un lugar a explorar, y la cantidad de tiendas que venden componentes de todos tipos (no solo de electronica) es mayor de lo que puede una persona tener suficiente experiencia en compras como para poder hablar de todas ellas con debida justicia. Por eso vamos a hablar de las tiendas principales cerca de la esquina mágica de Juarez y colon en el centro de Monterrey

Electronicas en Monterrey

Me referí a esa esquina en términos exagerados porque los componentes que podemos encontrar aquí son muy variados, desde robotica, control, iluminación LED, energía, audio e instrumentos musicales.

A continuación una pequeña descripción de las tiendas marcadas con Estrella en…

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Deep learning

Deep learning (deep machine learning, or deep structured learning, or hierarchical learning, or sometimes DL) is a branch ofmachine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures or otherwise, composed of multiple non-linear transformations.[1][2][3][4][5]

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc.. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition[6]) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.[7]

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.[8]

Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks andrecurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.

Alternatively, deep learning has been characterized as a buzzword, or a rebranding of neural networks.[9][10]