14) Neocortex is composed of columnar functional units (Mountcastle – 1957, Hubel & Wiesel – 1962) Mountcastle found that nearby neurons in monkey somatosensory cortex tend to activate for similar sensory experiences. For example, a neuron might respond best to a vibration of the right index finger tip, while a neuron slightly deeper in might …
Continue reading “History’s Top Brain Computation Insights: Day 14”
13) Larger cortical space is correlated with greater representational resolution; memories are stored in cortex (Penfield – 1957) Prior to performing surgery, Wilder Penfield electrically stimulated epileptic patients' brains while they were awake. He found the motor and somatosensory strips along the central sulcus, just as was found in dogs by Fitsch & Hitzig (see …
Continue reading “History’s Top Brain Computation Insights: Day 13”
12) Hippocampus is necessary for episodic memory formation (Milner – 1953) Patient H.M. had terrible epilepsy originating in the medial temporal lobes. His neurosurgeon decided to take out the source of the epilepsy: the hippocampus. Surprisingly, after the operation H.M. could no longer form new long-term memories. He could remember things for short time periods …
Continue reading “History’s Top Brain Computation Insights: Day 12”
11) Action potentials, the electrical events underlying brain communication, are governed by ion concentrations and voltage differences mediated by ion channels (Hodgkin & Huxley – 1952) Hodgkin & Huxley developed the voltage clamp, which allows ion concentrations in a neuron to be measured with the voltage constant. Using this device, they demonstrated changes in ion …
Continue reading “History’s Top Brain Computation Insights: Day 11”
10) The Hebbian learning rule: 'Neurons that fire together wire together' [plus corollaries] (Hebb – 1949) D. O. Hebb's most famous idea, that neurons with correlated activity increase their synaptic connection strength, was based on the more general concept of association of correlated ideas by philosopher David Hume (1739) and others. Hebb expanded on this …
Continue reading “History’s Top Brain Computation Insights: Day 10”
9) Convergence and divergence between layers of neural units can perform abstract computations (Pitts & McCulloch – 1947) Pitts & McCulloch created the first artificial neurons and artificial neural network. In 1943 they showed that computational processing could be performed by a series of convergent and divergent connections among neuron-like units. In 1947 they demonstrated …
Continue reading “History’s Top Brain Computation Insights: Day 9”
8) Reward-based reinforcement learning can explain much of behavior (Skinner – 1938, Thorndike – 1911, Pavlov – 1905) B. F. Skinner showed that reward governs much of human and animal behavior. He discovered operant conditioning, a method for manipulating behavior so powerful he could teach a pigeon to bowl (or a dog to jump on …
Continue reading “History’s Top Brain Computation Insights: Day 8”
7) Brain signals are chemical (Dale – 1914, Loewi – 1921) Loewi found that electrically stimulating a heart causes it to release a chemical substance which changes the beating of a different heart when exposed to that chemical substance. Dale had already discovered neurotransmitters, one of which (acetylcholine) was the chemical responsible for the change …
Continue reading “History’s Top Brain Computation Insights: Day 7”
6) Neural networks consist of excitatory and inhibitory neurons connected by synapses (Sherrington – 1906) Based on his observations in the spinal cord, Sherrington theorized that the brain consists of complex networks of excitatory and inhibitory cells he was the first to term 'neurons', with connection points he was the first to term 'synapses'. His …
Continue reading “History’s Top Brain Computation Insights: Day 6”
5) Neurons are fundamental units of brain computation (Ramon y Cajal – 1889) Golgi, a prominent 19th century biologist, argued that the brain is one unified reticulum (or web) of neural tissue, much like the circulatory system. However, Ramon y Cajal came to a very different conclusion using Golgi’s very own silver chromate staining technique. …
Continue reading “History’s Top Brain Computation Insights: Day 5”
Backpropogation of Error (or "backprop") is the most commonly-used neural network training algorithm. Although fundamentally different from the less common Hebbian-like mechanism mentioned in my last post , it similarly specifies how the weights between the units in a network should be changed in response to various patterns of activity. Since backprop is so …
Continue reading “A Popular but Problematic Learning Rule: “Backpropogration of Error””
4) Functions can be localized in the brain (Bouillaud – 1825, Broca – 1861, Fritsch & Hitzig – 1870) Bouillaud and Broca discovered patients with frontal cortex lesions who had speech problems. Fritsch & Hitzig discovered primary motor cortex; a specialized chunk of cortex specifically for motor control. Broca believed that all brain functions would …
Continue reading “History’s Top Brain Computation Insights: Day 4”
3) Functions are distributed in the brain (Flourens – 1824, Lashley – 1929) Flourens found that it did not matter where he lesioned inside cortex; what mattered was how much he lesioned. This suggested that functions were equally distributed (the law of equipotentiality) and widely distributed (the law of mass action) across cortex. Lashley updated …
Continue reading “History’s Top Brain Computation Insights: Day 3”
This post is part of a series chronicling history's top brain computation insights (see the first of the series for a detailed description). 2) Brain signals are electrical (Galvani – 1791, Rolando – 1809) Galvani (whose name inspired the word 'galvanize') discovered that electrically shocking frog nerves made their muscles move. Rolando used this same …
Continue reading “History’s Top Brain Computation Insights: Day 2”
It is hard to maintain historical perspective as neuroscience progresses. Today's complications and confusions seem to cloud the clear insights of the past. This is inevitable when trying to understand the brain, the most complex computational device known. The plan here is to highlight history's major brain computational insights in the interest of integrating them …
Continue reading “History’s Top Brain Computation Insights: Day 1”
