The Big Ideas in Cognitive Neuroscience, Explained

Are emergent properties really for losers? Why are architectures important? What are “mirror neuron ensembles” anyway? Mylast post presented an idiosyncratic distillation of theBig Ideas in Cognitive Neuroscience symposium, presented by six speakers at the 2017 CNS meeting. Here I ’ll briefly explain what I meant in the bullet points. In some cases I didn ' t quite understand what the speaker meant so I used outside sources. At the end is a bonus reading list.The first two speakers made an especially fun pair on the topic of memory: they held opposing views on the “engram”, the physical manifestation of a memory in the brain.1 They also disagreed on most everything else.1. Charles Randy Gallistel (Rutgers University)–What Memory Must Look LikeGallistel is convinced thatMost Neuroscientists Are Wrong About the Brain. Thissubtly bizarre essay inNautilus (whichwaswidelyscorned on Twitter) succinctly summarized the major points of his talk. You and I may think the brain-as-computer metaphor has outlived its usefulness, but Gallistel says that “Computation in the brain must resemble computation in a computer.” The brain is an information processing device in the sense ofShannon information theory.Shannon information is a set of possible messages encoded as bit patterns and sent over a noisy channel to a recipient that will hopefully decode the message with minimal error. In this purely mathematical theory, the semantic content (meaning) of a message is irrelev...
Source: The Neurocritic - Category: Neuroscience Authors: Source Type: blogs