z. X "The Brain, Pt. 1" Quick steps through key concepts in neuroscience of great utility when reading our works on neurodivergence, consciousness, the critical brain, differential processing, etc
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z. X "The Brain, Pt. 1" Quick steps through key concepts in neuroscience of great utility when reading our works on neurodivergence, consciousness, the critical brain, differential processing, etc

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Classic and connectionist computationalism
Spurred by a couple of recent conversations, I’ve been thinking about computation in the brain. It was accelerated this week by the news that the connectome of the fly brain is complete, a mapping of its 140,000 neurons and 55 million synapses. It’s a big improvement over the 302 neurons of the C. Elegans worm, which were mapped decades ago. Apparently there are already new computational models…
Substitute Item Exercise #CreativeThinking #Creativity #amcreating #writerslife #MotivationMonday
I like to be an “early adopter” of undeveloped technology and one adventure into a clumsily performed service led me into an altogether peculiar, profound, and yet productive creative direction. I was one of the first guys to ‘sign up’ for online grocery delivery here in England (they reckon about ¾ of UK customers now choose groceries on-line. But back then, nobody did.) Michael Aldrich The…
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Connectionism and learning - Psychology Dictionary of Arguments
Learning/connectionism/Mareschal/Kaufman: (i.e., adapting one’s behavior) is accomplished by tuning the connection weights until some stable behavior is obtained. Supervised networks adjust their weights until the output response (for a given input) matches a target response. That target can come from an active teacher, or passively through observing the environment, but it must come from outside the system. Unsupervised networks adjust their weights until some internal constraint is satisfied (e.g., maximally different inputs must have maximally different internal representations). Slater I 94 The key conclusion from this work (Munakata et al. 1997)(1) is the notion of the graded representation of knowledge. That is, rather than existing as an all-or-none concept, object permanence was acquired gradually. Consequently, the representations that underlay this concept existed in graded states, becoming ever more robust with age and experience, and supporting ever more complex disappearance events. 1. Munakata, Y., McClelland, J. L., Johnson, M. H., & Siegler, R. S. (1997). Rethinking infant knowledge: Toward an adaptive process account of successes and failures in object permanence tasks. Psychological Review, 104, 686–713. Denis Mareschal and Jordy Kaufman, „Object permanence in Infancy. Revisiting Baillargeon’s Drawbridge Experiment“ in: Alan M. Slater & Paul C. Quinn (eds.) 2012. Developmental Psychology. Revisiting the Classic Studies. London: Sage Publications. Pp. 93-94
Connectionism | TESL Issues
Connectionism
Connectionism originated in the relativity well-established notion in psychology of ‘parallel processing. This was advanced by the work of the ‘parallel distributed processing’ (PDP) group held by Rumelhart and McClelland (1986).
The term “connectionism” refers in general to a form of cognitive modeling wherein cognitive processing is represented in terms that can be implemented by…
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Top Artificial Intelligence Developments and Examples
Top Artificial Intelligence Developments and Examples
Intelligence, defined as the ability to acquire knowledge and skills. Intelligence for the longest time possible is associated with the human brain. Artificial intelligence is basically defined as intelligence that is originating from machines. Most computer applications only make existing processes and functions faster and maybe more efficiently but cannot create new duties altogether. However,…
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Connectionism
With the rise of neuroscience, we are able to understand human behavior in a new way, one that does not rely on the abstract concepts of folk psychology. A more advanced model of the mind, connectionism, is an artificial intelligence model based on the biological workings of the brain. It focuses on the idea of behavior being an emergent process of interconnected, parallel, highly distributed neural networks comprised of nodes (e.g. neurons) with varying activation levels (e.g. action potentials). Beliefs and other constructs are therefore not necessary to understand the mind. There are no single unchanging structures of the mind that influence behavior (e.g. intentions, motivations, beliefs). Instead, the mind can be understood as being emergent from the parallel distributed processing of the brain. The relationship between activation in a system of nodes represents different cognitive capabilities. Connectionism can accurately incorporate things we see in advancements in neuroscience, such as neuroplasticity (the way the brain is able to regain functioning when a particular area is damaged). A connectionist model is explanatorily strong because it is essentially modeling its emergent phenomena as the biology of the brain.