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... example, Jordan & Bishop [18] used neural networks to name SNs, and Tenenbaum et al. [40] used SNs to model the mind. An emergent representation emerges autonomously from system’s interactions with the external world (outside the brain or network) and the internal world via its sensors and its effec ...
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Dynamic Decision Making in Complex Task Environments

... The Drift Diffusion Model • Continuous version of the SPRT • At each time step a small random step is taken. • Mean direction of steps is +m for one direction, –m for the other. • When criterion is reached, respond. • Alternatively, in ‘time controlled’ tasks, respond when signal is given. ...
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Kristin Völk – Curriculum Vitae

... Working Title Predictiveness and prediction in classical conditioning: a Bayesian statistical model Description Classical conditioning is a rather pure form of prediction learning. Here we focus on one of its critical facets that still lacks a statistical treatment, namely, that conditioned stimuli ...
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... axon and the length of the branching segments. While angle of branching and segment length can vary from one neuron to another in the same network, all the branchings of the axon of a given neuron have the same branching angle and the same length. The growing axon of all neurons branches a fixed num ...
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Introduction to Psychology - John Marshall High School

... when released by the sending neuron, neurotransmitters travel across the synapse and bind to receptor sites on the receiving neuron, thereby influencing whether it will generate a neural impulse ...
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Neuroplasticity - Bakersfield College

... cell division in the ventricular zone of the neural tube, they migrate Migrating cells are immature, lacking axons and dendrites ...
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... even consciousness are thought to be realized through complex interactions of streams of millisecond-order electrical spikes (known as action potentials) generated by billions of neurons. How can one investigate such a complicated organ? As action potentials are electric signals mediated by flows of ...
Pietro Berkes , Richard E. Turner , József Fiser
Pietro Berkes , Richard E. Turner , József Fiser

... Hamiltonian Monte Carlo: augment model variables with „momentum variables‟, in analogy with physical system Langevin sampling: special case of Hamiltonian MC; following dynamics for a single step at each iteration, one can get rid of the momentum variables, which results in this ...
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... , University of Houston, USA: “Closed Loop Deep Brain Stimulation in Movement Disorders” , Brandeis University, USA: “Using artificial neural networks to analyze biological neurons via transfer learning” , Ort Braude College, Israel: “Incremental reasoning on strongly distributed systems” , Ghent U ...
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... normal development of spinal column, skull & overlying skin. If closure does not occur normally, nervous system may remain exposed (“open NTD”). • In other cases the neural tube may not be exposed to the surface (“closed NTD”), but the spinal vertebrae and skin surrounding the spine may not be compl ...
Lecture3n
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... is the collection of all real-valued continuous functions defined on some interval . is the collection of all functions with continuous th derivatives. A function space is a topological vector space whose "points" are functions. http://mathworld.wolfram.com/FunctionSpace.html ...
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Recurrent neural network

A recurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. This makes them applicable to tasks such as unsegmented connected handwriting recognition or speech recognition
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