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HTM Neuron paper 12-1
HTM Neuron paper 12-1

... Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple pa ...
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... measure the distribution of selectivity across cells before and after training. When comparing multiple networks, we use the mean of the stimulus-pair selectivity across cells. In order to determine whether or not the information about stimulus-pairs within a given associative network is sufficient ...
Learning Bayesian Networks: A Unification for Discrete and
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... p(GB.IB:,�). In the remainder of this section, we show how to compute p(DI8B•,B:, �). A method for computing this term follows from our random-sample assumption. Namely, given hypothe­ sis B:, it follows that D can be separated into a set of random samples, where these random samples are determined ...
Dynamic traffic splitting to parallel wireless networks with partial information: a Bayesian approach
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... observations have shown that the firing rate responses of some neurons are very sensitive to input fluctuations, while responses of others are not [18, 21]. Interestingly, we found that commonly used neuron models (single-compartment, biophysical models) do not replicate this diversity with their st ...
Combining Rule Induction and Reinforcement Learning
Combining Rule Induction and Reinforcement Learning

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IOSR Journal of Computer Engineering (IOSR-JCE)

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Copy of the full paper
Copy of the full paper

... signals by precisely computing the neurons’ asynchronous spikes. Neuron models can precisely describe the biophysics of spikes (action potentials) by computing the currents flowing through cell membrane and synaptic nodes. It is possible to reduce the size of these models to facilitate their computa ...
PDF
PDF

Computational Intelligence Methods
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Digital Selection and Analogue Amplification Coexist in a cortex-inspired silicon circuit
Digital Selection and Analogue Amplification Coexist in a cortex-inspired silicon circuit

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Chunking of Action Sequences in the Cortex

... There is a plethora of different models that try to better mimic the function of the basal ganglia. The level of detail has increased over time. Houk et al. (1995) presented one of the first models. The problem with it was that it did not account for the timed depression of dopamine when an expected r ...
Synchronous vs. Conjunctive Binding: A False Dichotomy? Robert F. Hadley ()
Synchronous vs. Conjunctive Binding: A False Dichotomy? Robert F. Hadley ()

... considerable notice, both because of its biological basis (cf. Singer, 1999, 2003) and because it (purportedly) avoids the proliferation of nodes attributed to conjunctive coding. Evidence for the role of synchronously firing neurons in sensory feature binding is substantial (Singer, 1999), and sync ...
Machine Learning for Medical Diagnosis
Machine Learning for Medical Diagnosis

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Networks of Spiking Neurons: The Third Generation of
Networks of Spiking Neurons: The Third Generation of

... gates. They give rise to a variety of neural network models such as multilayer perceptrons (also called threshold circuits), Hopfield nets, and Boltzmann machines. A characteristic feature of these models is that they can only give digital output. In fact they are universal for computations with dig ...
Hypernetworks: A Molecular Evolutionary Architecture for Cognitive
Hypernetworks: A Molecular Evolutionary Architecture for Cognitive

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Catastrophic interference



Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.
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