Above are papers related to the following paper: [Paper_ID] Paper #1118017. 	[Title] A Neural Network Model of Memory Consolidation [Abstract] Some forms of memory rely temporarily on a system of brain structures located in the medial temporal lobe that includes the hippocampus. The recall of recent events is one task that relies crucially on the proper functioning of this system. As the event becomes less recent, the medial temporal lobe becomes less critical to the recall of the event, and the recollection appears to rely more upon the neocortex. It has been proposed that a process called consolidation is responsible for transfer of memory from the medial temporal lobe to the neocortex. We examine a network model proposed by P. Alvarez and L. Squire designed to incorporate some of the known features of consolidation, and propose several possible experiments intended to help evaluate the performance of this model under more realistic conditions. Finally, we implement an extended version of the model that can accommodate varying assumptions about the number of areas and connections within the brain and memory capacity, and examine the performance of our model on Alvarez and Squire's original task. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #157401. 	[Title] Convergence-Zone Episodic Memory: Analysis and Simulations [Abstract] Human episodic memory provides a seemingly unlimited storage for everyday experiences, and a retrieval system that allows us to access the experiences with partial activation of their components. The system is believed to consist of a fast, temporary storage in the hippocampus, and a slow, long-term storage within the neocortex. This paper presents a neural network model of the hippocampal episodic memory inspired by Damasio's idea of Convergence Zones. The model consists of a layer of perceptual feature maps and a binding layer. A perceptual feature pattern is coarse coded in the binding layer, and stored on the weights between layers. A partial activation of the stored features activates the binding pattern, which in turn reactivates the entire stored pattern. For many configurations of the model, a theoretical lower bound for the memory capacity can be derived, and it can be an order of magnitude or higher than the number of all units in the model, and several orders of magnitude higher than the number of binding-layer units. Computational simulations further indicate that the average capacity is an order of magnitude larger than the theoretical lower bound, and making the connectivity between layers sparser causes an even further increase in capacity. Simulations also show that if more descriptive binding patterns are used, the errors tend to be more plausible (patterns are confused with other similar patterns), with a slight cost in capacity. The convergence-zone episodic memory therefore accounts for the immediate storage and associative retrieval capability and large capacity of the hippocampal memory, and shows why the memory encoding areas can be much smaller than the perceptual maps, consist of rather coarse computational units, and be only sparsely connected to the perceptual maps. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1365. 	[Title] Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex. [Abstract] Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3229. 	[Title] Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2 [Abstract] Neural computation, also called connectionism, parallel distributed processing, neural network modeling or brain-style computation, has grown rapidly in the last decade. Despite this explosion, and ultimately because of impressive applications, there has been a dire need for a concise introduction from a theoretical perspective, analyzing the strengths and weaknesses of connectionist approaches and establishing links to other disciplines, such as statistics or control theory. The Introduction to the Theory of Neural Computation by Hertz, Krogh and Palmer (subsequently referred to as HKP) is written from the perspective of physics, the home discipline of the authors. The book fulfills its mission as an introduction for neural network novices, provided that they have some background in calculus, linear algebra, and statistics. It covers a number of models that are often viewed as disjoint. Critical analyses and fruitful comparisons between these models Please summarise all the given information.

Paper #1118017 has the title `A Neural Network Model of Memory Consolidation  
` and the abstract `Some forms of memory rely temporarily on a system of brain structures located in the medial temporal lobe that includes the hippocampus. The recall of recent events is one task that relies crucially on the proper functioning of this system. As the event becomes less recent, the medial temporal lobe becomes less critical to the recall of the event, and the recollection appears to rely more upon the neocortex. It has been proposed that a process called consolidation is responsible for transfer of memory from the medial temporal lobe to the neocortex. We examine a network model proposed by P. Alvarez and L. Squire designed to incorporate some of the known features of consolidation, and propose several possible experiments intended to help evaluate the performance of this model under more realistic conditions. Finally, we implement an extended version of the model that can accommodate varying assumptions about the number of areas and connections within the brain and memory capacity, and examine the performance of our model on Alvarez and Squire's original task. 
`.
Question: Which category should Paper #1118017 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1118017 has the title `A Neural Network Model of Memory Consolidation  
` and the abstract `Some forms of memory rely temporarily on a system of brain structures located in the medial temporal lobe that includes the hippocampus. The recall of recent events is one task that relies crucially on the proper functioning of this system. As the event becomes less recent, the medial temporal lobe becomes less critical to the recall of the event, and the recollection appears to rely more upon the neocortex. It has been proposed that a process called consolidation is responsible for transfer of memory from the medial temporal lobe to the neocortex. We examine a network model proposed by P. Alvarez and L. Squire designed to incorporate some of the known features of consolidation, and propose several possible experiments intended to help evaluate the performance of this model under more realistic conditions. Finally, we implement an extended version of the model that can accommodate varying assumptions about the number of areas and connections within the brain and memory capacity, and examine the performance of our model on Alvarez and Squire's original task. 
`.
Question: Which category should Paper #1118017 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks