Above are papers related to the following paper: [Paper_ID] Paper #1136446. 	[Title] The role of afferent excitatory and lateral inhibitory synaptic plasticity in visual cortical ocular dominance [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136449. 	[Title] Rearrangement of receptive field topography after intracortical and peripheral stimulation: The role of plasticity in [Abstract] Intracortical microstimulation (ICMS) of a single site in the somatosensory cortex of rats and monkeys for 2-6 hours produces a large increase in the number of neurons responsive to the skin region corresponding to the ICMS-site receptive field (RF), with very little effect on the position and size of the ICMS-site RF, and the response evoked at the ICMS site by tactile stimulation (Recanzone et al., 1992b). Large changes in RF topography are observed following several weeks of repetitive stimulation of a restricted skin region in monkeys (Jenkins et al., 1990; Recanzone et al., 1992acde). Repetitive stimulation of a localized skin region in monkeys produced by training the monkeys in a tactile frequency discrimination task improves their performance (Recanzone et al., 1992a). It has been suggested that these changes in RF topography are caused by competitive learning in excitatory pathways (Grajski & Merzenich, 1990; Jenkins et al., 1990; Recanzone et al., 1992abcde). ICMS almost simultaneously excites excitatory and inhibitory terminals and excitatory and inhibitory cortical neurons within a few microns of the stimulating electrode. Thus, this paper investigates the implications of the possibility that lateral inhibitory pathways too may undergo synaptic plasticity during ICMS. Lateral inhibitory pathways may also undergo synaptic plasticity in adult animals during peripheral conditioning. The "EXIN" (afferent excitatory and lateral inhibitory) synaptic plasticity rules Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #7297. 	[Title] Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex [Abstract] Cells in the visual cortex are selective not only to ocular dominance and orientation of the input, but also to its size and spatial frequency. The simulations reported in this paper show how size selectivity could develop through Hebbian self-organization, and how receptive fields of different sizes could organize into columns like those for orientation and ocular dominance. The lateral connections in the network self-organize cooperatively and simultaneously with the receptive field sizes, and produce patterns of lateral connectivity that closely follow the receptive field organization. Together with our previous work on ocular dominance and orientation selectivity, these results suggest that a single Hebbian self-organizing process can give rise to all the major receptive field properties in the visual cortex, and also to structured patterns of lateral interactions, some of which have been verified experimentally and others predicted by the model. The model also suggests a functional role for the self-organized structures: The afferent receptive fields develop a sparse coding of the visual input, and the recurrent lateral interactions eliminate redundancies in cortical activity patterns, allowing the cortex to efficiently process massive amounts of visual information. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #763009. 	[Title] Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning [Abstract] Acknowledgements: This research was supported in part by the Office of Naval Research (Cognitive and Neural Sciences, N00014-93-1-0208) and by the Whitaker Foundation (Special Opportunity Grant). We thank George Kalarickal, Charles Schmitt, William Ross, and Douglas Kelly for valuable discussions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #762980. 	[Title] Modeling dynamic receptive field changes produced by intracortical microstimulation [Abstract] Intracortical microstimulation (ICMS) of a localized site in the somatosensory cortex of rats and monkeys for 2-6 hours produces a large increase in the cortical representation of the skin region represented by the ICMS-site neurons before ICMS, with very little effect on the ICMS-site neuron's RF location, RF size, and responsiveness (Recanzone et al., 1992). The "EXIN" (afferent excitatory and lateral inhibitory) learning rules (Marshall, 1995) are used to model RF changes during ICMS. The EXIN model produces reorganization of RF topography similar to that observed experimentally. The possible role of inhibitory learning in producing the effects of ICMS is studied by simulating the EXIN model with only lateral inhibitory learning. The model also produces an increase in the cortical representation of the skin region represented by the ICMS-site RF. ICMS is compared to artificial scotoma conditioning (Pettet & Gilbert, 1992) and retinal lesions (Darian-Smith & Gilbert, 1995), and it is suggested that lateral inhibitory learning may be a general principle of cortical plasticity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #763010. 	[Title] Modeling dynamic receptive field changes in primary visual cortex using inhibitory learning [Abstract] The position, size, and shape of the visual receptive field (RF) of some primary visual cortical neurons change dynamically, in response to artificial scotoma conditioning in cats (Pettet & Gilbert, 1992) and to retinal lesions in cats and monkeys (Darian-Smith & Gilbert, 1995). The "EXIN" learning rules (Marshall, 1995) are used to model dynamic RF changes. The EXIN model is compared with an adaptation model (Xing & Gerstein, 1994) and the LISSOM model (Sirosh & Miikkulainen, 1994; Sirosh et al., 1996). To emphasize the role of the lateral inhibitory learning rules, the EXIN and the LISSOM simulations were done with only lateral inhibitory learning. During scotoma conditioning, the EXIN model without feedforward learning produces centrifugal expansion of RFs initially inside the scotoma region, accompanied by increased responsiveness, without changes in spontaneous activation. The EXIN model without feedforward learning is more consistent with the neurophysiological data than are the adaptation model and the LISSOM model. The comparison between the EXIN and the LISSOM models suggests experiments to determine the role of feedforward excitatory and lateral inhibitory learning in producing dynamic RF changes during scotoma conditioning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #19621. 	[Title] References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature [Abstract] 2] D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning Internal Representations by Error Propagation", in D. E. Rumelhart and J. L. McClelland (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1), MIT Press (1986). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #7272. 	[Title] Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2665. 	[Title] An information-maximisation approach to blind separation and blind deconvolution [Abstract] We derive a new self-organising learning algorithm which maximises the information transferred in a network of non-linear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximisation has extra properties not found in the linear case (Linsker 1989). The non-linearities in the transfer function are able to pick up higher-order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation. This enables the network to separate statistically independent components in the inputs: a higher-order generalisation of Principal Components Analysis. We apply the network to the source separation (or cocktail party) problem, successfully separating unknown mixtures of up to ten speakers. We also show that a variant on the network architecture is able to perform blind deconvolution (cancellation of unknown echoes and reverberation in a speech signal). Finally, we derive dependencies of information transfer on time delays. We suggest that information max-imisation provides a unifying framework for problems in `blind' signal processing. fl Please send comments to tony@salk.edu. This paper will appear as Neural Computation, 7, 6, 1004-1034 (1995). The reference for this version is: Technical Report no. INC-9501, February 1995, Institute for Neural Computation, UCSD, San Diego, CA 92093-0523. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27531. 	[Title] Using Sampling and Queries to Extract Rules from Trained Neural Networks [Abstract] Concepts learned by neural networks are difficult to understand because they are represented using large assemblages of real-valued parameters. One approach to understanding trained neural networks is to extract symbolic rules that describe their classification behavior. There are several existing rule-extraction approaches that operate by searching for such rules. We present a novel method that casts rule extraction not as a search problem, but instead as a learning problem. In addition to learning from training examples, our method exploits the property that networks can be efficiently queried. We describe algorithms for extracting both conjunctive and M -of-N rules, and present experiments that show that our method is more efficient than conventional search-based approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #7276. 	[Title] Self-Organization and Segmentation in a Laterally Connected Orientation Map of Spiking Neurons [Abstract] The RF-SLISSOM model integrates two separate lines of research on computational modeling of the visual cortex. Laterally connected self-organizing maps have been used to model how afferent structures such as orientation columns and patterned lateral connections can simultaneously self-organize through input-driven Hebbian adaptation. Spiking neurons with leaky integrator synapses have been used to model image segmentation and binding by synchronization and desynchronization of neuronal group activity. Although these approaches differ in how they model the neuron and what they explain, they share the same overall layout of a laterally connected two-dimensional network. This paper shows how both self-organization and segmentation can be achieved in such an integrated network, thus presenting a unified model of development and functional dynamics in the primary visual cortex. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #763181. 	[Title] A Neural Network Model of Visual Tilt Aftereffects [Abstract] RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map and its lateral connections are shown to result in tilt aftereffects over short time scales in the adult. The model allows observing large numbers of neurons and connections simultaneously, making it possible to relate higher-level phenomena to low-level events, which is difficult to do experimentally. The results give computational support for the idea that direct tilt aftereffects arise from adaptive lateral interactions between feature detectors, as has long been surmised. They also suggest that indirect effects could result from the conservation of synaptic resources during this process. The model thus provides a unified computational explanation of self-organization and both direct and indirect tilt aftereffects in the primary visual cortex. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136110. 	[Title] A Theory of Visual Relative Motion Perception: Grouping, Binding, and Gestalt Organization [Abstract] The human visual system is more sensitive to the relative motion of objects than to their absolute motion. An understanding of motion perception requires an understanding of how neural circuits can group moving visual elements relative to one another, based upon hierarchical reference frames. We have modeled visual relative motion perception using a neural network architecture that groups visual elements according to Gestalt common-fate principles and exploits information about the behavior of each group to predict the behavior of individual elements. A simple competitive neural circuit binds visual elements together into a representation of a visual object. Information about the spiking pattern of neurons allows transfer of the bindings of an object representation from location to location in the neural circuit as the object moves. The model exhibits characteristics of human object grouping and solves some key neural circuit design problems in visual relative motion perception. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #756061. 	[Title] In  Unsmearing Visual Motion: Development of Long-Range Horizontal Intrinsic Connections [Abstract] Human vision systems integrate information nonlocally, across long spatial ranges. For example, a moving stimulus appears smeared when viewed briefly (30 ms), yet sharp when viewed for a longer exposure (100 ms) (Burr, 1980). This suggests that visual systems combine information along a trajectory that matches the motion of the stimulus. Our self-organizing neural network model shows how developmental exposure to moving stimuli can direct the formation of horizontal trajectory-specific motion integration pathways that unsmear representations of moving stimuli. These results account for Burr's data and can potentially also model other phenomena, such as visual inertia. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136447. 	[Title] Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA [Abstract] Infusion of a GABA agonist (Reiter & Stryker, 1988) and infusion of an NMDA receptor antagonist (Bear et al., 1990), in the primary visual cortex of kittens during monocular deprivation, shifts ocular dominance toward the closed eye, in the cortical region near the infusion site. This reverse ocular dominance shift has been previously modeled by variants of a covariance synaptic plasticity rule (Bear et al., 1990; Clothiaux et al., 1991; Miller et al., 1989; Reiter & Stryker, 1988). Kasamatsu et al. (1997, 1998) showed that infusion of an NMDA receptor antagonist in adult cat primary visual cortex changes ocular dominance distribution, reduces binocularity, and reduces orientation and direction selectivity. This paper presents a novel account of the effects of these pharmacological treatments, based on the EXIN synaptic plasticity rules (Marshall, 1995), which include both an instar afferent excitatory and an outstar lateral inhibitory rule. Functionally, the EXIN plasticity rules enhance the efficiency, discrimination, and context-sensitivity of a neural network's representation of perceptual patterns (Marshall, 1995; Marshall & Gupta, 1998). The EXIN model decreases lateral inhibition from neurons outside the infusion site (control regions) to neurons inside the infusion region, during monocular deprivation. In the model, plasticity in afferent pathways to neurons affected by the pharmacological treatments is assumed to be blocked , as opposed to previous models (Bear et al., 1990; Miller et al., 1989; Reiter & Stryker, 1988), in which afferent pathways from the open eye to neurons in the infusion region are weakened . The proposed model is consistent with results suggesting that long-term plasticity can be blocked by NMDA antagonists or by postsynaptic hyperpolarization (Bear et al., 1990; Dudek & Bear, 1992; Goda & Stevens, 1996; Kirkwood et al., 1993). Since the role of plasticity in lateral inhibitory pathways in producing cortical plasticity has not received much attention, several predictions are made based on the EXIN lateral inhibitory plasticity rule. Please summarise all the given information.

Paper #1136446 has the title `The role of afferent excitatory and lateral inhibitory synaptic plasticity in visual cortical ocular dominance
` and the abstract `nan`.
Question: Which category should Paper #1136446 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1136446 has the title `The role of afferent excitatory and lateral inhibitory synaptic plasticity in visual cortical ocular dominance
` and the abstract `nan`.
Question: Which category should Paper #1136446 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