A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons

Zipser, David; Andersen, Richard A.

doi:10.1038/331679a0

Cited by 1,099 publications

(800 citation statements)

References 14 publications

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“…We build on earlier DNF models of multi-item visual working memory, which represent memory items through self-sustained peaks of activity in neural populations (Johnson et al 2008). We combine this type of representation with a mechanism for reference frame transformation that emulates the response properties of gain-modulates neurons in the parietal cortex (Zipser and Andersen 1988). Implementing that transformation mechanism within the framework of DNFs, we account for the continuous evolution of neural activation that follows visual stimulation and changes of gaze direction.…”

Section: Discussionmentioning

confidence: 99%

“…2c), with the latter acting as a common element of the two parts. This module is analogous to previous models of reference frame transformation (Zipser and Andersen 1988;Pouget and Sejnowski 1997), but it extends them to allow the parallel mapping of multiple locations from the retinocentric to the body-centered representation and back.…”

Section: Overview Over the Dnf Architecturementioning

confidence: 99%

“…Similar response properties have also been described for neurons in the frontal eye field (FEF, Cassanello and Ferrera 2007). In a neural network model, Zipser and Andersen (1988) have shown how a population of such gain-modulated neurons could perform a transformation of eye-centered visual input into a head-centered reference frame, given the current eye position. This mechanism has later been formalized and extended to multi-directional transformations by Pouget and colleagues (Pouget and Sejnowski 1997;Denève et al 2001).…”

Section: Introductionmentioning

confidence: 99%

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A neural mechanism for coordinate transformation predicts pre-saccadic remapping

Schneegans

Schöner

2012

Biol Cybern

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Whenever we shift our gaze, any location information encoded in the retinocentric reference frame that is predominant in the visual system is obliterated. How is spatial memory retained across gaze changes? Two different explanations have been proposed: Retinocentric information may be transformed into a gaze-invariant representation through a mechanism consistent with gain fields observed in parietal cortex, or retinocentric information may be updated in anticipation of the shift expected with every gaze change, a proposal consistent with neural observations in LIP. The explanations were considered incompatible with each other, because retinocentric update is observed before the gaze shift has terminated. Here, we show that a neural dynamic mechanism for coordinate transformation can also account for retinocentric updating. Our model postulates an extended mechanism of reference frame transformation that is based on bidirectional mapping between a retinocentric and a bodycentered representation and that enables transforming multiple object locations in parallel. The dynamic coupling between the two reference frames generates a shift of the retinocentric representation for every gaze change. We account for the predictive nature of the observed remapping activity by using the same kind of neural mechanism to generate an internal representation of gaze direction that is predictively updated based on corollary discharge signals. We provide evidence for the model by accounting for a series of behavioral and neural experimental observations.

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Section: Discussionmentioning

confidence: 99%

Section: Overview Over the Dnf Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A neural mechanism for coordinate transformation predicts pre-saccadic remapping

Schneegans

Schöner

2012

Biol Cybern

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“…Modulation of receptive fields is an effective way of handling information about multiple spatial locations or directions (Zipser and Andersen, 1988;Salinas and Abbott, 1995;Sejnowski, 1994, 1996). Experimentally, modulation has been seen, for example, in parietal areas (Andersen and Mountcastle, 1983;Andersen et al, 1985).…”

Section: Introductionmentioning

confidence: 99%

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Gerstner

Abbott

1997

Journal of Computational Neuroscience

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Abstract. We analyze a model of navigational map formation based on correlation-based, temporally asymmetric potentiation and depression of synapses between hippocampal place cells. We show that synaptic modification during random exploration of an environment shifts the location encoded by place cell activity in such a way that it indicates the direction from any location to a fixed target avoiding walls and other obstacles. Multiple maps to different targets can be simultaneously stored if we introduce target-dependent modulation of place cell activity. Once maps to a number of target locations in a given environment have been stored, novel maps to previously unknown target locations are automatically constructed by interpolation between existing maps.

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“…Moreover, this transformation is achieved by a distributed processing mechanism in a feedforward network (Lockery & Kristan, 1990b). Thus, both functionally and mechanistically, the local bending reflex shares essential features of sensorimotor integration (Anastasio & Robinson, 1990) or spatial transformation (Zipser & Andersen, 1988) in vertebrate systems. Because vertebrate networks often contain many more neurons and connections than the local bending net-work, it is conceivable that theoretically minimal engrams in these instances are even more widely distributed.…”

Section: Relevance To Other Systemsmentioning

confidence: 99%

A lower bound on the detectability of nonassociative learning in the local bending reflex of the medicinal leech

Lockery¹,

Sejnowski²

1993

Behavioral and Neural Biology

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Studies of neural mechanisms of learning and memory have focused on large changes at identified synapses. However, memory in distributed processing reflexes could involve widely distributed engrams characterized by small changes at every synapse in the network. To investigate this possibility, we used a neural network optimization algorithm to construct distributed engrams for nonassociative conditioning in a model of the local bending reflex of the medicinal leech (Hirudo medicinalis). The model comprised 4 sensory neurons, 10 to 40 interneurons, 8 motor neurons, and up to 480 connections. Synaptic connections in the model were first optimized to reproduce the amplitude and time course of motor neuron synaptic potentials recorded during local bending. This network, which represented the naive state before conditioning, was then reoptimized to the habituated or sensitized state. Following reoptimization, the memory for nonassociative learning was encoded by small changes dispersed across the entire network, and each change made only a small contribution to the learning. Moreover, because the changes were small, resolution of a few tenths of a millivolt, or 3-5% of an average synaptic potential, would be needed to account for half of the nonassociative learning. These results show how difficult distributed engrams can be to detect and provide a likely lower bound on the detectability ofnonassociative learning in this and related networks.

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A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons

Cited by 1,099 publications

References 14 publications

A neural mechanism for coordinate transformation predicts pre-saccadic remapping

A neural mechanism for coordinate transformation predicts pre-saccadic remapping

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A lower bound on the detectability of nonassociative learning in the local bending reflex of the medicinal leech

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