Book contents
- Frontmatter
- Contents
- Contributors
- Introduction: Modelling perception with artificial neural networks
- Part I General themes
- Part II The use of artificial neural networks to elucidate the nature of perceptual processes in animals
- 3 Correlation versus gradient type motion detectors: the pros and cons
- 4 Spatial constancy and the brain: insights from neural networks
- 5 The interplay of Pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat
- 6 Evolution, (sequential) learning and generalisation in modular and nonmodular visual neural networks
- 7 Effects of network structure on associative memory
- 8 Neural networks and neuro-oncology: the complex interplay between brain tumour, epilepsy and cognition
- Part III Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
- Part IV Methodological issues in the use of simple feedforward networks
- Index
- References
4 - Spatial constancy and the brain: insights from neural networks
from Part II - The use of artificial neural networks to elucidate the nature of perceptual processes in animals
Published online by Cambridge University Press: 05 July 2011
Book contents
- Frontmatter
- Contents
- Contributors
- Introduction: Modelling perception with artificial neural networks
- Part I General themes
- Part II The use of artificial neural networks to elucidate the nature of perceptual processes in animals
- 3 Correlation versus gradient type motion detectors: the pros and cons
- 4 Spatial constancy and the brain: insights from neural networks
- 5 The interplay of Pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat
- 6 Evolution, (sequential) learning and generalisation in modular and nonmodular visual neural networks
- 7 Effects of network structure on associative memory
- 8 Neural networks and neuro-oncology: the complex interplay between brain tumour, epilepsy and cognition
- Part III Artificial neural networks as models of perceptual processing in ecology and evolutionary biology
- Part IV Methodological issues in the use of simple feedforward networks
- Index
- References
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- Modelling Perception with Artificial Neural Networks , pp. 74 - 92Publisher: Cambridge University PressPrint publication year: 2010
References
, , , & 1990. Eye position effects on visual, memory, and saccade-related activity in areas LIP and 7a of macaque. J Neurosci 10, 1176–1196.CrossRefGoogle ScholarPubMed
, & 1992. Evidence for the lateral intraparietal area as the parietal eye field. Curr Opin Neurobiol 2, 840–846.CrossRefGoogle ScholarPubMed
& 1983. The influence of the angle of gaze upon the excitability of the light-sensitive neurons of the posterior parietal cortex. J Neurosci 3, 532–548.CrossRefGoogle ScholarPubMed
, & 2003. Spatial memory following shifts of gaze. I. Saccades to memorized world-fixed and gaze-fixed targets. J Neurophysiol 89, 2564–2576.CrossRefGoogle ScholarPubMed
, & 2002. Reference frames and spatial memory operations: area LIP and saccade behavior. Soc Neurosci Abstr 57.16.Google Scholar
& 2003. Effects of gaze shifts on maintenance of spatial memory in macaque frontal eye field. J Neurosci 23, 5446–5454.CrossRefGoogle ScholarPubMed
1995. A review of the role of efference copy in sensory and oculomotor control systems. Ann Biomed Eng 23, 409–422.CrossRefGoogle ScholarPubMed
, , & 1992. The analysis of visual motion: a comparison of neuronal and psychophysical performance. J Neurosci 12, 4745–4765.CrossRefGoogle ScholarPubMed
& 2004. Vector subtraction using gain fields in the frontal eye fields of macaque monkeys. Soc Neurosci Abstr 186.11.Google Scholar
, & 1995. Oculocentric spatial representation in parietal cortex. Cereb Cortex 5, 470–481.CrossRefGoogle ScholarPubMed
, & 1998. Immediate post-saccadic information mediates space constancy. Vision Res 38, 3147–3159.CrossRefGoogle ScholarPubMed
& 1991. A neural network model of sensoritopic maps with predictive short-term memory properties. Proc Natl Acad Sci USA 88, 9653–9657.CrossRefGoogle ScholarPubMed
, , & 1997. Spatial invariance of visual receptive fields in parietal cortex neurons. Nature 389, 845–848.CrossRefGoogle ScholarPubMed
, & 1993. Parietal neurons encoding spatial locations in craniotopic coordinates. Exp Brain Res 96, 221–229.CrossRefGoogle ScholarPubMed
& 1988. Memory related motor planning activity in posterior parietal cortex of macaque. Exp Brain Res 70, 216–220.Google ScholarPubMed
, & 1983. Corollary discharge provides accurate eye position information to the oculomotor system. Science 221, 1193–1195.CrossRefGoogle ScholarPubMed
& 1950. The reafferent principle: reciprocal effects between central nervous system and periphery. Naturwissenschaften 37, 464–476.Google Scholar
, , & 2004. Frames of reference for eye-head gaze commands in primate supplementary eye fields. Neuron 44, 1057–1066.CrossRefGoogle ScholarPubMed
& 2001. A model of visual-spatial memory across saccades. Vision Res 41, 1575–1592.
2003. Brain representation of object-centered space in monkeys and humans. Annu Rev Neurosci 26, 331–354.CrossRefGoogle ScholarPubMed
, & 2005. Effects of electrical microstimulation in monkey frontal eye field on saccades to remembered targets. Vision Res 45, 3414–3429.CrossRefGoogle ScholarPubMed
, Schlag- & 2006. Frames of reference for saccadic command, tested by saccade collision in the supplementary eye field. J Neurophysiol 95, 159–170.CrossRefGoogle ScholarPubMed
, & 1986. Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition (ed. & ), pp. 316–362. MIT Press.Google Scholar
& 2005. Distributed population mechanism for the 3-D oculomotor reference frame transformation. J Neurophysiol 93, 1742–1761.CrossRefGoogle ScholarPubMed
, , & 1998. Separate body- and world-referenced representations of visual space in parietal cortex. Nature 394, 887–891.CrossRefGoogle ScholarPubMed
& 2002. A pathway in primate brain for internal monitoring of movements. Science 296, 1480–1482.CrossRefGoogle ScholarPubMed
& 1983. Role of corollary discharge in space constancy. Percept Psychophys 34, 371–380.CrossRefGoogle ScholarPubMed
, 3rd & 2004. A neural network model of flexible spatial updating. J Neurophysiol 91, 1608–1619.CrossRefGoogle ScholarPubMed
& 2007. Subthreshold microstimulation in frontal eye fields updates spatial memories. Exp Brain Res. 181, 477–92.CrossRefGoogle ScholarPubMed
& 1995. Gradient-based learning algorithms for recurrent neural networks. In Back-Propagation: Theory, Architecture and Applications (ed. & ), pp. 433–486. Lawrence Erlbaum.Google Scholar
& 2000. Memory activity of LIP neurons for sequential eye movements simulated with neural networks. J Neurophysiol 84, 651–665.CrossRefGoogle ScholarPubMed
& 1988. A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons. Nature 331, 679–684.CrossRefGoogle ScholarPubMed