Computational Diversity in Complex Cells of Cat Primary Visual Cortex

Finn, Ian M.; Ferster, David

doi:10.1523/jneurosci.2119-07.2007

Cited by 32 publications

(17 citation statements)

References 30 publications

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“…In this context, there have been studies in which V1 complex cells were studied regarding the nature of pooling, i.e. whether the pooling is based on summation or MAX operation [39,40]. However, these studies examined neural responses to single and combined stimuli only, and not directly the properties of connections from simple cells to complex cells.…”

Section: (F ) Pooling In Actual Neurons and Artificial Neural Netsmentioning

confidence: 99%

Effects of generalized pooling on binocular disparity selectivity of neurons in the early visual cortex

Kato

Baba

Sasaki

et al. 2016

Phil. Trans. R. Soc. B

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One contribution of 15 to a theme issue 'Vision in our three-dimensional world'. The key problem of stereoscopic vision is traditionally defined as accurately finding the positional shifts of corresponding object features between left and right images. Here, we demonstrate that the problem must be considered in a four-dimensional parameter space; with respect not only to shifts in space (X, Y ), but also spatial frequency (SF) and orientation (OR). The proposed model sums outputs of binocular energy units linearly over the multi-dimensional V1 parameter space (X, Y, SF, OR). Theoretical analyses and physiological experiments show that many binocular neurons achieve sharp binocular tuning properties by pooling the output of multiple neurons with relatively broad tuning. Pooling in the space domain sharpens disparity-selective responses in the SF domain so that the responses to combinations of unmatched left-right SFs are attenuated. Conversely, pooling in the SF domain sharpens disparity selectivity in the space domain, reducing the possibility of false matches. Analogous effects are observed for the OR domain in that the spatial pooling sharpens the binocular tuning in the OR domain. Such neurons become selective to relative OR disparity. Therefore, pooling allows the visual system to refine binocular information into a form more desirable for stereopsis.This article is part of the themed issue 'Vision in our three-dimensional world'.

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Section: (F ) Pooling In Actual Neurons and Artificial Neural Netsmentioning

confidence: 99%

Effects of generalized pooling on binocular disparity selectivity of neurons in the early visual cortex

Kato

Baba

Sasaki

et al. 2016

Phil. Trans. R. Soc. B

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“…A better understanding of the elaboration of cortical neurons RFs requires the examination of their synaptic counterparts. Both the spatial and temporal organizations of cortical neurons RFs at the membrane potential level have been addressed in a number of studies (Borg-Graham et al 1998;Bringuier et al 1999;Cardin et al 2005;Creutzfeldt and Ito 1968;Ferster 1988;Finn et al 2007;Hirsch et al 1998Hirsch et al , 2002Hirsch et al , 2003Lampl et al 2001Lampl et al , 2004Martinez et al 2002Martinez et al , 2005Nelson et al 1994;Nowak et al 2005;Pei et al 1994; Priebe and Ferster 2005;Priebe et al 2004;Volgushev et al 1995Volgushev et al , 1996, yet these studies have usually provided either no or only partial examination of the relationship between synaptic and discharge RFs. A quantitative comparison of features of discharge and synaptic RFs in the same cells is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Features of Synaptic to Discharge Receptive Field Transformation in Cat Area 17

Nowak

Sanchez-Vives

McCormick

2010

Journal of Neurophysiology

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NowakLG, Sanchez-Vives MV, McCormick DA. Spatial and temporal features of synaptic to discharge receptive field transformation in cat area 17. J Neurophysiol 103: 677-697, 2010. First published November 11, 2009 doi:10.1152/jn.90946.2008. The aim of the present study was to characterize the spatial and temporal features of synaptic and discharge receptive fields (RFs), and to quantify their relationships, in cat area 17. For this purpose, neurons were recorded intracellularly while high-frequency flashing bars were used to generate RFs maps for synaptic and spiking responses. Comparison of the maps shows that some features of the discharge RFs depended strongly on those of the synaptic RFs, whereas others were less dependent. Spiking RF duration depended poorly and spiking RF amplitude depended moderately on those of the underlying synaptic RFs. At the other extreme, the optimal spatial frequency and phase of the discharge RFs in simple cells were almost entirely inherited from those of the synaptic RFs. Subfield width, in both simple and complex cells, was less for spiking responses compared with synaptic responses, but synaptic to discharge width ratio was relatively variable from cell to cell. When considering the whole RF of simple cells, additional variability in width ratio resulted from the presence of additional synaptic subfields that remained subthreshold. Due to these additional, subthreshold subfields, spatial frequency tuning predicted from synaptic RFs appears sharper than that predicted from spiking RFs. Excitatory subfield overlap in spiking RFs was well predicted by subfield overlap at the synaptic level. When examined in different regions of the RF, latencies appeared to be quite variable, but this variability showed negligible dependence on distance from the RF center. Nevertheless, spiking response latency faithfully reflected synaptic response latency.

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“…With respect to speech, this might involve the pooling of numerous, rigidly tuned representations of different exemplars of a given phoneme into an abstracted representation of the entire pool. Spatial pooling is well documented in visual cortex (7,31,32) and there is some evidence for its analog, spectrotemporal pooling, in auditory cortex (33)(34)(35), including the observation of complex cells when A1 is developmentally reprogrammed as a surrogate V1 (36). However, a formal equivalence is yet to be demonstrated (37,38).…”

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confidence: 99%

Phoneme and word recognition in the auditory ventral stream

DeWitt

Rauschecker²

2012

Proc. Natl. Acad. Sci. U.S.A.

409

328

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Spoken word recognition requires complex, invariant representations. Using a meta-analytic approach incorporating more than 100 functional imaging experiments, we show that preference for complex sounds emerges in the human auditory ventral stream in a hierarchical fashion, consistent with nonhuman primate electrophysiology. Examining speech sounds, we show that activation associated with the processing of short-timescale patterns (i.e., phonemes) is consistently localized to left mid-superior temporal gyrus (STG), whereas activation associated with the integration of phonemes into temporally complex patterns (i.e., words) is consistently localized to left anterior STG. Further, we show left midto anterior STG is reliably implicated in the invariant representation of phonetic forms and that this area also responds preferentially to phonetic sounds, above artificial control sounds or environmental sounds. Together, this shows increasing encoding specificity and invariance along the auditory ventral stream for temporally complex speech sounds.functional MRI | meta-analysis | auditory cortex | object recognition | language S poken word recognition presents several challenges to the brain. Two key challenges are the assembly of complex auditory representations and the variability of natural speech (SI Appendix, Fig. S1) (1). Representation at the level of primary auditory cortex is precise: fine-grained in scale and local in spectrotemporal space (2, 3). The recognition of complex spectrotemporal forms, like words, in higher areas of auditory cortex requires the transformation of this granular representation into Gestalt-like, object-centered representations. In brief, local features must be bound together to form representations of complex spectrotemporal contours, which are themselves the constituents of auditory "objects" or complex sound patterns (4, 5). Next, representations must be generalized and abstracted. Coding in primary auditory cortex is sensitive even to minor physical transformations. Object-centered coding in higher areas, however, must be invariant (i.e., tolerant of natural stimulus variation) (6). For example, whereas the phonemic structure of a word is fixed, there is considerable variation in physical, spectrotemporal form-attributable to accent, pronunciation, body size, and the like-among utterances of a given word. It has been proposed for visual cortical processing that a feed-forward, hierarchical architecture (7) may be capable of simultaneously solving the problems of complexity and variability (8-12). Here, we examine these ideas in the context of auditory cortex.In a hierarchical pattern-recognition scheme (8), coding in the earliest cortical field would reflect the tuning and organization of primary auditory cortex (or core) (2, 3, 13). That is, single-neuron receptive fields (more precisely, frequency-response areas) would be tuned to particular center frequencies and would have minimal spectrotemporal complexity (i.e., a single excitatory zone and one-to-two inhibitory side bands). ...

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Computational Diversity in Complex Cells of Cat Primary Visual Cortex

Cited by 32 publications

References 30 publications

Effects of generalized pooling on binocular disparity selectivity of neurons in the early visual cortex

Effects of generalized pooling on binocular disparity selectivity of neurons in the early visual cortex

Spatial and Temporal Features of Synaptic to Discharge Receptive Field Transformation in Cat Area 17

Phoneme and word recognition in the auditory ventral stream

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