scholarly journals Intervening inhibition underlies simple-cell receptive field structure in visual cortex

2009 ◽  
Vol 13 (1) ◽  
pp. 89-96 ◽  
Author(s):  
Bao-hua Liu ◽  
Pingyang Li ◽  
Yujiao J Sun ◽  
Ya-tang Li ◽  
Li I Zhang ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Field Structure ◽  
Simple Cell

DEVELOPMENT OF FEEDFORWARD RECEPTIVE FIELD STRUCTURE OF A SIMPLE CELL AND ITS CONTRIBUTION TO THE ORIENTATION SELECTIVITY: A MODELING STUDY

2005 ◽  
Vol 15 (01n02) ◽  
pp. 55-70 ◽  
Author(s):  
AKHIL R GARG ◽  
KLAUS OBERMAYER ◽  
BASABI BHAUMIK
Keyword(s):  
Receptive Field ◽  
Field Structure ◽  
Orientation Selectivity ◽  
Simple Cell ◽  
Homogeneous State ◽  
Synaptic Efficacy ◽  
Synaptic Interactions ◽  
Experimental Findings ◽  
Modeling Study

Recent experimental studies of hetero-synaptic interactions in various systems have shown the role of signaling in the plasticity, challenging the conventional understanding of Hebb's rule. It has also been found that activity plays a major role in plasticity, with neurotrophins acting as molecular signals translating activity into structural changes. Furthermore, role of synaptic efficacy in biasing the outcome of competition has also been revealed recently. Motivated by these experimental findings we present a model for the development of simple cell receptive field structure based on the competitive hetero-synaptic interactions for neurotrophins combined with cooperative hetero-synaptic interactions in the spatial domain. We find that with proper balance in competition and cooperation, the inputs from two populations (ON/OFF) of LGN cells segregate starting from the homogeneous state. We obtain segregated ON and OFF regions in simple cell receptive field. Our modeling study supports the experimental findings, suggesting the role of synaptic efficacy and the role of spatial signaling. We find that using this model we obtain simple cell RF, even for positively correlated activity of ON/OFF cells. We also compare different mechanism of finding the response of cortical cell and study their possible role in the sharpening of orientation selectivity. We find that degree of selectivity improvement in individual cells varies from case to case depending upon the structure of RF field and type of sharpening mechanism.

Depth is encoded in the visual cortex by a specialized receptive field structure

Nature ◽  
1991 ◽  
Vol 352 (6331) ◽  
pp. 156-159 ◽  
Author(s):  
Gregory C. DeAngelis ◽  
Izumi Ohzawa ◽  
Ralph D. Freeman
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Field Structure

Receptive-field maps of correlated discharge between pairs of neurons in the cat's visual cortex

1994 ◽  
Vol 71 (1) ◽  
pp. 330-346 ◽  
Author(s):  
G. M. Ghose ◽  
I. Ohzawa ◽  
R. D. Freeman
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Correlation Analysis ◽  
Random Sequence ◽  
Receptive Fields ◽  
Simple Cell ◽  
Reverse Correlation ◽  
Sinusoidal Gratings ◽  
Reverse Correlation Analysis ◽  
Correlation Procedure

1. To investigate the functional significance of temporally correlated discharge between nearby cells in the visual cortex, we obtained receptive-field maps of correlated discharge for 68 cell pairs in kittens and cats. Discharge from cell pairs was measured by a single extracellular electrode. A reverse correlation procedure was used to relate neural discharge to particular stimuli within a random sequence of briefly flashed bright and dark bars. Bicellular receptive fields (BRFs) were mapped by applying reverse correlation to approximately synchronous discharge from two cells. Unicellular receptive fields (URFs) were simultaneously mapped by separately applying reverse correlation to the discharge of each cell. 2. The receptive fields of the two neurons within each pair were initially studied by varying the orientation and spatial frequency of drifting sinusoidal gratings. After these tests a random sequence of appropriately oriented bars was used to evoke discharge suitable for reverse correlation analysis. For most cell pairs, the temporal pattern or strength of correlated discharge produced by such stimulation is different from that observed with stimulation by sinusoidal gratings. This indicates that visually evoked correlated discharge between nearby cells is stimulus dependent. 3. BRFs were classified according to their pattern of spatial sensitivity into three groups that roughly correspond to the single-cell receptive-field types of the lateral geniculate nucleus (LGN; center-surround) and visual cortex (simple and complex). These classifications were compared with the receptive-field types of the single cells within each pair. LGN-type and simple-type BRFs were only seen for pairs in which at least one of the cells was simple. Conversely, complex-type BRFs were only seen for pairs in which at least one of the cells was complex. 4. Because the reverse correlation procedure can be used to characterize the spatiotemporal receptive-field structure of simple cells, we were able to compare both the spatial and temporal properties associated with the URFs and BRFs of simple cell pairs. The spatiotemporal structure of the BRF of a simple-cell pair can largely be predicted on the basis of the two URFs. Although this prediction suggests the possibility that BRFs are stimulus artifacts, a shuffle procedure, in which multiple repetitions of random sequences were presented, verifies the neural origin of BRFs. BRFs emerge from specific neural pathways and are not simply a consequence of unicellular response preferences. 5. Five measures were derived from the reverse correlation analysis of simple-cell receptive fields: width, duration, optimal spatial and temporal frequency, and optimal velocity.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Replicating Receptive Fields of Simple and Complex Cells in Primary Visual Cortex in a Neuronal Network Model with Temporal and Population Sparseness and Reliability

2012 ◽  
Vol 24 (10) ◽  
pp. 2700-2725 ◽  
Author(s):  
Takuma Tanaka ◽  
Toshio Aoyagi ◽  
Takeshi Kaneko
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Learning Rule ◽  
Simple Cell ◽  
Complex Cells ◽  
Receptive Field Properties ◽  
Output Neurons ◽  
Simple And Complex Cells

We propose a new principle for replicating receptive field properties of neurons in the primary visual cortex. We derive a learning rule for a feedforward network, which maintains a low firing rate for the output neurons (resulting in temporal sparseness) and allows only a small subset of the neurons in the network to fire at any given time (resulting in population sparseness). Our learning rule also sets the firing rates of the output neurons at each time step to near-maximum or near-minimum levels, resulting in neuronal reliability. The learning rule is simple enough to be written in spatially and temporally local forms. After the learning stage is performed using input image patches of natural scenes, output neurons in the model network are found to exhibit simple-cell-like receptive field properties. When the output of these simple-cell-like neurons are input to another model layer using the same learning rule, the second-layer output neurons after learning become less sensitive to the phase of gratings than the simple-cell-like input neurons. In particular, some of the second-layer output neurons become completely phase invariant, owing to the convergence of the connections from first-layer neurons with similar orientation selectivity to second-layer neurons in the model network. We examine the parameter dependencies of the receptive field properties of the model neurons after learning and discuss their biological implications. We also show that the localized learning rule is consistent with experimental results concerning neuronal plasticity and can replicate the receptive fields of simple and complex cells.

scholarly journals Receptive field structure in the visual cortex: does selective stimulation induce plasticity?

1995 ◽  
Vol 92 (21) ◽  
pp. 9682-9686 ◽  
Author(s):  
G. C. DeAngelis ◽  
A. Anzai ◽  
I. Ohzawa ◽  
R. D. Freeman
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Field Structure ◽  
Selective Stimulation
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