Limits...
Model-based analysis of pattern motion processing in mouse primary visual cortex.

Muir DR, Roth MM, Helmchen F, Kampa BM - Front Neural Circuits (2015)

Bottom Line: We also found a large proportion of cells that respond strongly to only one stimulus class.Our results show that a broad range of pattern integration processes already take place at the level of V1.This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zürich Zürich, Switzerland ; Biozentrum, University of Basel Basel, Switzerland.

ABSTRACT
Neurons in sensory areas of neocortex exhibit responses tuned to specific features of the environment. In visual cortex, information about features such as edges or textures with particular orientations must be integrated to recognize a visual scene or object. Connectivity studies in rodent cortex have revealed that neurons make specific connections within sub-networks sharing common input tuning. In principle, this sub-network architecture enables local cortical circuits to integrate sensory information. However, whether feature integration indeed occurs locally in rodent primary sensory areas has not been examined directly. We studied local integration of sensory features in primary visual cortex (V1) of the mouse by presenting drifting grating and plaid stimuli, while recording the activity of neuronal populations with two-photon calcium imaging. Using a Bayesian model-based analysis framework, we classified single-cell responses as being selective for either individual grating components or for moving plaid patterns. Rather than relying on trial-averaged responses, our model-based framework takes into account single-trial responses and can easily be extended to consider any number of arbitrary predictive models. Our analysis method was able to successfully classify significantly more responses than traditional partial correlation (PC) analysis, and provides a rigorous statistical framework to rank any number of models and reject poorly performing models. We also found a large proportion of cells that respond strongly to only one stimulus class. In addition, a quarter of selectively responding neurons had more complex responses that could not be explained by any simple integration model. Our results show that a broad range of pattern integration processes already take place at the level of V1. This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features.

No MeSH data available.


Related in: MedlinePlus

Pattern integration and hypothesized sub-network circuitry. (A) Schematic network diagram showing excitatory sub-networks integrating information from two grating components. (B–D) Simple integration of tuned input components (shaded areas) can produce a wide range of responses to both gratings (solid curves) and plaids (dashed curves). (B) A classical component cell produced by a single narrow-bandwidth orientation- or direction- tuned input (orange shading). Direction-tuned input and responses are shown over 180° of orientation, to match our experimental paradigm. Shown to the right is a neuron with this class of response (see also trace I in Figure 1D). (C) An example cell that integrates two narrow-bandwidth input components tuned to two different directions (orange and green shading). Shown to the right is a neuron with this class of response. (D) Integration of broadly tuned input components (orange and green shading), produces a broadly tuned “pattern” cell response to drifting gratings (blue curve) and plaid (red curve); shown to the right is a neuron with this class of response (see also pattern classified cell c2 in Figure 1D).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4525018&req=5

Figure 6: Pattern integration and hypothesized sub-network circuitry. (A) Schematic network diagram showing excitatory sub-networks integrating information from two grating components. (B–D) Simple integration of tuned input components (shaded areas) can produce a wide range of responses to both gratings (solid curves) and plaids (dashed curves). (B) A classical component cell produced by a single narrow-bandwidth orientation- or direction- tuned input (orange shading). Direction-tuned input and responses are shown over 180° of orientation, to match our experimental paradigm. Shown to the right is a neuron with this class of response (see also trace I in Figure 1D). (C) An example cell that integrates two narrow-bandwidth input components tuned to two different directions (orange and green shading). Shown to the right is a neuron with this class of response. (D) Integration of broadly tuned input components (orange and green shading), produces a broadly tuned “pattern” cell response to drifting gratings (blue curve) and plaid (red curve); shown to the right is a neuron with this class of response (see also pattern classified cell c2 in Figure 1D).

Mentions: In order to detect the motion of the plaid stimuli, pattern cells must integrate inputs tuned over a broad range of orientations (see also Figure 6). Hence, if pattern integration occurs locally, the tuning of pattern cells to individual gratings should also be broader. We tested this hypothesis by measuring a tuning SI for the classified neurons over the set of grating stimuli (Figure 4B; see Methods). As expected, we found that pattern-classified neurons are significantly more broadly tuned to the orientation of grating stimuli than component-classified neurons (Figure 4B; median SIg of 0.6 vs. 0.7; p < 0.01, rank-sum test) as previously suggested without being quantified for primate area MT (Rust et al., 2006). Pattern-classified cells integrate inputs from the individual components of the presented plaid patterns, resulting in broader orientation tuning when tested with individual drifting gratings.


Model-based analysis of pattern motion processing in mouse primary visual cortex.

Muir DR, Roth MM, Helmchen F, Kampa BM - Front Neural Circuits (2015)

Pattern integration and hypothesized sub-network circuitry. (A) Schematic network diagram showing excitatory sub-networks integrating information from two grating components. (B–D) Simple integration of tuned input components (shaded areas) can produce a wide range of responses to both gratings (solid curves) and plaids (dashed curves). (B) A classical component cell produced by a single narrow-bandwidth orientation- or direction- tuned input (orange shading). Direction-tuned input and responses are shown over 180° of orientation, to match our experimental paradigm. Shown to the right is a neuron with this class of response (see also trace I in Figure 1D). (C) An example cell that integrates two narrow-bandwidth input components tuned to two different directions (orange and green shading). Shown to the right is a neuron with this class of response. (D) Integration of broadly tuned input components (orange and green shading), produces a broadly tuned “pattern” cell response to drifting gratings (blue curve) and plaid (red curve); shown to the right is a neuron with this class of response (see also pattern classified cell c2 in Figure 1D).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4525018&req=5

Figure 6: Pattern integration and hypothesized sub-network circuitry. (A) Schematic network diagram showing excitatory sub-networks integrating information from two grating components. (B–D) Simple integration of tuned input components (shaded areas) can produce a wide range of responses to both gratings (solid curves) and plaids (dashed curves). (B) A classical component cell produced by a single narrow-bandwidth orientation- or direction- tuned input (orange shading). Direction-tuned input and responses are shown over 180° of orientation, to match our experimental paradigm. Shown to the right is a neuron with this class of response (see also trace I in Figure 1D). (C) An example cell that integrates two narrow-bandwidth input components tuned to two different directions (orange and green shading). Shown to the right is a neuron with this class of response. (D) Integration of broadly tuned input components (orange and green shading), produces a broadly tuned “pattern” cell response to drifting gratings (blue curve) and plaid (red curve); shown to the right is a neuron with this class of response (see also pattern classified cell c2 in Figure 1D).
Mentions: In order to detect the motion of the plaid stimuli, pattern cells must integrate inputs tuned over a broad range of orientations (see also Figure 6). Hence, if pattern integration occurs locally, the tuning of pattern cells to individual gratings should also be broader. We tested this hypothesis by measuring a tuning SI for the classified neurons over the set of grating stimuli (Figure 4B; see Methods). As expected, we found that pattern-classified neurons are significantly more broadly tuned to the orientation of grating stimuli than component-classified neurons (Figure 4B; median SIg of 0.6 vs. 0.7; p < 0.01, rank-sum test) as previously suggested without being quantified for primate area MT (Rust et al., 2006). Pattern-classified cells integrate inputs from the individual components of the presented plaid patterns, resulting in broader orientation tuning when tested with individual drifting gratings.

Bottom Line: We also found a large proportion of cells that respond strongly to only one stimulus class.Our results show that a broad range of pattern integration processes already take place at the level of V1.This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zürich Zürich, Switzerland ; Biozentrum, University of Basel Basel, Switzerland.

ABSTRACT
Neurons in sensory areas of neocortex exhibit responses tuned to specific features of the environment. In visual cortex, information about features such as edges or textures with particular orientations must be integrated to recognize a visual scene or object. Connectivity studies in rodent cortex have revealed that neurons make specific connections within sub-networks sharing common input tuning. In principle, this sub-network architecture enables local cortical circuits to integrate sensory information. However, whether feature integration indeed occurs locally in rodent primary sensory areas has not been examined directly. We studied local integration of sensory features in primary visual cortex (V1) of the mouse by presenting drifting grating and plaid stimuli, while recording the activity of neuronal populations with two-photon calcium imaging. Using a Bayesian model-based analysis framework, we classified single-cell responses as being selective for either individual grating components or for moving plaid patterns. Rather than relying on trial-averaged responses, our model-based framework takes into account single-trial responses and can easily be extended to consider any number of arbitrary predictive models. Our analysis method was able to successfully classify significantly more responses than traditional partial correlation (PC) analysis, and provides a rigorous statistical framework to rank any number of models and reject poorly performing models. We also found a large proportion of cells that respond strongly to only one stimulus class. In addition, a quarter of selectively responding neurons had more complex responses that could not be explained by any simple integration model. Our results show that a broad range of pattern integration processes already take place at the level of V1. This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features.

No MeSH data available.


Related in: MedlinePlus