scholarly journals Mechanisms underlying contrast-dependent orientation selectivity in mouse V1

2018 ◽  
Vol 115 (45) ◽  
pp. 11619-11624 ◽  
Author(s):  
Wei P. Dai ◽  
Douglas Zhou ◽  
David W. McLaughlin ◽  
David Cai
Keyword(s):  
Large Scale ◽  
Feedback Inhibition ◽  
Orientation Selectivity ◽  
Inhibitory Neurons ◽  
Orientation Tuning ◽  
Response Properties ◽  
Firing Rates ◽  
Excitatory Neurons ◽  
Cortical Excitation ◽  
Contrast Invariance

Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties—one of which is that contrast invariance in the orientation selectivity (OS) of the neurons’ firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties—for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance—providing insights for future studies on contrast dependence (invariance).

Bridging the Functional and Wiring Properties of V1 Neurons Through Sparse Coding

2021 ◽  
pp. 1-34
Author(s):  
Xiaolin Hu ◽  
Zhigang Zeng
Keyword(s):  
Sparse Coding ◽  
Hebbian Learning ◽  
Inhibitory Neuron ◽  
Learning Rule ◽  
Orientation Selectivity ◽  
Inhibitory Neurons ◽  
Natural Scenes ◽  
Firing Rates ◽  
V1 Neurons ◽  
Excitatory Neurons

Abstract The functional properties of neurons in the primary visual cortex (V1) are thought to be closely related to the structural properties of this network, but the specific relationships remain unclear. Previous theoretical studies have suggested that sparse coding, an energy-efficient coding method, might underlie the orientation selectivity of V1 neurons. We thus aimed to delineate how the neurons are wired to produce this feature. We constructed a model and endowed it with a simple Hebbian learning rule to encode images of natural scenes. The excitatory neurons fired sparsely in response to images and developed strong orientation selectivity. After learning, the connectivity between excitatory neuron pairs, inhibitory neuron pairs, and excitatory-inhibitory neuron pairs depended on firing pattern and receptive field similarity between the neurons. The receptive fields (RFs) of excitatory neurons and inhibitory neurons were well predicted by the RFs of presynaptic excitatory neurons and inhibitory neurons, respectively. The excitatory neurons formed a small-world network, in which certain local connection patterns were significantly overrepresented. Bidirectionally manipulating the firing rates of inhibitory neurons caused linear transformations of the firing rates of excitatory neurons, and vice versa. These wiring properties and modulatory effects were congruent with a wide variety of data measured in V1, suggesting that the sparse coding principle might underlie both the functional and wiring properties of V1 neurons.

scholarly journals Cortical circuits implement optimal context integration

2017 ◽  
Author(s):  
Ramakrishnan Iyer ◽  
Stefan Mihalas
Keyword(s):  
Receptive Field ◽  
Large Scale ◽  
Inhibitory Neuron ◽  
Visual Input ◽  
Inhibitory Neurons ◽  
Specific Cell ◽  
Natural Scene ◽  
Cortical Circuits ◽  
Classical Receptive Field ◽  
Excitatory Neurons

Neurons in the primary visual cortex (V1) predominantly respond to a patch of the visual input, their classical receptive field. These responses are modulated by the visual input in the surround [2]. This reflects the fact that features in natural scenes do not occur in isolation: lines, surfaces are generally continuous, and the surround provides context for the information in the classical receptive field. It is generally assumed that the information in the near surround is transmitted via lateral connections between neurons in the same area [2]. A series of large scale efforts have recently described the relation between lateral connectivity and visual evoked responses and found like-to-like connectivity between excitatory neurons [16, 18]. Additionally, specific cell type connectivity for inhibitory neuron types has been described [11, 31]. Current normative models of cortical function relying on sparsity [27], saliency [4] predict functional inhibition between similarly tuned neurons. What computations are consistent with the observed structure of the lateral connections between the excitatory and diverse types of inhibitory neurons?We combined natural scene statistics [24] and mouse V1 neuron responses [7] to compute the lateral connections and computations of individual neurons which optimally integrate information from the classical receptive field with that from the surround by directly implementing Bayes rule. This increases the accuracy of representation of a natural scene under noisy conditions. We show that this network has like-to-like connectivity between excitatory neurons, similar to the observed one [16, 18, 11], and has three types of inhibition: local normalization, surround inhibition and gating of inhibition from the surround - that can be attributed to three classes of inhibitory neurons. We hypothesize that this computation: optimal integration of contextual cues with a gate to ignore context when necessary is a general property of cortical circuits, and the rules constructed for mouse V1 generalize to other areas and species.

scholarly journals Stimulus selectivity and response latency in putative inhibitory and excitatory neurons of the primate inferior temporal cortex

2012 ◽  
Vol 108 (10) ◽  
pp. 2725-2736 ◽  
Author(s):  
Ryan E. B. Mruczek ◽  
David L. Sheinberg
Keyword(s):  
Functional Properties ◽  
Response Latency ◽  
Visual Processing ◽  
Temporal Cortex ◽  
Cell Types ◽  
Inferior Temporal Cortex ◽  
Inhibitory Neurons ◽  
Response Latencies ◽  
Firing Rates ◽  
Excitatory Neurons

The cerebral cortex is composed of many distinct classes of neurons. Numerous studies have demonstrated corresponding differences in neuronal properties across cell types, but these comparisons have largely been limited to conditions outside of awake, behaving animals. Thus the functional role of the various cell types is not well understood. Here, we investigate differences in the functional properties of two widespread and broad classes of cells in inferior temporal cortex of macaque monkeys: inhibitory interneurons and excitatory projection cells. Cells were classified as putative inhibitory or putative excitatory neurons on the basis of their extracellular waveform characteristics (e.g., spike duration). Consistent with previous intracellular recordings in cortical slices, putative inhibitory neurons had higher spontaneous firing rates and higher stimulus-evoked firing rates than putative excitatory neurons. Additionally, putative excitatory neurons were more susceptible to spike waveform adaptation following very short interspike intervals. Finally, we compared two functional properties of each neuron's stimulus-evoked response: stimulus selectivity and response latency. First, putative excitatory neurons showed stronger stimulus selectivity compared with putative inhibitory neurons. Second, putative inhibitory neurons had shorter response latencies compared with putative excitatory neurons. Selectivity differences were maintained and latency differences were enhanced during a visual search task emulating more natural viewing conditions. Our results suggest that short-latency inhibitory responses are likely to sculpt visual processing in excitatory neurons, yielding a sparser visual representation.

scholarly journals Feedback inhibition and its control in an insect olfactory circuit

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Subhasis Ray ◽  
Zane N Aldworth ◽  
Mark A Stopfer
Keyword(s):  
Mushroom Body ◽  
Large Scale ◽  
Feedback Inhibition ◽  
Temporal Patterns ◽  
Inhibitory Neurons ◽  
Biophysical Model ◽  
Olfactory Pathway ◽  
Olfactory Coding ◽  
Olfactory Responses ◽  
Electrophysiological Recordings

Inhibitory neurons play critical roles in regulating and shaping olfactory responses in vertebrates and invertebrates. In insects, these roles are performed by relatively few neurons, which can be interrogated efficiently, revealing fundamental principles of olfactory coding. Here, with electrophysiological recordings from the locust and a large-scale biophysical model, we analyzed the properties and functions of GGN, a unique giant GABAergic neuron that plays a central role in structuring olfactory codes in the locust mushroom body. Our simulations suggest that depolarizing GGN at its input branch can globally inhibit KCs several hundred microns away. Our in vivorecordings show that GGN responds to odors with complex temporal patterns of depolarization and hyperpolarization that can vary with odors and across animals, leading our model to predict the existence of a yet-undiscovered olfactory pathway. Our analysis reveals basic new features of GGN and the olfactory network surrounding it.

scholarly journals Analysis of the Stabilized Supralinear Network

2013 ◽  
Vol 25 (8) ◽  
pp. 1994-2037 ◽  
Author(s):  
Yashar Ahmadian ◽  
Daniel B. Rubin ◽  
Kenneth D. Miller
Keyword(s):  
Feedback Inhibition ◽  
Power Laws ◽  
Dynamic Stabilization ◽  
Inhibitory Neurons ◽  
Input Output ◽  
Firing Rates ◽  
Linear Behavior ◽  
Wide Range ◽  
Inhibitory Population ◽  
Balanced Network

We study a rate-model neural network composed of excitatory and inhibitory neurons in which neuronal input-output functions are power laws with a power greater than 1, as observed in primary visual cortex. This supralinear input-output function leads to supralinear summation of network responses to multiple inputs for weak inputs. We show that for stronger inputs, which would drive the excitatory subnetwork to instability, the network will dynamically stabilize provided feedback inhibition is sufficiently strong. For a wide range of network and stimulus parameters, this dynamic stabilization yields a transition from supralinear to sublinear summation of network responses to multiple inputs. We compare this to the dynamic stabilization in the balanced network, which yields only linear behavior. We more exhaustively analyze the two-dimensional case of one excitatory and one inhibitory population. We show that in this case, dynamic stabilization will occur whenever the determinant of the weight matrix is positive and the inhibitory time constant is sufficiently small, and analyze the conditions for supersaturation, or decrease of firing rates with increasing stimulus contrast (which represents increasing input firing rates). In work to be presented elsewhere, we have found that this transition from supralinear to sublinear summation can explain a wide variety of nonlinearities in cerebral cortical processing.

scholarly journals Receptive Fields and Response Properties of Neurons in Layer 4 of Ferret Visual Cortex

2003 ◽  
Vol 89 (2) ◽  
pp. 1003-1015 ◽  
Author(s):  
W. Martin Usrey ◽  
Michael P. Sceniak ◽  
Barbara Chapman
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Receptive Fields ◽  
Field Structure ◽  
Orientation Selectivity ◽  
Stimulus Orientation ◽  
Orientation Tuning ◽  
Simple Cells ◽  
Response Properties ◽  
Layer 4

The ferret has become a model animal for studies exploring the development of the visual system. However, little is known about the receptive-field structure and response properties of neurons in the adult visual cortex of the ferret. We performed single-unit recordings from neurons in layer 4 of adult ferret primary visual cortex to determine the receptive-field structure and visual-response properties of individual neurons. In particular, we asked what is the spatiotemporal structure of receptive fields of layer 4 neurons and what is the orientation selectivity of layer 4 neurons? Receptive fields of layer 4 neurons were mapped using a white-noise stimulus; orientation selectivity was determined using drifting, sine-wave gratings. Our results show that most neurons (84%) within layer 4 are simple cells with elongated, spatially segregated,on and off subregions. These neurons are also selective for stimulus orientation; peaks in orientation-tuning curves have, on average, a half-width at half-maximum response of 21.5 ± 1.2° (mean ± SD). The remaining neurons in layer 4 (16%) lack orientation selectivity and have center/surround receptive fields. Although the organization of geniculate inputs to layer 4 differs substantially between ferret and cat, our results demonstrate that, like in the cat, most neurons in ferret layer 4 are orientation-selective simple cells.

scholarly journals Cell type specific information transfer for sparse coding

2020 ◽  
Author(s):  
Fleur Zeldenrust ◽  
Niccolò Calcini ◽  
Xuan Yan ◽  
Ate Bijlsma ◽  
Tansu Celikel
Keyword(s):  
Sparse Coding ◽  
Information Transfer ◽  
Spike Trains ◽  
Inhibitory Neurons ◽  
Specific Information ◽  
Cell Type ◽  
Sensory Stimuli ◽  
Firing Rates ◽  
Excitatory Neurons ◽  
Cell Type Specific

AbstractSensory neurons reconstruct the world from action potentials (spikes) impinging on them. Recent work argues that the formation of sensory representations are cell-type specific, as excitatory and inhibitory neurons use complementary information available in spike trains to represent sensory stimuli. Here, by measuring the mutual information between synaptic input and spike trains, we show that inhibitory and excitatory neurons in the barrel cortex transfer information differently: excitatory neurons show strong threshold adaptation and a reduction of intracellular information transfer with increasing firing rates. Inhibitory neurons, on the other hand, show threshold behaviour that facilitates broadband information transfer. We propose that cell-type specific intracellular information transfer is the rate-limiting step for neuronal communication across synaptically coupled networks. Ultimately, at high firing rates, the reduction of information transfer by excitatory neurons and its facilitation by inhibitory neurons together provides a mechanism for sparse coding and information compression in cortical networks.

scholarly journals Feedback inhibition and its control in an insect olfactory circuit

2019 ◽  
Author(s):  
Subhasis Ray ◽  
Zane N. Aldworth ◽  
Mark A. Stopfer
Keyword(s):  
Large Scale ◽  
Dynamic Range ◽  
Feedback Inhibition ◽  
Inhibitory Neurons ◽  
Biophysical Model ◽  
Intrinsic Properties ◽  
Olfactory Pathway ◽  
Olfactory Responses ◽  
Electrophysiological Recordings

AbstractInhibitory neurons play critical roles in regulating and shaping olfactory responses in vertebrates and invertebrates. In insects, these roles are performed by relatively few neurons, which can be interrogated efficiently, revealing fundamental principles of olfactory coding. Here, with electrophysiological recordings from the locust and a large-scale biophysical model, we analyzed the properties and functions of GGN, a unique giant GABAergic neuron that plays a central role in structuring olfactory codes in the locust mushroom body. Analysis of our in vivo recordings and simulations of our model of the olfactory network suggests that GGN extends the dynamic range of KCs, and leads us to predict the existence of a yet undiscovered olfactory pathway. Our analysis of GGN’s intrinsic properties, inputs, and outputs, in vivo and in silico, reveals basic new features of this critical neuron and the olfactory network that surrounds it.

scholarly journals Inhibitory control of correlated intrinsic variability in cortical networks

2016 ◽  
Author(s):  
Carsen Stringer ◽  
Marius Pachitariu ◽  
Michael Okun ◽  
Peter Bartho ◽  
Kenneth Harris ◽  
...  
Keyword(s):  
Large Scale ◽  
Feedback Inhibition ◽  
Activity Patterns ◽  
Network Models ◽  
External Noise ◽  
Inhibitory Neurons ◽  
Computational Techniques ◽  
Model Parameters ◽  
Intrinsic Variability ◽  
Cortical Networks

AbstractCortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the wide variety of activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

scholarly journals Inhibitory control of correlated intrinsic variability in cortical networks

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Carsen Stringer ◽  
Marius Pachitariu ◽  
Nicholas A Steinmetz ◽  
Michael Okun ◽  
Peter Bartho ◽  
...  
Keyword(s):  
Large Scale ◽  
Feedback Inhibition ◽  
Activity Patterns ◽  
Network Models ◽  
External Noise ◽  
Inhibitory Neurons ◽  
Computational Techniques ◽  
Model Parameters ◽  
Intrinsic Variability ◽  
Cortical Networks

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

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