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.

Dynamic Properties of Recurrent Inhibition in Primary Visual Cortex: Contrast and Orientation Dependence of Contextual Effects

2000 ◽  
Vol 83 (2) ◽  
pp. 1019-1030 ◽  
Author(s):  
Valentin Dragoi ◽  
Mriganka Sur
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Dynamic Properties ◽  
Orientation Dependence ◽  
Recurrent Inhibition ◽  
Inhibitory Neurons ◽  
Classical Receptive Field ◽  
Contextual Interactions ◽  
Receptive Field Center

A fundamental feature of neural circuitry in the primary visual cortex (V1) is the existence of recurrent excitatory connections between spiny neurons, recurrent inhibitory connections between smooth neurons, and local connections between excitatory and inhibitory neurons. We modeled the dynamic behavior of intermixed excitatory and inhibitory populations of cells in V1 that receive input from the classical receptive field (the receptive field center) through feedforward thalamocortical afferents, as well as input from outside the classical receptive field (the receptive field surround) via long-range intracortical connections. A counterintuitive result is that the response of oriented cells can be facilitated beyond optimal levels when the surround stimulus is cross-oriented with respect to the center and suppressed when the surround stimulus is iso-oriented. This effect is primarily due to changes in recurrent inhibition within a local circuit. Cross-oriented surround stimulation leads to a reduction of presynaptic inhibition and a supraoptimal response, whereas iso-oriented surround stimulation has the opposite effect. This mechanism is used to explain the orientation and contrast dependence of contextual interactions in primary visual cortex: responses to a center stimulus can be both strongly suppressed and supraoptimally facilitated as a function of surround orientation, and these effects diminish as stimulus contrast decreases.

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).

scholarly journals Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex

2018 ◽  
Author(s):  
Petr Znamenskiy ◽  
Mean-Hwan Kim ◽  
Dylan R. Muir ◽  
Maria Florencia Iacaruso ◽  
Sonja B. Hofer ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Pyramidal Cells ◽  
Fine Tuning ◽  
Inhibitory Neurons ◽  
Local Network ◽  
Cortical Circuits ◽  
Sensory Stimuli ◽  
Excitatory Neurons ◽  
Strong Excitation

In the cerebral cortex, the interaction of excitatory and inhibitory synaptic inputs shapes the responses of neurons to sensory stimuli, stabilizes network dynamics1 and improves the efficiency and robustness of the neural code2–4. Excitatory neurons receive inhibitory inputs that track excitation5–8. However, how this co-tuning of excitation and inhibition is achieved by cortical circuits is unclear, since inhibitory interneurons are thought to pool the inputs of nearby excitatory cells and provide them with non-specific inhibition proportional to the activity of the local network9–13. Here we show that although parvalbumin-expressing (PV) inhibitory cells in mouse primary visual cortex make connections with the majority of nearby pyramidal cells, the strength of their synaptic connections is structured according to the similarity of the cells’ responses. Individual PV cells strongly inhibit those pyramidal cells that provide them with strong excitation and share their visual selectivity. This fine-tuning of synaptic weights supports co-tuning of inhibitory and excitatory inputs onto individual pyramidal cells despite dense connectivity between inhibitory and excitatory neurons. Our results indicate that individual PV cells are preferentially integrated into subnetworks of inter-connected, co-tuned pyramidal cells, stabilising their recurrent dynamics. Conversely, weak but dense inhibitory connectivity between subnetworks is sufficient to support competition between them, de-correlating their output. We suggest that the history and structure of correlated firing adjusts the weights of both inhibitory and excitatory connections, supporting stable amplification and selective recruitment of cortical subnetworks.

Epileptiform activity in microcultures containing one excitatory hippocampal neuron

1991 ◽  
Vol 65 (4) ◽  
pp. 761-770 ◽  
Author(s):  
M. M. Segal
Keyword(s):  
Action Potentials ◽  
Epileptiform Activity ◽  
Inhibitory Neuron ◽  
Excitatory Neuron ◽  
Inhibitory Neurons ◽  
Gamma Aminobutyric Acid ◽  
Intracellular Recordings ◽  
Voltage Fluctuations ◽  
Excitatory Neurons ◽  
Small Voltage

1. Paroxysmal depolarizing shifts (PDSs) occur during interictal epileptiform activity. Sustained depolarizations are characteristic of ictal activity, and events resembling PDSs also occur during the sustained depolarizations. To study these elements of epileptiform activity in a simpler context, I used the in vitro chronic-excitatory-block model of epilepsy of Furshpan and Potter and the microculture technique of Segal and Furshpan. 2. Intracellular recordings were made from 93 single-neuron microcultures. Forty of these solitary neurons were excitatory, their action potentials were replaced by PDS-like events or sustained depolarizations as kynurenate was removed from the perfusion solution. PDS-like events were similar to PDSs in intact cortex, mass cultures, and microcultures with more than one neuron. Small voltage fluctuations were also seen in solitary excitatory neurons in the absence of recorded action potentials. Sustained depolarizations developed in 5 of the 40 excitatory neurons. Forty-eight of the 93 solitary neurons were inhibitory, with bicuculline-sensitive hyperpolarizations after the action potential (ascribable to gamma-aminobutyric acid-A autapses). None of the solitary inhibitory neurons displayed sustained depolarizations. Five of the 93 neurons were insensitive to both kynurenate and bicuculline and were not placed in either the excitatory or the inhibitory category. 3. Both N-methyl-D-aspartate (NMDA) and non-NMDA glutamate receptors contributed to the PDS-like events and sustained depolarizations. Only a non-NMDA glutamate receptor component was evident for the small voltage fluctuations. 4. Intracellular recordings were also made from two-neuron microcultures, each containing one excitatory neuron and one inhibitory neuron. Sustained depolarizations developed in five microcultures, in each case only in the excitatory neuron.

scholarly journals Neurons in Visual Cortex are Driven by Feedback Projections when their Feedforward Sensory Input is Missing

2020 ◽  
Author(s):  
Andreas J Keller ◽  
Morgane M Roth ◽  
Massimo Scanziani
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Visual System ◽  
Receptive Fields ◽  
Visual Space ◽  
Inhibitory Neurons ◽  
Predictive Processing ◽  
Excitatory Neurons ◽  
Use Context ◽  
Stimulus Features

We sense our environment through pathways linking sensory organs to the brain. In the visual system, these feedforward pathways define the classical feedforward receptive field (ffRF), the area in space where visual stimuli excite a neuron1. The visual system also uses visual context, the visual scene surrounding a stimulus, to predict the content of the stimulus2, and accordingly, neurons have been found that are excited by stimuli outside their ffRF3–8. The mechanisms generating excitation to stimuli outside the ffRF are, however, unclear. Here we show that feedback projections onto excitatory neurons in mouse primary visual cortex (V1) generate a second receptive field driven by stimuli outside the ffRF. Stimulating this feedback receptive field (fbRF) elicits slow and delayed responses compared to ffRF stimulation. These responses are preferentially reduced by anesthesia and, importantly, by silencing higher visual areas (HVAs). Feedback inputs from HVAs have scattered receptive fields relative to their putative V1 targets enabling the generation of the fbRF. Neurons with fbRFs are located in cortical layers receiving strong feedback projections and are absent in the main input layer, consistent with a laminar processing hierarchy. The fbRF and the ffRF are mutually antagonistic since large, uniform stimuli, covering both, suppress responses. While somatostatin-expressing inhibitory neurons are driven by these large stimuli, parvalbumin and vasoactive-intestinal-peptide-expressing inhibitory neurons have antagonistic fbRF and ffRF, similar to excitatory neurons. Therefore, feedback projections may enable neurons to use context to predict information missing from the ffRF and to report differences in stimulus features across visual space, regardless if excitation occurs inside or outside the ffRF. We have identified a fbRF which, by complementing the ffRF, may contribute to predictive processing.

scholarly journals Somatosensory response properties of excitatory and inhibitory neurons in rat motor cortex

2011 ◽  
Vol 106 (3) ◽  
pp. 1355-1362 ◽  
Author(s):  
Peter D. Murray ◽  
Asaf Keller
Keyword(s):  
Motor Cortex ◽  
Receptive Field ◽  
Cortical Neurons ◽  
Inhibitory Neurons ◽  
Field Size ◽  
Spiking Neuron ◽  
Excitatory Neurons ◽  
Fast Spiking ◽  
Motor Cortical ◽  
Feedforward Inhibition

In sensory cortical networks, peripheral inputs differentially activate excitatory and inhibitory neurons. Inhibitory neurons typically have larger responses and broader receptive field tuning compared with excitatory neurons. These differences are thought to underlie the powerful feedforward inhibition that occurs in response to sensory input. In the motor cortex, as in the somatosensory cortex, cutaneous and proprioceptive somatosensory inputs, generated before and during movement, strongly and dynamically modulate the activity of motor neurons involved in a movement and ultimately shape cortical command. Human studies suggest that somatosensory inputs modulate motor cortical activity in a center excitation, surround inhibition manner such that input from the activated muscle excites motor cortical neurons that project to it, whereas somatosensory input from nearby, nonactivated muscles inhibit these neurons. A key prediction of this hypothesis is that inhibitory and excitatory motor cortical neurons respond differently to somatosensory inputs. We tested this prediction with the use of multisite extracellular recordings in anesthetized rats. We found that fast-spiking (presumably inhibitory) neurons respond to tactile and proprioceptive inputs at shorter latencies and larger response magnitudes compared with regular-spiking (presumably excitatory) neurons. In contrast, we found no differences in the receptive field size of these neuronal populations. Strikingly, all fast-spiking neuron pairs analyzed with cross-correlation analysis displayed common excitation, which was significantly more prevalent than common excitation for regular-spiking neuron pairs. These findings suggest that somatosensory inputs preferentially evoke feedforward inhibition in the motor cortex. We suggest that this provides a mechanism for dynamic selection of motor cortical modules during voluntary movements.

scholarly journals Cell-autonomous role of Presenilin in age-dependent survival of cortical interneurons

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Jongkyun Kang ◽  
Jie Shen
Keyword(s):  
Cerebral Cortex ◽  
Inhibitory Neuron ◽  
Gabaergic Neurons ◽  
Inhibitory Neurons ◽  
Double Knockout ◽  
Normal Number ◽  
Cortical Interneurons ◽  
Age Dependent ◽  
Excitatory Neurons

Abstract Background Mutations in the PSEN1 and PSEN2 genes are the major cause of familial Alzheimer’s disease. Previous studies demonstrated that Presenilin (PS), the catalytic subunit of γ-secretase, is required for survival of excitatory neurons in the cerebral cortex during aging. However, the role of PS in inhibitory interneurons had not been explored. Methods To determine PS function in GABAergic neurons, we generated inhibitory neuron-specific PS conditional double knockout (IN-PS cDKO) mice, in which PS is selectively inactivated by Cre recombinase expressed under the control of the endogenous GAD2 promoter. We then performed behavioral, biochemical, and histological analyses to evaluate the consequences of selective PS inactivation in inhibitory neurons. Results IN-PS cDKO mice exhibit earlier mortality and lower body weight despite normal food intake and basal activity. Western analysis of protein lysates from various brain sub-regions of IN-PS cDKO mice showed significant reduction of PS1 levels and dramatic accumulation of γ-secretase substrates. Interestingly, IN-PS cDKO mice develop age-dependent loss of GABAergic neurons, as shown by normal number of GAD67-immunoreactive interneurons in the cerebral cortex at 2–3 months of age but reduced number of cortical interneurons at 9 months. Moreover, age-dependent reduction of Parvalbumin- and Somatostatin-immunoreactive interneurons is more pronounced in the neocortex and hippocampus of IN-PS cDKO mice. Consistent with these findings, the number of apoptotic cells is elevated in the cerebral cortex of IN-PS cDKO mice, and the enhanced apoptosis is due to dramatic increases of apoptotic interneurons, whereas the number of apoptotic excitatory neurons is unaffected. Furthermore, progressive loss of interneurons in the cerebral cortex of IN-PS cDKO mice is accompanied with astrogliosis and microgliosis. Conclusion Our results together support a cell-autonomous role of PS in the survival of cortical interneurons during aging. Together with earlier studies, these findings demonstrate a universal, essential requirement of PS in the survival of both excitatory and inhibitory neurons during aging.

scholarly journals Contextual modulation of sensitivity to naturalistic image structure in macaque V2

2018 ◽  
Vol 120 (2) ◽  
pp. 409-420 ◽  
Author(s):  
Corey M. Ziemba ◽  
Jeremy Freeman ◽  
Eero P. Simoncelli ◽  
J. Anthony Movshon
Keyword(s):  
Visual Field ◽  
Receptive Field ◽  
Time Course ◽  
Visual Input ◽  
Contextual Modulation ◽  
Image Structure ◽  
V1 Neurons ◽  
Enhanced Sensitivity ◽  
Area V2 ◽  
Classical Receptive Field

The stimulus selectivity of neurons in V1 is well known, as is the finding that their responses can be affected by visual input to areas outside of the classical receptive field. Less well understood are the ways selectivity is modified as signals propagate to visual areas beyond V1, such as V2. We recently proposed a role for V2 neurons in representing the higher order statistical dependencies found in images of naturally occurring visual texture. V2 neurons, but not V1 neurons, respond more vigorously to “naturalistic” images that contain these dependencies than to “noise” images that lack them. In this work, we examine the dependency of these effects on stimulus size. For most V2 neurons, the preference for naturalistic over noise stimuli was modest when presented in small patches and gradually strengthened with increasing size, suggesting that the mechanisms responsible for this enhanced sensitivity operate over regions of the visual field that are larger than the classical receptive field. Indeed, we found that surround suppression was stronger for noise than for naturalistic stimuli and that the preference for large naturalistic stimuli developed over a delayed time course consistent with lateral or feedback connections. These findings are compatible with a spatially broad facilitatory mechanism that is absent in V1 and suggest that a distinct role for the receptive field surround emerges in V2 along with sensitivity for more complex image structure. NEW & NOTEWORTHY The responses of neurons in visual cortex are often affected by visual input delivered to regions of the visual field outside of the conventionally defined receptive field, but the significance of such contextual modulations are not well understood outside of area V1. We studied the importance of regions beyond the receptive field in establishing a novel form of selectivity for the statistical dependencies contained in natural visual textures that first emerges in area V2.

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 Experience shapes activity dynamics and stimulus coding of VIP inhibitory cells

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Marina Garrett ◽  
Sahar Manavi ◽  
Kate Roll ◽  
Douglas R Ollerenshaw ◽  
Peter A Groblewski ◽  
...  
Keyword(s):  
Visual Experience ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Specific Cell ◽  
Natural Scene ◽  
Cortical Circuits ◽  
Modes Of Processing ◽  
Visual Change Detection ◽  
Mouse Visual Cortex ◽  
Familiar Stimuli

Cortical circuits can flexibly change with experience and learning, but the effects on specific cell types, including distinct inhibitory types, are not well understood. Here we investigated how excitatory and VIP inhibitory cells in layer 2/3 of mouse visual cortex were impacted by visual experience in the context of a behavioral task. Mice learned a visual change detection task with a set of eight natural scene images. Subsequently, during 2-photon imaging experiments, mice performed the task with these familiar images and three sets of novel images. Strikingly, the temporal dynamics of VIP activity differed markedly between novel and familiar images: VIP cells were stimulus-driven by novel images but were suppressed by familiar stimuli and showed ramping activity when expected stimuli were omitted from a temporally predictable sequence. This prominent change in VIP activity suggests that these cells may adopt different modes of processing under novel versus familiar conditions.

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