Gain control by layer six in cortical circuits of vision

Olsen, Shawn R.; Bortone, Dante S.; Adesnik, Hillel; Scanziani, Massimo

doi:10.1038/nature10835

Cited by 473 publications

(699 citation statements)

References 48 publications

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“…Indeed, numerous studies using a variety of experimental techniques to manipulate CG feedback have documented changes in the magnitude of LGN responses (7)(8)(9)(10)(11)(12)(13)(14). Our results are consistent with some, but not all, of these observations.…”

Section: Discussionsupporting

confidence: 82%

“…Thus, CG feedback can both depolarize and hyperpolarize LGN relay neurons via direct monosynaptic excitation and indirect disynaptic inhibition, respectively. Through these excitatory and inhibitory connections, CG feedback could reduce LGN response latency and enhance response precision by (i) privileging the shortest-latency spikes in LGN relay neurons through monosynaptic depolarization, and (ii) eliminating sluggish spikes in LGN relay neurons through delayed hyperpolarization via disynaptic GABAergic inputs from LGN interneurons and TRN neurons (39)(40)(41), both of which receive glutamatergic CG input (12,42). Our model simulation supports this simple circuit mechanism and further suggests that a small increase in the weight of CG input is sufficient to reduce response latencies among LGN relay neurons (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Some have proposed that CG feedback modulates the gain (7)(8)(9)(10)(11)(12)(13)(14) and/or the spatiotemporal properties of LGN neurons (15)(16)(17). Others have proposed that corticothalamic feedback controls whether thalamic neurons are in a state of net excitation or inhibition (8,12), depending upon oscillatory activity in corticothalamic networks (18). Our goal was to conduct a causal and comprehensive examination of CG function using a combination of virus-mediated gene delivery and optogenetic strategies to selectively and reversibly manipulate the activity of CG neurons in vivo.…”

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confidence: 99%

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Corticogeniculate feedback sharpens the temporal precision and spatial resolution of visual signals in the ferret

Hasse

Briggs

2017

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

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The corticogeniculate (CG) pathway connects the visual cortex with the visual thalamus (LGN) in the feedback direction and enables the cortex to directly influence its own input. Despite numerous investigations, the role of this feedback circuit in visual perception remained elusive. To probe the function of CG feedback in a causal manner, we selectively and reversibly manipulated the activity of CG neurons in anesthetized ferrets in vivo using a combined viral-infection and optogenetics approach to drive expression of channelrhodopsin2 (ChR2) in CG neurons. We observed significant increases in temporal precision and spatial resolution of LGN neuronal responses to drifting grating and white noise stimuli when CG neurons expressing ChR2 were light activated. Enhancing CG feedback reduced visually evoked response latencies, increased spike-timing precision, and reduced classical receptive field size. Increased precision among LGN neurons led to increased spike-timing precision among granular layer V1 neurons as well. Together, our findings suggest that the function of CG feedback is to control the timing and precision of thalamic responses to incoming visual signals.T he feedforward progression of sensory information from peripheral receptors through nuclei in the sensory thalamus to the primary sensory cortex is well understood. For example, much is known about how neurons in the primary sensory cortex represent elementary sensory features based on the inputs they receive from peripheral and thalamic neurons with well-defined receptive field properties. In addition to these feedforward circuits, mammalian sensory systems include a substantial feedback projection from the primary sensory cortex to the sensory thalamus (1). Despite a rich history of investigation, the functional role of corticothalamic feedback circuits in sensory perception remains a fundamental mystery in neuroscience.Our goal was to determine the functional contribution of corticothalamic feedback to vision. Corticogeniculate (CG) circuits link the primary visual cortex (V1) with the lateral geniculate nucleus of the thalamus (LGN) and constitute the first cortical feedback connection in the visual processing hierarchy (2). CG axons target LGN relay neurons, local interneurons within the LGN, and neurons in the visual portion of the thalamic reticular nucleus (TRN) that inhibit LGN relay neurons (3-5) (Fig. 1A). Based on this pattern of axonal innervation, CG modulation of LGN neurons could include both monosynaptic excitation and disynaptic inhibition of LGN relay neurons via TRN and/or local LGN inhibitory circuitry. The CG circuit is anatomically robust-cortical synapses onto LGN relay neurons far outnumber retinal synapses (4); however, the receptive fields of LGN relay neurons reflect their retinal and not their cortical inputs (6). In part due to its subtle influence on LGN responses, the function of CG feedback has remained elusive.There have been numerous experimental examinations of CG function-using methods with varying degrees of sele...

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Corticogeniculate feedback sharpens the temporal precision and spatial resolution of visual signals in the ferret

Hasse

Briggs

2017

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

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“…Given that PV interneurons are implicated in both divisive and multiplicative functions, we now need more information on the sources of their inputs. Their excitatory inputs are likely to include those from Layer 6 pyramidal cells, because activating them suppresses visually evoked activity in pyramidal neurons in Layer 2/3 of the same cortical column (Olsen et al, 2012). Output from Layer 2/3 could therefore be amplified by suppressing Layer 6 pyramidal cells.…”

Section: Modulation That Amplifies Responses To Rf Inputmentioning

confidence: 99%

On the functions, mechanisms, and malfunctions of intracortical contextual modulation

Phillips

Clark

Silverstein

2015

Neuroscience & Biobehavioral Reviews

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A broad neuron-centric conception of contextual modulation is reviewed and re-assessed in the light of recent neurobiological studies of amplification, suppression, and synchronization. Behavioural and computational studies of perceptual and higher cognitive functions that depend on these processes are outlined, and evidence that those functions and their neuronal mechanisms are impaired in schizophrenia is summarized. Finally, we compare and assess the long-term biological functions of contextual modulation at the level of computational theory as formalized by the theories of coherent infomax and free energy reduction. We conclude that those theories, together with the many empirical findings reviewed, show how contextual modulation at the neuronal level enables the cortex to flexibly adapt the use of its knowledge to current circumstances by amplifying and grouping relevant activities and by suppressing irrelevant activities.

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“…As the density of I A channels increases from zero, the membrane is able to support progressively lower firing frequencies at spiking threshold. The resulting linearization of the FI curve from Type II to Type I has clear consequences for encoding information in firing rate as well as other computational properties such as thresholding and gain scaling, all of which are subjects of intense research (10)(11)(12)(13)(14)(15)(16)(17).…”

mentioning

confidence: 99%

Ion channel degeneracy enables robust and tunable neuronal firing rates

Drion

O’Leary

Marder

2015

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

128

165

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Firing rate is an important means of encoding information in the nervous system. To reliably encode a wide range of signals, neurons need to achieve a broad range of firing frequencies and to move smoothly between low and high firing rates. This can be achieved with specific ionic currents, such as A-type potassium currents, which can linearize the frequency-input current curve. By applying recently developed mathematical tools to a number of biophysical neuron models, we show how currents that are classically thought to permit low firing rates can paradoxically cause a jump to a high minimum firing rate when expressed at higher levels. Consequently, achieving and maintaining a low firing rate is surprisingly difficult and fragile in a biological context. This difficulty can be overcome via interactions between multiple currents, implying a need for ion channel degeneracy in the tuning of neuronal properties.FI curve | bifurcation | Type I excitability | Type II excitability | reduced neuron model F iring rates encode the intensities of many signals in the nervous system, whether these are inputs from sensory organs, internal representations of percepts, or muscle contraction commands in motor nerves. For a neuron to represent continuously varying signals in its firing rate, it must be able to fire at low, high, and all intermediate frequencies. Experimentally, this means the frequency-input current (or FI) curve has a specific shape, called Type I, such that firing frequency smoothly approaches zero at current threshold (1-6). By contrast, so-called Type II neurons have a lower bound in their firing frequency and move abruptly from quiescence to fast spiking, with this transition visible as a sharp jump in the FI curve at threshold (1,3,5).Type I behavior is physiologically unlikely with a minimal set of membrane currents such as the voltage-gated sodium and delayed-rectifier potassium currents in the standard squid giant axon Hodgkin-Huxley model, which is Type II. Classic experimental (1, 7) and theoretical studies (3-5, 8, 9) revealed that a Type II membrane (such as a squid giant axon) can be turned into a Type I membrane by adding an inactivating (A-type) potassium conductance, I A . As the density of I A channels increases from zero, the membrane is able to support progressively lower firing frequencies at spiking threshold. The resulting linearization of the FI curve from Type II to Type I has clear consequences for encoding information in firing rate as well as other computational properties such as thresholding and gain scaling, all of which are subjects of intense research (10-17).We now show that this picture is incomplete. Using rigorous but intuitive methods (18) and building on previous technical results (19-21), we show that introducing I A to a Type II neuron progressively linearizes but then delinearizes the FI curve as I A density increases further. Consequently, I A density must be tuned in a strict range to achieve Type I behavior. However, we show that other, unrelated currents including v...

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Gain control by layer six in cortical circuits of vision

Cited by 473 publications

References 48 publications

Corticogeniculate feedback sharpens the temporal precision and spatial resolution of visual signals in the ferret

Corticogeniculate feedback sharpens the temporal precision and spatial resolution of visual signals in the ferret

On the functions, mechanisms, and malfunctions of intracortical contextual modulation

Ion channel degeneracy enables robust and tunable neuronal firing rates

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