Axonal Conduction Delays, Brain State, and Corticogeniculate Communication

Fast-Spike Interneurons in Visual Cortical Layer 5: Heterogeneous Response Properties Are Related to Thalamocortical Connectivity

Layer 4 (L4) of rabbit V1 contains fast-spike GABAergic interneurons (suspected inhibitory interneurons, SINs) that receive potent synaptic input from the LGN and generate fast, local feedforward inhibition. These cells display receptive fields with overlapping ON/OFF subregions, nonlinear spatial summation, very broad orientation/directional tuning, and high spontaneous and visually driven firing rates. Fast-spike interneurons are also found in Layer 5 (L5), which receives a much sparser input from the LGN, but the response properties and thalamocortical connectivity of L5 SINs are relatively unstudied. Here, we study L5 SINs in awake rabbits (both sexes) and compare their response properties with previously studied SINs of L4. We also assess thalamocortical connectivity of L5 SINs, examining cross-correlation of retinotopically aligned LGN-SIN spike trains and L5 SIN responses to electrical stimulation of the LGN. These analyses confirmed that many L5 SINs, like L4 SINs, receive a strong, fast monosynaptic drive from the LGN. Moreover, these LGN-connected L5 SINs had response properties similar to those of L4 SINs and were predominantly found in the upper half of L5. In contrast, L5 SINs with longer synaptic latencies to LGN stimulation displayed (1) sharper orientation tuning, (2) longer visual response latencies, (3) lower spontaneous and (4) visually driven firing rates, and (5) were found in the deeper half of L5. We suggest that the long-latency synaptic responses in such L5 SINs reflect a multisynaptic intracortical pathway that generates a different constellation of response properties than seen in L5 SINs that are driven directly by LGN input.

Adapting to time: Why nature may have evolved a diverse set of neurons

Brains have evolved diverse neurons with varying morphologies and dynamics that impact temporal information processing. In contrast, most neural network models use homogeneous units that vary only in spatial parameters (weights and biases). To explore the importance of temporal parameters, we trained spiking neural networks on tasks with varying temporal complexity, holding different parameter subsets constant. We found that adapting conduction delays is crucial for solving all test conditions under tight resource constraints. Remarkably, these tasks can be solved using only temporal parameters (delays and time constants) with constant weights. In more complex spatio-temporal tasks, an adaptable bursting parameter was essential. Overall, allowing adaptation of both temporal and spatial parameters enhances network robustness to noise, a vital feature for biological brains and neuromorphic computing systems. Our findings suggest that rich and adaptable dynamics may be the key for solving temporally structured tasks efficiently in evolving organisms, which would help explain the diverse physiological properties of biological neurons.

Integration of distinct cortical inputs to primary and higher order inhibitory cells of the thalamus

The neocortex controls its own sensory input in part through top-down inhibitory mechanisms. Descending corticothalamic projections drive GABAergic neurons of the thalamic reticular nucleus (TRN), which govern thalamocortical cell activity via inhibition. Neurons in sensory TRN are organized into primary and higher order (HO) subpopulations, with separate intrathalamic connections and distinct genetic and functional properties. Here, we investigated top-down neocortical control over primary and HO neurons of somatosensory TRN. Projections from layer 6 of somatosensory cortex evoked stronger and more state-dependent activity in primary than in HO TRN, driven by more robust synaptic inputs and potent T-type calcium currents. However, HO TRN received additional, physiologically distinct, inputs from motor cortex and layer 5 of S1. Thus, in a departure from the canonical focused sensory layer 6 innervation characteristic of primary TRN, HO TRN integrates broadly from multiple corticothalamic systems, with unique state-dependence, extending the range of mechanisms for top-down control.

Imaging high-frequency voltage dynamics in multiple neuron classes of behaving mammals

Fluorescent genetically encoded voltage indicators report transmembrane potentials of targeted cell-types. However, voltage-imaging instrumentation has lacked the sensitivity to track spontaneous or evoked high-frequency voltage oscillations in neural populations. Here we describe two complementary TEMPO voltage-sensing technologies that capture neural oscillations up to ~100 Hz. Fiber-optic TEMPO achieves ~10-fold greater sensitivity than prior photometry systems, allows hour-long recordings, and monitors two neuron-classes per fiber-optic probe in freely moving mice. With it, we uncovered cross-frequency-coupled theta- and gamma-range oscillations and characterized excitatory-inhibitory neural dynamics during hippocampal ripples and visual cortical processing. The TEMPO mesoscope images voltage activity in two cell-classes across a ~8-mm-wide field-of-view in head-fixed animals. In awake mice, it revealed sensory-evoked excitatory-inhibitory neural interactions and traveling gamma and 3-7 Hz waves in the visual cortex, and previously unreported propagation directions for hippocampal theta and beta waves. These technologies have widespread applications probing diverse oscillations and neuron-type interactions in healthy and diseased brains.

Motor Control of Distinct Layer 6 Corticothalamic Feedback Circuits

Layer 6 corticothalamic (L6 CT) neurons provide massive input to the thalamus, and these feedback connections enable the cortex to influence its own sensory input by modulating thalamic excitability. However, the functional role(s) feedback serves during sensory processing is unclear. One hypothesis is that CT feedback is under the control of extrasensory signals originating from higher-order cortical areas, yet we know nothing about the mechanisms of such control. It is also unclear whether such regulation is specific to CT neurons with distinct thalamic connectivity. Using mice (either sex) combined with in vitro electrophysiology techniques, optogenetics, and retrograde labeling, we describe studies of vibrissal primary motor cortex (vM1) influences on different CT neurons in the vibrissal primary somatosensory cortex (vS1) with distinct intrathalamic axonal projections. We found that vM1 inputs are highly selective, evoking stronger postsynaptic responses in CT neurons projecting to the dual ventral posterior medial nucleus (VPm) and posterior medial nucleus (POm) located in lower L6a than VPm-only-projecting CT cells in upper L6a. A targeted analysis of the specific cells and synapses involved revealed that the greater responsiveness of Dual CT neurons was due to their distinctive intrinsic membrane properties and synaptic mechanisms. These data demonstrate that vS1 has at least two discrete L6 CT subcircuits distinguished by their thalamic projection patterns, intrinsic physiology, and functional connectivity with vM1. Our results also provide insights into how a distinct CT subcircuit may serve specialized roles specific to contextual modulation of tactile-related sensory signals in the somatosensory thalamus during active vibrissa movements.

Motor Control of Distinct Layer 6 Corticothalamic Feedback Circuits

Layer 6 corticothalamic (L6 CT) neurons provide massive input to the thalamus, and these feedback connections enable the cortex to influence its own sensory input by modulating thalamic excitability. However, the functional role(s) feedback serves during sensory processing is unclear. One hypothesis is that CT feedback is under the control of extra-sensory signals originating from higher-order cortical areas, yet we know nothing about the mechanisms of such control. It is also unclear whether such regulation is specific to CT neurons with distinct thalamic connectivity. Using mice (either sex) combined with in vitro electrophysiology techniques, optogenetics, and retrograde labeling, we describe studies of vibrissal primary motor cortex (vM1) influences on different CT neurons in the vibrissal primary somatosensory cortex (vS1) with distinct intrathalamic axonal projections. We found that vM1 inputs are highly selective, evoking stronger postsynaptic responses in Dual ventral posterior medial nucleus (VPm) and posterior medial nucleus (POm) projecting CT neurons located in lower L6a than VPm-only projecting CT cells in upper L6a. A targeted analysis of the specific cells and synapses involved revealed that the greater responsiveness of Dual CT neurons was due to their distinctive intrinsic membrane properties and synaptic mechanisms. These data demonstrate that vS1 has at least two discrete L6 CT subcircuits distinguished by their thalamic projection patterns, intrinsic physiology, and functional connectivity with vM1. Our results also provide insights into how a distinct CT subcircuit may serve specialized roles specific to contextual modulation of tactile-related sensory signals in the somatosensory thalamus during active vibrissa movements.

Visual Corticotectal Neurons in Awake Rabbits: Receptive Fields and Driving Monosynaptic Thalamocortical Inputs

The superior colliculus receives powerful synaptic inputs from corticotectal neurons in the visual cortex. The function of these corticotectal neurons remains largely unknown due to a limited understanding of their response properties and connectivity. Here, we use antidromic methods to identify corticotectal neurons in awake male and female rabbits, and measure their axonal conduction times, thalamic inputs and receptive field properties. All corticotectal neurons responded to sinusoidal drifting gratings with a nonlinear (nonsinusoidal) increase in mean firing rate but showed pronounced differences in their ON-OFF receptive field structures that we classified into three groups, Cx, S2, and S1. Cx receptive fields had highly overlapping ON and OFF subfields as classical complex cells, S2 had largely separated ON and OFF subfields as classical simple cells, and S1 had a single ON or OFF subfield. Thus, all corticotectal neurons are homogeneous in their nonlinear spatial summation but very heterogeneous in their spatial integration of ON and OFF inputs. The Cx type had the fastest conducting axons, the highest spontaneous activity, and the strongest and fastest visual responses. The S2 type had the highest orientation selectivity, and the S1 type had the slowest conducting axons. Moreover, our cross-correlation analyses found that a subpopulation of corticotectal neurons with very fast conducting axons and high spontaneous firing rates (largely "Cx" type) receives monosynaptic input from retinotopically aligned thalamic neurons. This previously unrecognized fast-conducting thalamic-mediated corticotectal pathway may provide specialized information to superior colliculus and prime recipient neurons for subsequent corticotectal or retinal synaptic input.

Learnable axonal delay in spiking neural networks improves spoken word recognition

Spiking neural networks (SNNs), which are composed of biologically plausible spiking neurons, and combined with bio-physically realistic auditory periphery models, offer a means to explore and understand human auditory processing-especially in tasks where precise timing is essential. However, because of the inherent temporal complexity in spike sequences, the performance of SNNs has remained less competitive compared to artificial neural networks (ANNs). To tackle this challenge, a fundamental research topic is the configuration of spike-timing and the exploration of more intricate architectures. In this work, we demonstrate a learnable axonal delay combined with local skip-connections yields state-of-the-art performance on challenging benchmarks for spoken word recognition. Additionally, we introduce an auxiliary loss term to further enhance accuracy and stability. Experiments on the neuromorphic speech benchmark datasets, NTIDIDIGITS and SHD, show improvements in performance when incorporating our delay module in comparison to vanilla feedforward SNNs. Specifically, with the integration of our delay module, the performance on NTIDIDIGITS and SHD improves by 14% and 18%, respectively. When paired with local skip-connections and the auxiliary loss, our approach surpasses both recurrent and convolutional neural networks, yet uses 10 × fewer parameters for NTIDIDIGITS and 7 × fewer for SHD.

Delay-dependent transitions of phase synchronization and coupling symmetry between neurons shaped by spike-timing-dependent plasticity

Synchronization plays a key role in learning and memory by facilitating the communication between neurons promoted by synaptic plasticity. Spike-timing-dependent plasticity (STDP) is a form of synaptic plasticity that modifies the strength of synaptic connections between neurons based on the coincidence of pre- and postsynaptic spikes. In this way, STDP simultaneously shapes the neuronal activity and synaptic connectivity in a feedback loop. However, transmission delays due to the physical distance between neurons affect neuronal synchronization and the symmetry of synaptic coupling. To address the question that how transmission delays and STDP can jointly determine the emergent pairwise activity-connectivity patterns, we studied phase synchronization properties and coupling symmetry between two bidirectionally coupled neurons using both phase oscillator and conductance-based neuron models. We show that depending on the range of transmission delays, the activity of the two-neuron motif can achieve an in-phase/anti-phase synchronized state and its connectivity can attain a symmetric/asymmetric coupling regime. The coevolutionary dynamics of the neuronal system and the synaptic weights due to STDP stabilizes the motif in either one of these states by transitions between in-phase/anti-phase synchronization states and symmetric/asymmetric coupling regimes at particular transmission delays. These transitions crucially depend on the phase response curve (PRC) of the neurons, but they are relatively robust to the heterogeneity of transmission delays and potentiation-depression imbalance of the STDP profile.

Brain State-Dependent Modulation of Thalamic Visual Processing by Cortico-Thalamic Feedback

The behavioral state of a mammal impacts how the brain responds to visual stimuli as early as in the dorsolateral geniculate nucleus of the thalamus (dLGN), the primary relay of visual information to the cortex. A clear example of this is the markedly stronger response of dLGN neurons to higher temporal frequencies of the visual stimulus in alert as compared with quiescent animals. The dLGN receives strong feedback from the visual cortex, yet whether this feedback contributes to these state-dependent responses to visual stimuli is poorly understood. Here, we show that in male and female mice, silencing cortico-thalamic feedback profoundly reduces state-dependent differences in the response of dLGN neurons to visual stimuli. This holds true for dLGN responses to both temporal and spatial features of the visual stimulus. These results reveal that the state-dependent shift of the response to visual stimuli in an early stage of visual processing depends on cortico-thalamic feedback.SIGNIFICANCE STATEMENT Brain state affects even the earliest stages of sensory processing. A clear example of this phenomenon is the change in thalamic responses to visual stimuli depending on whether the animal's brain is in an alert or quiescent state. Despite the radical impact that brain state has on sensory processing, the underlying circuits are still poorly understood. Here, we show that both the temporal and spatial response properties of thalamic neurons to visual stimuli depend on the state of the animal and, crucially, that this state-dependent shift relies on the feedback projection from visual cortex to thalamus.

Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity

The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulation can enable the suppression of pathologically strong inter-population synaptic connectivity through spike-timing-dependent plasticity (STDP). More specifically, we tested how introducing a time shift between stimuli delivered to two interacting populations of neurons can effectively decouple them. To that end, we first used a tractable model, i.e., two bidirectionally coupled leaky integrate-and-fire (LIF) neurons, to theoretically analyze the optimal range of stimulation frequency and time shift for decoupling. We then extended our results to two reciprocally connected neuronal populations (modules) where inter-population delayed connections were modified by STDP. As predicted by the theoretical results, appropriately time-shifted stimulation causes a decoupling of the two-module system through STDP, i.e., by unlearning pathologically strong synaptic interactions between the two populations. Based on the overall topology of the connections, the decoupling of the two modules, in turn, causes a desynchronization of the populations that outlasts the cessation of stimulation. Decoupling effects of the time-shifted stimulation can be realized by time-shifted burst stimulation as well as time-shifted continuous simulation. Our results provide insight into the further optimization of a variety of multichannel stimulation protocols aiming at a therapeutic reshaping of diseased brain networks.

State-Dependent Modulation of Activity in Distinct Layer 6 Corticothalamic Neurons in Barrel Cortex of Awake Mice

Layer 6 corticothalamic (L6 CT) neurons are in a strategic position to control sensory input to the neocortex, yet we understand very little about their functions. Apart from studying their anatomic, physiological, and synaptic properties, most recent efforts have focused on the activity-dependent influences CT cells can exert on thalamic and cortical neurons through causal optogenetic manipulations. However, few studies have attempted to study them during behavior. To address this gap, we performed juxtacellular recordings from optogenetically identified CT neurons in whisker-related primary somatosensory cortex (wS1) of awake, head-fixed mice (either sex) free to rest quietly or self-initiate bouts of whisking and locomotion. We found a rich diversity of response profiles exhibited by CT cells. Their spiking patterns were either modulated by whisking-related behavior (∼28%) or not (∼72%). Whisking-responsive neurons exhibited both increases (activated-type) and decreases in firing rates (suppressed-type) that aligned with whisking onset better than locomotion. We also encountered responsive neurons with preceding modulations in firing rate before whisking onset. Overall, whisking better explained these changes in rates than overall changes in arousal. Whisking-unresponsive CT cells were generally quiet, with many having low spontaneous firing rates (sparse-type) and others being completely silent (silent-type). Remarkably, the sparse firing CT population preferentially spiked at the state transition point when pupil diameter constricted, and the mouse entered quiet wakefulness. Thus, our results demonstrate that L6 CT cells in wS1 show diverse spiking patterns, perhaps subserving distinct functional roles related to precisely timed responses during complex behaviors and transitions between discrete waking states.SIGNIFICANCE STATEMENT Layer 6 corticothalamic neurons provide a massive input to the sensory thalamus and local connectivity within cortex, but their role in thalamocortical processing remains unclear because of difficulty accessing and isolating their activity. Although several recent optogenetic studies reveal that the net influence of corticothalamic actions, suppression versus enhancement, depends critically on the rate these neurons fire, the factors that influence their spiking are poorly understood, particularly during wakefulness. Using the well-established Ntsr1-Cre line to target this elusive population in the whisker somatosensory cortex of awake mice, we found that corticothalamic neurons show diverse state-related responses and modulations in firing rate. These results suggest separate corticothalamic populations can differentially influence thalamocortical excitability during rapid state transitions in awake, behaving animals.

Robust effects of corticothalamic feedback and behavioral state on movie responses in mouse dLGN

Neurons in the dorsolateral geniculate nucleus (dLGN) of the thalamus receive a substantial proportion of modulatory inputs from corticothalamic (CT) feedback and brain stem nuclei. Hypothesizing that these modulatory influences might be differentially engaged depending on the visual stimulus and behavioral state, we performed in vivo extracellular recordings from mouse dLGN while optogenetically suppressing CT feedback and monitoring behavioral state by locomotion and pupil dilation. For naturalistic movie clips, we found CT feedback to consistently increase dLGN response gain and promote tonic firing. In contrast, for gratings, CT feedback effects on firing rates were mixed. For both stimulus types, the neural signatures of CT feedback closely resembled those of behavioral state, yet effects of behavioral state on responses to movies persisted even when CT feedback was suppressed. We conclude that CT feedback modulates visual information on its way to cortex in a stimulus-dependent manner, but largely independently of behavioral state.

Synaptic mechanisms of top-down control in the non-lemniscal inferior colliculus

Corticofugal projections to evolutionarily ancient, subcortical structures are ubiquitous across mammalian sensory systems. These 'descending' pathways enable the neocortex to control ascending sensory representations in a predictive or feedback manner, but the underlying cellular mechanisms are poorly understood. Here, we combine optogenetic approaches with in vivo and in vitro patch-clamp electrophysiology to study the projection from mouse auditory cortex to the inferior colliculus (IC), a major descending auditory pathway that controls IC neuron feature selectivity, plasticity, and auditory perceptual learning. Although individual auditory cortico-collicular synapses were generally weak, IC neurons often integrated inputs from multiple corticofugal axons that generated reliable, tonic depolarizations even during prolonged presynaptic activity. Latency measurements in vivo showed that descending signals reach the IC within 30 ms of sound onset, which in IC neurons corresponded to the peak of synaptic depolarizations evoked by short sounds. Activating ascending and descending pathways at latencies expected in vivo caused a NMDA receptor-dependent, supralinear excitatory postsynaptic potential summation, indicating that descending signals can nonlinearly amplify IC neurons' moment-to-moment acoustic responses. Our results shed light upon the synaptic bases of descending sensory control and imply that heterosynaptic cooperativity contributes to the auditory cortico-collicular pathway's role in plasticity and perceptual learning.

Distinct "driving" versus "modulatory" influences of different visual corticothalamic pathways

Higher-order (HO) thalamic nuclei interact extensively and reciprocally with the cerebral cortex. These corticothalamic (CT) interactions are thought to be important for sensation and perception, attention, and many other important brain functions. CT projections to HO thalamic nuclei, such as the visual pulvinar, originate from two different excitatory populations in cortical layers 5 and 6, whereas first-order nuclei (such as the dorsolateral geniculate nucleus; dLGN) only receive layer 6 CT input. It has been proposed that these layer 5 and layer 6 CT pathways have different functional influences on the HO thalamus, but this has never been directly tested. By optogenetically inactivating different CT populations in the primary visual cortex (V1) and recording single-unit activity from V1, dLGN, and pulvinar of awake mice, we demonstrate that layer 5, but not layer 6, CT projections drive visual responses in the pulvinar, even while both pathways provide retinotopic, baseline excitation to their thalamic targets. Inactivating the superior colliculus also suppressed visual responses in the same subregion of the pulvinar, demonstrating that cortical layer 5 and subcortical inputs both contribute to HO visual thalamic activity-even at the level of putative single neurons. Altogether, these results indicate a functional division of "driver" and "modulator" CT pathways from V1 to the visual thalamus in vivo.

The synaptic inputs and thalamic projections of two classes of layer 6 corticothalamic neurons in primary somatosensory cortex of the mouse

Although corticothalamic neurons (CThNs) represent the largest source of synaptic input to thalamic neurons, their role in regulating thalamocortical interactions remains incompletely understood. CThNs in sensory cortex have historically been divided into two types, those with cell bodies in Layer 6 (L6) that project back to primary sensory thalamic nuclei and those with cell bodies in Layer 5 (L5) that project to higher-order thalamic nuclei and subcortical structures. Recently, diversity among L6 CThNs has increasingly been appreciated. In the rodent somatosensory cortex, two major classes of L6 CThNs have been identified: one projecting to the ventral posterior medial nucleus (VPM-only L6 CThNs) and one projecting to both VPM and the posterior medial nucleus (VPM/POm L6 CThNs). Using rabies-based tracing methods in mice, we asked whether these L6 CThN populations integrate similar synaptic inputs. We found that both types of L6 CThNs received local input from somatosensory cortex and thalamic input from VPM and POm. However, VPM/POm L6 CThNs received significantly more input from a number of additional cortical areas, higher order thalamic nuclei, and subcortical structures. We also found that the two types of L6 CThNs target different functional regions within the thalamic reticular nucleus (TRN). Together, our results indicate that these two types of L6 CThNs represent distinct information streams in the somatosensory cortex and suggest that VPM-only L6 CThNs regulate, via their more restricted circuits, sensory responses related to a cortical column while VPM/POm L6 CThNs, which are integrated into more widespread POm-related circuits, relay contextual information.

Aversive stimuli bias corticothalamic responses to motivationally significant cues

Making predictions about future rewards or punishments is fundamental to adaptive behavior. These processes are influenced by prior experience. For example, prior exposure to aversive stimuli or stressors changes behavioral responses to negative- and positive-value predictive cues. Here, we demonstrate a role for medial prefrontal cortex (mPFC) neurons projecting to the paraventricular nucleus of the thalamus (PVT; mPFC→PVT) in this process. We found that a history of aversive stimuli negatively biased behavioral responses to motivationally relevant cues in mice and that this negative bias was associated with hyperactivity in mPFC→PVT neurons during exposure to those cues. Furthermore, artificially mimicking this hyperactive response with selective optogenetic excitation of the same pathway recapitulated the negative behavioral bias induced by aversive stimuli, whereas optogenetic inactivation of mPFC→PVT neurons prevented the development of the negative bias. Together, our results highlight how information flow within the mPFC→PVT circuit is critical for making predictions about motivationally-relevant outcomes as a function of prior experience.

Role of NMDAR plasticity in a computational model of synaptic memory

A largely unexplored question in neuronal plasticity is whether synapses are capable of encoding and learning the timing of synaptic inputs. We address this question in a computational model of synaptic input time difference learning (SITDL), where N-methyl-d-aspartate receptor (NMDAR) isoform expression in silent synapses is affected by time differences between glutamate and voltage signals. We suggest that differences between NMDARs' glutamate and voltage gate conductances induce modifications of the synapse's NMDAR isoform population, consequently changing the timing of synaptic response. NMDAR expression at individual synapses can encode the precise time difference between signals. Thus, SITDL enables the learning and reconstruction of signals across multiple synapses of a single neuron. In addition to plausibly predicting the roles of NMDARs in synaptic plasticity, SITDL can be usefully applied in artificial neural network models.

Untangling the cortico-thalamo-cortical loop: cellular pieces of a knotty circuit puzzle

Functions of the neocortex depend on its bidirectional communication with the thalamus, via cortico-thalamo-cortical (CTC) loops. Recent work dissecting the synaptic connectivity in these loops is generating a clearer picture of their cellular organization. Here, we review findings across sensory, motor and cognitive areas, focusing on patterns of cell type-specific synaptic connections between the major types of cortical and thalamic neurons. We outline simple and complex CTC loops, and note features of these loops that appear to be general versus specialized. CTC loops are tightly interlinked with local cortical and corticocortical (CC) circuits, forming extended chains of loops that are probably critical for communication across hierarchically organized cerebral networks. Such CTC-CC loop chains appear to constitute a modular unit of organization, serving as scaffolding for area-specific structural and functional modifications. Inhibitory neurons and circuits are embedded throughout CTC loops, shaping the flow of excitation. We consider recent findings in the context of established CTC and CC circuit models, and highlight current efforts to pinpoint cell type-specific mechanisms in CTC loops involved in consciousness and perception. As pieces of the connectivity puzzle fall increasingly into place, this knowledge can guide further efforts to understand structure-function relationships in CTC loops.

Influence of Delayed Conductance on Neuronal Synchronization

In the brain, the excitation-inhibition balance prevents abnormal synchronous behavior. However, known synaptic conductance intensity can be insufficient to account for the undesired synchronization. Due to this fact, we consider time delay in excitatory and inhibitory conductances and study its effect on the neuronal synchronization. In this work, we build a neuronal network composed of adaptive integrate-and-fire neurons coupled by means of delayed conductances. We observe that the time delay in the excitatory and inhibitory conductivities can alter both the state of the collective behavior (synchronous or desynchronous) and its type (spike or burst). For the weak coupling regime, we find that synchronization appears associated with neurons behaving with extremes highest and lowest mean firing frequency, in contrast to when desynchronization is present when neurons do not exhibit extreme values for the firing frequency. Synchronization can also be characterized by neurons presenting either the highest or the lowest levels in the mean synaptic current. For the strong coupling, synchronous burst activities can occur for delays in the inhibitory conductivity. For approximately equal-length delays in the excitatory and inhibitory conductances, desynchronous spikes activities are identified for both weak and strong coupling regimes. Therefore, our results show that not only the conductance intensity, but also short delays in the inhibitory conductance are relevant to avoid abnormal neuronal synchronization.

Role of Feedback Connections in Central Visual Processing

The physiological response properties of neurons in the visual system are inherited mainly from feedforward inputs. Interestingly, feedback inputs often outnumber feedforward inputs. Although they are numerous, feedback connections are weaker, slower, and considered to be modulatory, in contrast to fast, high-efficacy feedforward connections. Accordingly, the functional role of feedback in visual processing has remained a fundamental mystery in vision science. At the core of this mystery are questions about whether feedback circuits regulate spatial receptive field properties versus temporal responses among target neurons, or whether feedback serves a more global role in arousal or attention. These proposed functions are not mutually exclusive, and there is compelling evidence to support multiple functional roles for feedback. In this review, the role of feedback in vision will be explored mainly from the perspective of corticothalamic feedback. Further generalized principles of feedback applicable to corticocortical connections will also be considered.

The Synaptic Organization of Layer 6 Circuits Reveals Inhibition as a Major Output of a Neocortical Sublamina

The canonical cortical microcircuit has principally been defined by interlaminar excitatory connections among the six layers of the neocortex. However, excitatory neurons in layer 6 (L6), a layer whose functional organization is poorly understood, form relatively rare synaptic connections with other cortical excitatory neurons. Here, we show that the vast majority of parvalbumin inhibitory neurons in a sublamina within L6 send axons through the cortical layers toward the pia. These interlaminar inhibitory neurons receive local synaptic inputs from both major types of L6 excitatory neurons and receive stronger input from thalamocortical afferents than do neighboring pyramidal neurons. The distribution of these interlaminar interneurons and their synaptic connectivity further support a functional subdivision within the standard six layers of the cortex. Positioned to integrate local and long-distance inputs in this sublayer, these interneurons generate an inhibitory interlaminar output. These findings call for a revision to the canonical cortical microcircuit.

Complementary Ca2+ Activity of Sensory Activated and Suppressed Layer 6 Corticothalamic Neurons Reflects Behavioral State

Layer 6 (L6) corticothalamic neurons project to thalamus, where they are thought to regulate sensory information transmission to cortex. However, the activity of these neurons during different behavioral states has not been described. Here, we imaged calcium changes in visual cortex L6 primary corticothalamic neurons with two-photon microscopy in head-fixed mice in response to passive viewing during a range of behavioral states, from locomotion to sleep. In addition to a substantial fraction of quiet neurons, we found sensory-activated and suppressed neurons, comprising two functionally distinct L6 feedback channels. Quiet neurons could be dynamically recruited to one or another functional channel, and the opposite, functional neurons could become quiet under different stimulation conditions or behavior states. The state dependence of neuronal activity was heterogeneous with respect to locomotion or level of alertness, although the average activity was largest during highest vigilance within populations of functional neurons. Interestingly, complementary activity of these distinct populations kept the overall corticothalamic feedback relatively constant during any given behavioral state. Thereby, in addition to sensory and non-sensory information, a constant activity level characteristic of behavioral state is conveyed to thalamus, where it can regulate signal transmission from the periphery to cortex.

Context-dependent and dynamic functional influence of corticothalamic pathways to first- and higher-order visual thalamus

Layer 6 (L6) is the sole purveyor of corticothalamic (CT) feedback to first-order thalamus and also sends projections to higher-order thalamus, yet how it engages the full corticothalamic circuit to contribute to sensory processing in an awake animal remains unknown. We sought to elucidate the functional impact of L6CT projections from the primary visual cortex to the dorsolateral geniculate nucleus (first-order) and pulvinar (higher-order) using optogenetics and extracellular electrophysiology in awake mice. While sustained L6CT photostimulation suppresses activity in both visual thalamic nuclei in vivo, moderate-frequency (10 Hz) stimulation powerfully facilitates thalamic spiking. We show that each stimulation paradigm differentially influences the balance between monosynaptic excitatory and disynaptic inhibitory corticothalamic pathways to the dorsolateral geniculate nucleus and pulvinar, as well as the prevalence of burst versus tonic firing. Altogether, our results support a model in which L6CTs modulate first- and higher-order thalamus through parallel excitatory and inhibitory pathways that are highly dynamic and context-dependent.

Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible With Various Temporal Codes

Recent studies have demonstrated the effectiveness of supervised learning in spiking neural networks (SNNs). A trainable SNN provides a valuable tool not only for engineering applications but also for theoretical neuroscience studies. Here, we propose a modified SpikeProp learning algorithm, which ensures better learning stability for SNNs and provides more diverse network structures and coding schemes. Specifically, we designed a spike gradient threshold rule to solve the well-known gradient exploding problem in SNN training. In addition, regulation rules on firing rates and connection weights are proposed to control the network activity during training. Based on these rules, biologically realistic features such as lateral connections, complex synaptic dynamics, and sparse activities are included in the network to facilitate neural computation. We demonstrate the versatility of this framework by implementing three well-known temporal codes for different types of cognitive tasks, namely, handwritten digit recognition, spatial coordinate transformation, and motor sequence generation. Several important features observed in experimental studies, such as selective activity, excitatory-inhibitory balance, and weak pairwise correlation, emerged in the trained model. This agreement between experimental and computational results further confirmed the importance of these features in neural function. This work provides a new framework, in which various neural behaviors can be modeled and the underlying computational mechanisms can be studied.

Activation of a Visual Cortical Column by a Directionally Selective Thalamocortical Neuron

The retinas of rabbits and rodents have directionally selective (DS) retinal ganglion cells that convey directional signals through the lateral geniculate nucleus (LGN) of the thalamus to the primary visual cortex (V1). Notably, the function and synaptic impact in V1 of these directional LGN signals are unknown. Here we measured, in awake rabbits, the synaptic impact generated in V1 by individual LGN DS neurons. We show that these neurons make fast and strong connections in layers 4 and 6, with postsynaptic effects that are similar to those made by LGN concentric neurons, the main thalamic drivers of V1. By contrast, the synaptic impact of LGN DS neurons on superficial cortical layers was not detectable. These results suggest that LGN DS neurons activate a cortical column by targeting the main cortical input layers and that the role of DS input to superficial cortical layers is likely to be weak and/or modulatory.

Corticocortical Systems Underlying High-Order Motor Control

Cortical networks are characterized by the origin, destination, and reciprocity of their connections, as well as by the diameter, conduction velocity, and synaptic efficacy of their axons. The network formed by parietal and frontal areas lies at the core of cognitive-motor control because the outflow of parietofrontal signaling is conveyed to the subcortical centers and spinal cord through different parallel pathways, whose orchestration determines, not only when and how movements will be generated, but also the nature of forthcoming actions. Despite intensive studies over the last 50 years, the role of corticocortical connections in motor control and the principles whereby selected cortical networks are recruited by different task demands remain elusive. Furthermore, the synaptic integration of different cortical signals, their modulation by transthalamic loops, and the effects of conduction delays remain challenging questions that must be tackled to understand the dynamical aspects of parietofrontal operations. In this article, we evaluate results from nonhuman primate and selected rodent experiments to offer a viewpoint on how corticocortical systems contribute to learning and producing skilled actions. Addressing this subject is not only of scientific interest but also essential for interpreting the devastating consequences for motor control of lesions at different nodes of this integrated circuit. In humans, the study of corticocortical motor networks is currently based on MRI-related methods, such as resting-state connectivity and diffusion tract-tracing, which both need to be contrasted with histological studies in nonhuman primates.

Dendritic and Axonal Propagation Delays May Shape Neuronal Networks With Plastic Synapses

Biological neuronal networks are highly adaptive and plastic. For instance, spike-timing-dependent plasticity (STDP) is a core mechanism which adapts the synaptic strengths based on the relative timing of pre- and postsynaptic spikes. In various fields of physiology, time delays cause a plethora of biologically relevant dynamical phenomena. However, time delays increase the complexity of model systems together with the computational and theoretical analysis burden. Accordingly, in computational neuronal network studies propagation delays were often neglected. As a downside, a classic STDP rule in oscillatory neurons without propagation delays is unable to give rise to bidirectional synaptic couplings, i.e., loops or uncoupled states. This is at variance with basic experimental results. In this mini review, we focus on recent theoretical studies focusing on how things change in the presence of propagation delays. Realistic propagation delays may lead to the emergence of neuronal activity and synaptic connectivity patterns, which cannot be captured by classic STDP models. In fact, propagation delays determine the inventory of attractor states and shape their basins of attractions. The results reviewed here enable to overcome fundamental discrepancies between theory and experiments. Furthermore, these findings are relevant for the development of therapeutic brain stimulation techniques aiming at shifting the diseased brain to more favorable attractor states.

Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters

Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ganglia-thalamocortical circuit where each brain area is represented by a "neuronal" node in a directed graph. Control parameters ranging from neuronal excitability that affects all cells to targeted local connections modified by a new adaptive plasticity rule, and the regulation of the interactive feedback affecting the external input stream of information, allow the network dynamics to switch between stable domains delimited by highly discontinuous boundaries and reach very high levels of complexity with specific configurations. The significance of this approach with regard to brain circuit studies is briefly discussed.

Axon Collaterals and Brain States

Multiple mechanisms have been identified as relevant to plasticity, functional stability, and reliable processing across brain states. In the context of stability under "ever-changing conditions" (this Topic), the role of axons has been relatively under-investigated. The highly branched topologies of many axons, however, seem well designed to differentially recruit and regulate distributed postsynaptic groups, possibly in a state-dependent fashion. In this Perspective, I briefly discuss several examples of axon collateralization, and then some of the branch-specific features that might subserve differential recruitment and whole brain activation. An emerging principle is that the number of collaterals and number of target structures are not stereotyped. Rather, axons originating from one defined source typically send branches to diversified subsets of target areas. This could achieve heterogeneous inputs, with different degrees of synchronicity. Variability of neuronal responses has been suggested as inversely proportional to the degree of temporally correlated input. Increased input homogeneity, driven by sensory stimulation or behavioral conditions, is reported to reduce neuronal variability, with axon collateralization potentially having an important role.

Firing-rate based network modeling of the dLGN circuit: Effects of cortical feedback on spatiotemporal response properties of relay cells

Visually evoked signals in the retina pass through the dorsal geniculate nucleus (dLGN) on the way to the visual cortex. This is however not a simple feedforward flow of information: there is a significant feedback from cortical cells back to both relay cells and interneurons in the dLGN. Despite four decades of experimental and theoretical studies, the functional role of this feedback is still debated. Here we use a firing-rate model, the extended difference-of-Gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. For this model the responses are found by direct evaluation of two- or three-dimensional integrals allowing for fast and comprehensive studies of putative effects of different candidate organizations of the cortical feedback. Our analysis identifies a special mixed configuration of excitatory and inhibitory cortical feedback which seems to best account for available experimental data. This configuration consists of (i) a slow (long-delay) and spatially widespread inhibitory feedback, combined with (ii) a fast (short-delayed) and spatially narrow excitatory feedback, where (iii) the excitatory/inhibitory ON-ON connections are accompanied respectively by inhibitory/excitatory OFF-ON connections, i.e. following a phase-reversed arrangement. The recent development of optogenetic and pharmacogenetic methods has provided new tools for more precise manipulation and investigation of the thalamocortical circuit, in particular for mice. Such data will expectedly allow the eDOG model to be better constrained by data from specific animal model systems than has been possible until now for cat. We have therefore made the Python tool pyLGN which allows for easy adaptation of the eDOG model to new situations.

On route from the retina to primary visual cortex, visually evoked signals have to pass through the dorsal lateral geniculate nucleus (dLGN). However, this is not an exclusive feedforward flow of information as feedback exists from neurons in the cortex back to both relay cells and interneurons in the dLGN. The functional role of this feedback remains mostly unresolved. Here, we use a firing-rate model, the extended difference-of-Gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. Our analysis indicates that a particular mix of excitatory and inhibitory cortical feedback agrees best with available experimental observations. In this configuration ON-center relay cells receive both excitatory and (indirect) inhibitory feedback from ON-center cortical cells (ON-ON feedback) where the excitatory feedback is fast and spatially narrow while the inhibitory feedback is slow and spatially widespread. In addition to the ON-ON feedback, the connections are accompanied by OFF-ON connections following a so-called phase-reversed (push-pull) arrangement. To facilitate further applications of the model, we have made the Python tool pyLGN which allows for easy modification and evaluation of the a priori quite general eDOG model to new situations.

Subset of Cortical Layer 6b Neurons Selectively Innervates Higher Order Thalamic Nuclei in Mice

The thalamus receives input from 3 distinct cortical layers, but input from only 2 of these has been well characterized. We therefore investigated whether the third input, derived from layer 6b, is more similar to the projections from layer 6a or layer 5. We studied the projections of a restricted population of deep layer 6 cells ("layer 6b cells") taking advantage of the transgenic mouse Tg(Drd1a-cre)FK164Gsat/Mmucd (Drd1a-Cre), that selectively expresses Cre-recombinase in a subpopulation of layer 6b neurons across the entire cortical mantle. At P8, 18% of layer 6b neurons are labeled with Drd1a-Cre::tdTomato in somatosensory cortex (SS), and some co-express known layer 6b markers. Using Cre-dependent viral tracing, we identified topographical projections to higher order thalamic nuclei. VGluT1+ synapses formed by labeled layer 6b projections were found in posterior thalamic nucleus (Po) but not in the (pre)thalamic reticular nucleus (TRN). The lack of TRN collaterals was confirmed with single-cell tracing from SS. Transmission electron microscopy comparison of terminal varicosities from layer 5 and layer 6b axons in Po showed that L6b varicosities are markedly smaller and simpler than the majority from L5. Our results suggest that L6b projections to the thalamus are distinct from both L5 and L6a projections.