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The cerebral cortex can perform powerful computations, including those involved in higher cognitive functions. Cortical processing for such computations is executed by local circuits and is further enriched by long-range connectivity. This connectivity is activated under specific conditions and modulates local processing, providing flexibility in the computational performance of the cortex. For instance, long-range connectivity in the primary visual cortex exerts facilitatory impacts when the cortex is silent but suppressive impacts when the cortex is strongly sensory-stimulated. These dual impacts can be captured by a divisive gain control model. Recent methodological advances such as optogenetics, anatomical tracing, and two-photon microscopy have enabled neuroscientists to probe the circuit and synaptic bases of long-range connectivity in detail. Here, I review a series of evidence indicating essential roles of long-range connectivity in visual and hierarchical processing involving numerous cortical areas. I also describe an overview of the challenges encountered in investigating underlying synaptic mechanisms and highlight recent technical approaches that may overcome these difficulties and provide new insights into synaptic mechanisms for cortical processing involving long-range connectivity.

Prepared movements are more efficient than those that are not prepared for. Although changes in cortical activity have been observed prior to a forthcoming action, the circuits involved in motor preparation remain unclear. Here, we use in vivo two-photon calcium imaging to uncover changes in the motor cortex during variable waiting periods prior to a forepaw reaching task in mice. Consistent with previous reports, we observed a subset of neurons with increased activity during the waiting period; however, these neurons did not account for the degree of preparation as defined by reaction time (RT). Instead, the suppression of activity of distinct neurons in the same cortical area better accounts for RT. This suppression of neural activity resulted in a distinct and reproducible pattern when mice were well prepared. Thus, the selective suppression of network activity in the motor cortex may be a key feature of prepared movements.

Cortical plasticity is fundamental to motor recovery following cortical perturbation. However, it is still unclear how this plasticity is induced at a functional circuit level. Here, we investigated motor recovery and underlying neural plasticity upon optogenetic suppression of a cortical area for eye movement. Using a visually-guided eye movement task in mice, we suppressed a portion of the secondary motor cortex (MOs) that encodes contraversive eye movement. Optogenetic unilateral suppression severely impaired contraversive movement on the first day. However, on subsequent days the suppression became inefficient and capability for the movement was restored. Longitudinal two-photon calcium imaging revealed that the regained capability was accompanied by an increased number of neurons encoding for ipsiversive movement in the unsuppressed contralateral MOs. Additional suppression of the contralateral MOs impaired the recovered movement again, indicating a compensatory mechanism. Our findings demonstrate that repeated optogenetic suppression leads to functional recovery mediated by the contralateral hemisphere.

Neurons in visual cortex are connected not only locally, but also through networks of distal connectivity. These distal networks recruit both excitatory and inhibitory synapses and result in divisive normalization. Normalization is traditionally thought to result from increases in synaptic inhibition. By combining optogenetic stimulation and intracellular recordings in mouse visual cortex, we found that, on the contrary, normalization is a result of a decrease in synaptic excitation.

Midbrain dopamine neurons signal reward value, their prediction error, and the salience of events. If they play a critical role in achieving specific distant goals, long-term future rewards should also be encoded as suggested in reinforcement learning theories. Here, we address this experimentally untested issue. We recorded 185 dopamine neurons in three monkeys that performed a multistep choice task in which they explored a reward target among alternatives and then exploited that knowledge to receive one or two additional rewards by choosing the same target in a set of subsequent trials. An analysis of anticipatory licking for reward water indicated that the monkeys did not anticipate an immediately expected reward in individual trials; rather, they anticipated the sum of immediate and multiple future rewards. In accordance with this behavioral observation, the dopamine responses to the start cues and reinforcer beeps reflected the expected values of the multiple future rewards and their errors, respectively. More specifically, when monkeys learned the multistep choice task over the course of several weeks, the responses of dopamine neurons encoded the sum of the immediate and expected multiple future rewards. The dopamine responses were quantitatively predicted by theoretical descriptions of the value function with time discounting in reinforcement learning. These findings demonstrate that dopamine neurons learn to encode the long-term value of multiple future rewards with distant rewards discounted.

Genetically encoded voltage indicators (GEVIs) promise to reveal the membrane potential of genetically targeted neuronal populations through noninvasive, chronic imaging of large portions of cortical space. Here we test a promising GEVI in mouse cortex during wakefulness, a challenging condition due to large hemodynamic activity, and we introduce a straightforward projection method to separate a signal dominated by membrane voltage from a signal dominated by hemodynamic activity. We expressed VSFP-Butterfly 1.2 plasmid in layer 2/3 pyramidal cells of visual cortex through electroporation in utero. We then used wide-field imaging with two cameras to measure both fluorophores of the indicator in response to visual stimuli. By taking weighted sums and differences of the two measurements, we obtained clear separation of hemodynamic and voltage signals. The hemodynamic signal showed strong heartbeat oscillations, superimposed on slow dynamics similar to blood oxygen level-dependent (BOLD) or “intrinsic” signals. The voltage signal had fast dynamics similar to neural responses measured electrically, and showed an orderly retinotopic mapping. We compared this voltage signal with calcium signals imaged in transgenic mice that express a calcium indicator (GCaMP3) throughout cortex. The voltage signal from VSFP had similar signal-to-noise ratios as the calcium signal, it was more immune to vascular artifacts, and it integrated over larger regions of visual space, which was consistent with its reporting mostly subthreshold activity rather than the spiking activity revealed by calcium signals. These results demonstrate that GEVIs provide a powerful tool to study the dynamics of neural populations at mesoscopic spatial scales in the awake cortex.

Neurons in different locations across the cortex are connected through polysynaptic networks involving both excitation and inhibition. To probe the functional effect of such networks, we used optogenetic stimulation to trigger antidromic spikes in a local region of primary visual cortex (V1). This local activity had two effects at distal V1 locations: summation and division. The balance between the two depended on visual contrast, and a normalization model precisely captured these effects.

Electrode recordings and imaging studies have revealed that localized visual stimuli elicit waves of activity that travel across primary visual cortex. Traveling waves are present also during spontaneous activity, but they can be greatly reduced by widespread and intensive visual stimulation. In this Review, we summarize the evidence in favor of these traveling waves. We suggest that their substrate may lie in long-range horizontal connections and that their functional role may involve the integration of information over large regions of space.

We investigated the chicken auditory system to understand how an interaural level difference (ILD) is processed. Sound intensity is extracted in the nucleus angularis (NA) and an ILD is processed in the dorsal lateral lemniscal nucleus (LLD). We found that the neural activity in these nuclei is affected by the interaural phase difference (IPD). Activity in the NA was suppressed by strong contralateral sound when binaural stimuli were presented in-phase, but the activity was enhanced by out-of-phase stimuli. These IPD dependent suppression or enhancement probably occurs through acoustic interference across the interaural canal connecting the middle ears of the two sides. The LLD neurons were excited by contralateral sound and inhibited by ipsilateral sound, reflecting excitation by the contralateral NA and inhibition from the ipsilateral NA, probably through the contralateral LLD as in the barn owl. The LLD unit activity encoded an ILD and was strongly modulated by the IPD. We propose a simple model to explain the interaural coupling effects and IPD modulation of LLD activity, and conclude that the modulation of neuronal activity by IPD may improve ILD processing and the direction sensitivity of LLD neurons to the contralateral ear, compensating for the small ILD cues.

Nucleus magnocellularis (NM) is one of the subnuclei of the avian cochlear nucleus and has a role of extracting the temporal information of sound from the auditory nerve fibers (ANFs). Neurons in NM are varied along the tonotopic axis in synaptic transmission and membrane excitability and are high-fidelity relay neurons at the high to middle characteristic frequency (CF) regions. Here we have compared the firing properties between ANFs and NM neurons in vivo and found that at high but not near threshold intensities, spike firings are more phase-locked in NM than in ANFs in the CF region

We recorded the activity of midbrain dopamine neurons in an instrumental conditioning task in which monkeys made a series of behavioral decisions on the basis of distinct reward expectations. Dopamine neurons responded to the first visual cue that appeared in each trial [conditioned stimulus (CS)] through which monkeys initiated trial for decision while expecting trial-specific reward probability and volume. The magnitude of neuronal responses to the CS was approximately proportional to reward expectations but with considerable discrepancy. In contrast, CS responses appear to represent motivational properties, because their magnitude at trials with identical reward expectation had significant negative correlation with reaction times of the animal after the CS. Dopamine neurons also responded to reinforcers that occurred after behavioral decisions, and the responses precisely encoded positive and negative reward expectation errors (REEs). The gain of coding REEs by spike frequency increased during learning act-outcome contingencies through a few months of task training, whereas coding of motivational properties remained consistent during the learning. We found that the magnitude of CS responses was positively correlated with that of reinforcers, suggesting a modulation of the effectiveness of REEs as a teaching signal by motivation. For instance, rate of learning could be faster when animals are motivated, whereas it could be slower when less motivated, even at identical REEs. Therefore, the dual correlated coding of motivation and REEs suggested the involvement of the dopamine system, both in reinforcement in more elaborate ways than currently proposed and in motivational function in reward-based decision-making and learning.