A cholinergic mechanism for reward timing within primary visual cortex

Calcium Imaging of Basal Forebrain Activity during Innate and Learned Behaviors

The basal forebrain (BF) plays crucial roles in arousal, attention, and memory, and its impairment is associated with a variety of cognitive deficits. The BF consists of cholinergic, GABAergic, and glutamatergic neurons. Electrical or optogenetic stimulation of BF cholinergic neurons enhances cortical processing and behavioral performance, but the natural activity of these cells during behavior is only beginning to be characterized. Even less is known about GABAergic and glutamatergic neurons. Here, we performed microendoscopic calcium imaging of BF neurons as mice engaged in spontaneous behaviors in their home cages (innate) or performed a go/no-go auditory discrimination task (learned). Cholinergic neurons were consistently excited during movement, including running and licking, but GABAergic and glutamatergic neurons exhibited diverse responses. All cell types were activated by overt punishment, either inside or outside of the discrimination task. These findings reveal functional similarities and distinctions between BF cell types during both spontaneous and task-related behaviors.

Neurocomputational Models of Interval and Pattern Timing

Most of the computations and tasks performed by the brain require the ability to tell time, and process and generate temporal patterns. Thus, there is a diverse set of neural mechanisms in place to allow the brain to tell time across a wide range of scales: from interaural delays on the order of microseconds to circadian rhythms and beyond. Temporal processing is most sophisticated on the scale of tens of milliseconds to a few seconds, because it is within this range that the brain must recognize and produce complex temporal patterns-such as those that characterize speech and music. Most models of timing, however, have focused primarily on simple intervals and durations, thus it is not clear whether they will generalize to complex pattern-based temporal tasks. Here, we review neurobiologically based models of timing in the subsecond range, focusing on whether they generalize to tasks that require placing consecutive intervals in the context of an overall pattern, that is, pattern timing.

Timing in the visual cortex and its investigation

While many high-level cortical areas have been implicated in timing, timing activity has also been observed even in the earliest cortical stages of the visual system over the past decade. This activity has been formally modeled as one arising from a reinforcement signal, leading to testable hypotheses with recent experimental support, demonstrating the necessity and sufficiency of that reinforcement signal. As observed in other cortical areas implicated in timing, interval timing activity within the visual cortex abides by the temporal scalar property. Finally, perturbations of the visual cortex during interval timing results in lawful shifts in timing. These and related observations advance the notion that visual cortex is a substrate for learning and expressing visually-associated temporal expectations governing behaviorally-relevant actions.

Learning and Recognition of a Non-conscious Sequence of Events in Human Primary Visual Cortex

Human primary visual cortex (V1) has long been associated with learning simple low-level visual discriminations [1] and is classically considered outside of neural systems that support high-level cognitive behavior in contexts that differ from the original conditions of learning, such as recognition memory [2, 3]. Here, we used a novel fMRI-based dichoptic masking protocol—designed to induce activity in V1, without modulation from visual awareness—to test whether human V1 is implicated in human observers rapidly learning and then later (15–20 min) recognizing a non-conscious and complex (second-order) visuospatial sequence. Learning was associated with a change in V1 activity, as part of a temporo-occipital and basal ganglia network, which is at variance with the cortico-cerebellar network identified in prior studies of “implicit” sequence learning that involved motor responses and visible stimuli (e.g., [4]). Recognition memory was associated with V1 activity, as part of a temporo-occipital network involving the hippocampus, under conditions that were not imputable to mechanisms associated with conscious retrieval. Notably, the V1 responses during learning and recognition separately predicted non-conscious recognition memory, and functional coupling between V1 and the hippocampus was enhanced for old retrieval cues. The results provide a basis for novel hypotheses about the signals that can drive recognition memory, because these data (1) identify human V1 with a memory network that can code complex associative serial visuospatial information and support later non-conscious recognition memory-guided behavior (cf. [5]) and (2) align with mouse models of experience-dependent V1 plasticity in learning and memory [6].

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Sequence learning and recognition memory can operate without visual awareness

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V1, hippocampus, and basal ganglia support learning of a non-conscious sequence

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Old/new status of non-conscious recognition probes modulates V1-hippocampal coupling

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V1 activity predicts non-conscious recognition memory performance

Optogenetic Dissection of the Basal Forebrain Neuromodulatory Control of Cortical Activation, Plasticity, and Cognition

The basal forebrain (BF) houses major ascending projections to the entire neocortex that have long been implicated in arousal, learning, and attention. The disruption of the BF has been linked with major neurological disorders, such as coma and Alzheimer's disease, as well as in normal cognitive aging. Although it is best known for its cholinergic neurons, the BF is in fact an anatomically and neurochemically complex structure. Recent studies using transgenic mouse lines to target specific BF cell types have led to a renaissance in the study of the BF and are beginning to yield new insights about cell-type-specific circuit mechanisms during behavior. These approaches enable us to determine the behavioral conditions under which cholinergic and noncholinergic BF neurons are activated and how they control cortical processing to influence behavior. Here we discuss recent advances that have expanded our knowledge about this poorly understood brain region and laid the foundation for future cell-type-specific manipulations to modulate arousal, attention, and cortical plasticity in neurological disorders.

SIGNIFICANCE STATEMENT

Although the basal forebrain is best known for, and often equated with, acetylcholine-containing neurons that provide most of the cholinergic innervation of the neocortex, it is in fact an anatomically and neurochemically complex structure. Recent studies using transgenic mouse lines to target specific cell types in the basal forebrain have led to a renaissance in this field and are beginning to dissect circuit mechanisms in the basal forebrain during behavior. This review discusses recent advances in the roles of basal forebrain cholinergic and noncholinergic neurons in cognition via their dynamic modulation of cortical activity.

A Simple Network Architecture Accounts for Diverse Reward Time Responses in Primary Visual Cortex

Many actions performed by animals and humans depend on an ability to learn, estimate, and produce temporal intervals of behavioral relevance. Exemplifying such learning of cued expectancies is the observation of reward-timing activity in the primary visual cortex (V1) of rodents, wherein neural responses to visual cues come to predict the time of future reward as behaviorally experienced in the past. These reward-timing responses exhibit significant heterogeneity in at least three qualitatively distinct classes: sustained increase or sustained decrease in firing rate until the time of expected reward, and a class of cells that reach a peak in firing at the expected delay. We elaborate upon our existing model by including inhibitory and excitatory units while imposing simple connectivity rules to demonstrate what role these inhibitory elements and the simple architectures play in sculpting the response dynamics of the network. We find that simply adding inhibition is not sufficient for obtaining the different distinct response classes, and that a broad distribution of inhibitory projections is necessary for obtaining peak-type responses. Furthermore, although changes in connection strength that modulate the effects of inhibition onto excitatory units have a strong impact on the firing rate profile of these peaked responses, the network exhibits robustness in its overall ability to predict the expected time of reward. Finally, we demonstrate how the magnitude of expected reward can be encoded at the expected delay in the network and how peaked responses express this reward expectancy.

SIGNIFICANCE STATEMENT

Heterogeneity in single-neuron responses is a common feature of neuronal systems, although sometimes, in theoretical approaches, it is treated as a nuisance and seldom considered as conveying a different aspect of a signal. In this study, we focus on the heterogeneous responses in the primary visual cortex of rodents trained with a predictable delayed reward time. We describe under what conditions this heterogeneity can arise by self-organization, and what information it can convey. This study, while focusing on a specific system, provides insight onto how heterogeneity can arise in general while also shedding light onto mechanisms of reinforcement learning using realistic biological assumptions.

Central Cholinergic Neurons Are Rapidly Recruited by Reinforcement Feedback

Basal forebrain cholinergic neurons constitute a major neuromodulatory system implicated in normal cognition and neurodegenerative dementias. Cholinergic projections densely innervate neocortex, releasing acetylcholine to regulate arousal, attention, and learning. However, their precise behavioral function is poorly understood because identified cholinergic neurons have never been recorded during behavior. To determine which aspects of cognition their activity might support, we recorded cholinergic neurons using optogenetic identification in mice performing an auditory detection task requiring sustained attention. We found that a non-cholinergic basal forebrain population-but not cholinergic neurons-were correlated with trial-to-trial measures of attention. Surprisingly, cholinergic neurons responded to reward and punishment with unusual speed and precision (18 ± 3 ms). Cholinergic responses were scaled by the unexpectedness of reinforcement and were highly similar across neurons and two nuclei innervating distinct cortical areas. These results reveal that the cholinergic system broadcasts a rapid and precisely timed reinforcement signal, supporting fast cortical activation and plasticity.

Distinct Eligibility Traces for LTP and LTD in Cortical Synapses

In reward-based learning, synaptic modifications depend on a brief stimulus and a temporally delayed reward, which poses the question of how synaptic activity patterns associate with a delayed reward. A theoretical solution to this so-called distal reward problem has been the notion of activity-generated "synaptic eligibility traces," silent and transient synaptic tags that can be converted into long-term changes in synaptic strength by reward-linked neuromodulators. Here we report the first experimental demonstration of eligibility traces in cortical synapses. We demonstrate the Hebbian induction of distinct traces for LTP and LTD and their subsequent timing-dependent transformation into lasting changes by specific monoaminergic receptors anchored to postsynaptic proteins. Notably, the temporal properties of these transient traces allow stable learning in a recurrent neural network that accurately predicts the timing of the reward, further validating the induction and transformation of eligibility traces for LTP and LTD as a plausible synaptic substrate for reward-based learning.

Spatial diversity of spontaneous activity in the cortex

The neocortex is a layered sheet across which a basic organization is thought to widely apply. The variety of spontaneous activity patterns is similar throughout the cortex, consistent with the notion of a basic cortical organization. However, the basic organization is only an outline which needs adjustments and additions to account for the structural and functional diversity across cortical layers and areas. Such diversity suggests that spontaneous activity is spatially diverse in any particular behavioral state. Accordingly, this review summarizes the laminar and areal diversity in cortical activity during fixation and slow oscillations, and the effects of attention, anesthesia and plasticity on the cortical distribution of spontaneous activity. Among questions that remain open, characterizing the spatial diversity in spontaneous membrane potential may help elucidate how differences in circuitry among cortical regions supports their varied functions. More work is also needed to understand whether cortical spontaneous activity not only reflects cortical circuitry, but also contributes to determining the outcome of plasticity, so that it is itself a factor shaping the functional diversity of the cortex.

Theta Oscillations in Visual Cortex Emerge with Experience to Convey Expected Reward Time and Experienced Reward Rate

The primary visual cortex (V1) is widely regarded as faithfully conveying the physical properties of visual stimuli. Thus, experience-induced changes in V1 are often interpreted as improving visual perception (i.e., perceptual learning). Here we describe how, with experience, cue-evoked oscillations emerge in V1 to convey expected reward time as well as to relate experienced reward rate. We show, in chronic multisite local field potential recordings from rat V1, that repeated presentation of visual cues induces the emergence of visually evoked oscillatory activity. Early in training, the visually evoked oscillations relate to the physical parameters of the stimuli. However, with training, the oscillations evolve to relate the time in which those stimuli foretell expected reward. Moreover, the oscillation prevalence reflects the reward rate recently experienced by the animal. Thus, training induces experience-dependent changes in V1 activity that relate to what those stimuli have come to signify behaviorally: when to expect future reward and at what rate.

Neocortical Rebound Depolarization Enhances Visual Perception

Animals are constantly exposed to the time-varying visual world. Because visual perception is modulated by immediately prior visual experience, visual cortical neurons may register recent visual history into a specific form of offline activity and link it to later visual input. To examine how preceding visual inputs interact with upcoming information at the single neuron level, we designed a simple stimulation protocol in which a brief, orientated flashing stimulus was subsequently coupled to visual stimuli with identical or different features. Using in vivo whole-cell patch-clamp recording and functional two-photon calcium imaging from the primary visual cortex (V1) of awake mice, we discovered that a flash of sinusoidal grating per se induces an early, transient activation as well as a long-delayed reactivation in V1 neurons. This late response, which started hundreds of milliseconds after the flash and persisted for approximately 2 s, was also observed in human V1 electroencephalogram. When another drifting grating stimulus arrived during the late response, the V1 neurons exhibited a sublinear, but apparently increased response, especially to the same grating orientation. In behavioral tests of mice and humans, the flashing stimulation enhanced the detection power of the identically orientated visual stimulation only when the second stimulation was presented during the time window of the late response. Therefore, V1 late responses likely provide a neural basis for admixing temporally separated stimuli and extracting identical features in time-varying visual environments.

A study of mice and humans shows that prior activity in the visual cortex induces a long-delayed depolarization that enhances perception of subsequent visual stimuli if these are identical to the previous one, thereby extracting invariant visual features from the constantly changing visual world.

Animals are constantly exposed to a visual world that varies over time. To examine how the visual cortex integrates visual information that is temporally spaced, we monitored neuronal activity of the primary visual cortex (V1) using single- and multicell recording techniques. We discovered that a brief visual stimulus induced an early, transient activation as well as a delayed reactivation of V1 neurons in mice and humans. Notably, this reactivation of visual cortex conveyed information about stimulus orientation: presentation of a second visual stimulus during this reactivation enhanced the V1 response specifically when the orientations of the two stimuli were identical. Behavioral tests in mice and humans revealed that the ability to detect visual stimuli was also enhanced when the second stimulus was presented during the time window of V1 reactivation. Because animals extract visual information from an environment in constant change, the modulation of visual responses through cortical reactivation might be a strategy commonly used in the visual system.

Visual recognition memory: a view from V1

Although work in primates on higher-order visual areas has revealed how the individual and concerted activity of neurons correlates with behavioral reports of object recognition, very little is known about the underlying mechanisms for visual recognition memory. Low-level vision, even as early as primary visual cortex (V1) and even in subjects as unsophisticated as rodents, promises to fill this void. Although this latter approach sacrifices interrogation of many of the most astounding features of visual recognition, it does provide experimental constraint, proximity to sensory input, and a wide range of interventional approaches. The tractability of rodent visual cortex promises to reveal the molecular mechanisms and circuits that are essential for a fundamental form of memory.

Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex

We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in behavioral performance were closely associated with increasingly distinguishable population-level representations of task-relevant stimuli, as a result of stabilization of existing and recruitment of new neurons selective for these stimuli. These effects correlated with the appearance of multiple task-dependent signals during learning: those that increased neuronal selectivity across the population when expert animals engaged in the task, and those reflecting anticipation or behavioral choices specifically in neuronal subsets preferring the rewarded stimulus. Therefore, learning engages diverse mechanisms that modify sensory and non-sensory representations in V1 to adjust its processing to task requirements and the behavioral relevance of visual stimuli.

Selective activation of a putative reinforcement signal conditions cued interval timing in primary visual cortex

As a consequence of conditioning visual cues with delayed reward, cue-evoked neural activity that predicts the time of expected future reward emerges in the primary visual cortex (V1). We hypothesized that this reward-timing activity is engendered by a reinforcement signal conveying reward acquisition to V1. In lieu of behavioral conditioning, we assessed in vivo whether selective activation of either basal forebrain (BF) or cholinergic innervation is sufficient to condition cued interval-timing activity. Substituting for actual reward, optogenetic activation of BF or cholinergic input within V1 at fixed delays following visual stimulation entrains neural responses that mimic behaviorally conditioned reward-timing activity. Optogenetically conditioned neural responses express cue-evoked temporal intervals that correspond to the conditioning intervals, are bidirectionally modifiable, display experience-dependent refinement, and exhibit a scale invariance to the encoded delay. Our results demonstrate that the activation of BF or cholinergic input within V1 is sufficient to encode cued interval-timing activity and indicate that V1 itself is a substrate for associative learning that may inform the timing of visually cued behaviors.

Visually cued action timing in the primary visual cortex

Most behaviors are generated in three steps: sensing the external world, processing that information to instruct decision-making, and producing a motor action. Sensory areas, especially primary sensory cortices, have long been held to be involved only in the first step of this sequence. Here, we develop a visually cued interval timing task that requires rats to decide when to perform an action following a brief visual stimulus. Using single-unit recordings and optogenetics in this task, we show that activity generated by the primary visual cortex (V1) embodies the target interval and may instruct the decision to time the action on a trial-by-trial basis. A spiking neuronal model of local recurrent connections in V1 produces neural responses that predict and drive the timing of future actions, rationalizing our observations. Our data demonstrate that the primary visual cortex may contribute to the instruction of visually cued timed actions.

Mechanisms underlying desynchronization of cholinergic-evoked thalamic network activity

Synchronous neuronal activity in the thalamocortical system is critical for a number of behaviorally relevant computations, but hypersynchrony can limit information coding and lead to epileptiform responses. In the somatosensory thalamus, afferent inputs are transformed by networks of reciprocally connected thalamocortical neurons in the ventrobasal nucleus (VB) and GABAergic neurons in the thalamic reticular nucleus (TRN). These networks can generate oscillatory activity, and studies in vivo and in vitro have suggested that thalamic oscillations are often accompanied by synchronous neuronal activity, in part mediated by widespread divergence and convergence of both reticulothalamic and thalamoreticular pathways, as well as by electrical synapses interconnecting TRN neurons. However, the functional organization of thalamic circuits and its role in shaping input-evoked activity patterns remain poorly understood. Here we show that optogenetic activation of cholinergic synaptic afferents evokes near-synchronous firing in mouse TRN neurons that is rapidly desynchronized in thalamic networks. We identify several mechanisms responsible for desynchronization: (1) shared inhibitory inputs in local VB neurons leading to asynchronous and imprecise rebound bursting; (2) TRN-mediated lateral inhibition that further desynchronizes firing in the VB; and (3) powerful yet sparse thalamoreticular connectivity that mediates re-excitation of the TRN but preserves asynchronous firing. Our findings reveal how distinct local circuit features interact to desynchronize thalamic network activity.

Cholinergic signals in mouse barrel cortex during active whisker sensing

Internal brain states affect sensory perception, cognition, and learning. Many neocortical areas exhibit changes in the pattern and synchrony of neuronal activity during quiet versus active behaviors. Active behaviors are typically associated with desynchronized cortical dynamics. Increased thalamic firing contributes importantly to desynchronize mouse barrel cortex during active whisker sensing. However, a whisking-related cortical state change persists after thalamic inactivation, which is mediated at least in part by acetylcholine, as we show here by using whole-cell recordings, local pharmacology, axonal calcium imaging, and optogenetic stimulation. During whisking, we find prominent cholinergic signals in the barrel cortex, which suppress spontaneous cortical activity. The desynchronized state of barrel cortex during whisking is therefore driven by at least two distinct signals with opposing functions: increased thalamic activity driving glutamatergic excitation of the cortex and increased cholinergic input suppressing spontaneous cortical activity.

Encoding and tracking of outcome-specific expectancy in the gustatory cortex of alert rats

In natural conditions, gustatory stimuli are typically expected. Anticipatory and contextual cues provide information that allows animals to predict the availability and the identity of the substance to be ingested. Recording in alert rats trained to self-administer tastants following a go signal revealed that neurons in the primary gustatory cortex (GC) can respond to anticipatory cues. These experiments were optimized to demonstrate that even the most general form of expectation can activate neurons in GC, and did not provide indications on whether cues predicting different tastants could be encoded selectively by GC neurons. Here we recorded single-neuron activity in GC of rats engaged in a task where one auditory cue predicted sucrose, while another predicted quinine. We found that GC neurons respond differentially to the two cues. Cue-selective responses develop in parallel with learning. Comparison between cue and sucrose responses revealed that cues could trigger the activation of anticipatory representations. Additional experiments showed that an expectation of sucrose leads a subset of neurons to produce sucrose-like responses even when the tastant was omitted. Altogether, the data show that primary sensory cortices can encode for cues predicting different outcomes, and that specific expectations result in the activation of anticipatory representations.

Contributions of cortical feedback to sensory processing in primary visual cortex

Closing the structure-function divide is more challenging in the brain than in any other organ (Lichtman and Denk, 2011). For example, in early visual cortex, feedback projections to V1 can be quantified (e.g., Budd, 1998) but the understanding of feedback function is comparatively rudimentary (Muckli and Petro, 2013). Focusing on the function of feedback, we discuss how textbook descriptions mask the complexity of V1 responses, and how feedback and local activity reflects not only sensory processing but internal brain states.

Boosting visual cortex function and plasticity with acetylcholine to enhance visual perception

The cholinergic system is a potent neuromodulatory system that plays critical roles in cortical plasticity, attention and learning. In this review, we propose that the cellular effects of acetylcholine (ACh) in the primary visual cortex during the processing of visual inputs might induce perceptual learning; i.e., long-term changes in visual perception. Specifically, the pairing of cholinergic activation with visual stimulation increases the signal-to-noise ratio, cue detection ability and long-term facilitation in the primary visual cortex. This cholinergic enhancement would increase the strength of thalamocortical afferents to facilitate the treatment of a novel stimulus while decreasing the cortico-cortical signaling to reduce recurrent or top-down modulation. This balance would be mediated by different cholinergic receptor subtypes that are located on both glutamatergic and GABAergic neurons of the different cortical layers. The mechanisms of cholinergic enhancement are closely linked to attentional processes, long-term potentiation (LTP) and modulation of the excitatory/inhibitory balance. Recently, it was found that boosting the cholinergic system during visual training robustly enhances sensory perception in a long-term manner. Our hypothesis is that repetitive pairing of cholinergic and sensory stimulation over a long period of time induces long-term changes in the processing of trained stimuli that might improve perceptual ability. Various non-invasive approaches to the activation of the cholinergic neurons have strong potential to improve visual perception.

Designing tools for assumption-proof brain mapping

Can we develop technologies to systematically map classical mechanisms throughout the brain, while retaining the flexibility to investigate new mechanisms as they are discovered? We discuss principles of scalable, flexible technologies that could yield comprehensive maps of brain function.

Higher brain functions served by the lowly rodent primary visual cortex

It has been more than 50 years since the first description of ocular dominance plasticity--the profound modification of primary visual cortex (V1) following temporary monocular deprivation. This discovery immediately attracted the intense interest of neurobiologists focused on the general question of how experience and deprivation modify the brain as a potential substrate for learning and memory. The pace of discovery has quickened considerably in recent years as mice have become the preferred species to study visual cortical plasticity, and new studies have overturned the dogma that primary sensory cortex is immutable after a developmental critical period. Recent work has shown that, in addition to ocular dominance plasticity, adult visual cortex exhibits several forms of response modification previously considered the exclusive province of higher cortical areas. These "higher brain functions" include neural reports of stimulus familiarity, reward-timing prediction, and spatiotemporal sequence learning. Primary visual cortex can no longer be viewed as a simple visual feature detector with static properties determined during early development. Rodent V1 is a rich and dynamic cortical area in which functions normally associated only with "higher" brain regions can be studied at the mechanistic level.

Experience-dependent plasticity of visual cortical microcircuits

The recent decade testified a tremendous increase in our knowledge on how cell-type-specific microcircuits process sensory information in the neocortex and on how such circuitry reacts to manipulations of the sensory environment. Experience-dependent plasticity has now been investigated with techniques endowed with cell resolution during both postnatal development and in adult animals. This review recapitulates the main recent findings in the field using mainly the primary visual cortex as a model system to highlight the more important questions and physiological principles (such as the role of non-competitive mechanisms, the role of inhibition in excitatory cell plasticity, the functional importance of spine and axonal plasticity on a microscale level). I will also discuss on which scientific problems the debate and controversies are more pronounced. New technologies that allow to perturbate cell-type-specific subcircuits will certainly shine new light in the years to come at least on some of the still open questions.

Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot's controller

In this paper, we investigate the operant conditioning (OC) learning process within a bio-inspired paradigm, using artificial spiking neural networks (ASNN) to act as robot brain controllers. In biological agents, OC results in behavioral changes learned from the consequences of previous actions, based on progressive prediction adjustment from rewarding or punishing signals. In a neurorobotics context, virtual and physical autonomous robots may benefit from a similar learning skill when facing unknown and unsupervised environments. In this work, we demonstrate that a simple invariant micro-circuit can sustain OC in multiple learning scenarios. The motivation for this new OC implementation model stems from the relatively complex alternatives that have been described in the computational literature and recent advances in neurobiology. Our elementary kernel includes only a few crucial neurons, synaptic links and originally from the integration of habituation and spike-timing dependent plasticity as learning rules. Using several tasks of incremental complexity, our results show that a minimal neural component set is sufficient to realize many OC procedures. Hence, with the proposed OC module, designing learning tasks with an ASNN and a bio-inspired robot context leads to simpler neural architectures for achieving complex behaviors.

Functional neuromodulation of chemosensation in vertebrates

Neuromodulation can be defined as a biophysical process that serves to modify-or modulate-the computation performed by a neuron or network as a function of task demands and behavioral state of the animal. These modulatory effects often involve substances extrinsic to the network under observation, such as acetylcholine (ACh), norepinephrine (NE), histamine, serotonin (5-HT), dopamine (DA), and a variety of neuropeptides. Olfactory and gustatory processes especially need to be adaptive and respond flexibly to changing environments, availability of resources and physiological needs. It is therefore crucial to understand the neuromodulatory processes that regulate the function of these systems.

What does scalar timing tell us about neural dynamics?

The “Scalar Timing Law,” which is a temporal domain generalization of the well known Weber Law, states that the errors estimating temporal intervals scale linearly with the durations of the intervals. Linear scaling has been studied extensively in human and animal models and holds over several orders of magnitude, though to date there is no agreed upon explanation for its physiological basis. Starting from the assumption that behavioral variability stems from neural variability, this work shows how to derive firing rate functions that are consistent with scalar timing. We show that firing rate functions with a log-power form, and a set of parameters that depend on spike count statistics, can account for scalar timing. Our derivation depends on a linear approximation, but we use simulations to validate the theory and show that log-power firing rate functions result in scalar timing over a large range of times and parameters. Simulation results match the predictions of our model, though our initial formulation results in a slight bias toward overestimation that can be corrected using a simple iterative approach to learn a decision threshold.

Basal forebrain dynamics during a tactile discrimination task

The nucleus basalis (NB) is a cholinergic neuromodulatory structure that projects liberally to the entire cortical mantle and regulates information processing in all cortical layers. Here, we recorded activity from populations of single units in the NB as rats performed a whisker-dependent tactile discrimination task. Over 80% of neurons responded with significant modulation in at least one phase of the task. Such activity started before stimulus onset and continued for seconds after reward delivery. Firing rates monotonically increased with reward magnitude during the task, suggesting that NB neurons are not indicating the absolute deviation from expected reward amounts. Individual neurons also encoded significant amounts of information about stimulus identity. Such robust coding was not present when the same stimuli were delivered to lightly anesthetized animals, suggesting that the NB neurons contain a sensorimotor, rather than purely sensory or motor, representation of the environment. Overall, these results support the hypothesis that neurons in the NB provide a value-laden representation of the sensorimotor state of the animal as it engages in significant behavioral tasks.

Learned spatiotemporal sequence recognition and prediction in primary visual cortex

Learning to recognize and predict temporal sequences is fundamental to sensory perception, and is impaired in several neuropsychiatric disorders, but little is known about where and how this occurs in the brain. We discovered that repeated presentations of a visual sequence over a course of days causes evoked response potentiation in mouse V1 that is highly specific for stimulus order and timing. Remarkably, after V1 is trained to recognize a sequence, cortical activity regenerates the full sequence even when individual stimulus elements are omitted. This novel neurophysiological report of sequence learning advances the understanding of how the brain makes “intelligent guesses” based on limited information to form visual percepts and suggests that it is possible to study the mechanistic basis of this high–level cognitive ability by studying low–level sensory systems.

Timing as an intrinsic property of neural networks: evidence from in vivo and in vitro experiments

The discrimination and production of temporal patterns on the scale of hundreds of milliseconds are critical to sensory and motor processing. Indeed, most complex behaviours, such as speech comprehension and production, would be impossible in the absence of sophisticated timing mechanisms. Despite the importance of timing to human learning and cognition, little is known about the underlying mechanisms, in particular whether timing relies on specialized dedicated circuits and mechanisms or on general and intrinsic properties of neurons and neural circuits. Here, we review experimental data describing timing and interval-selective neurons in vivo and in vitro. We also review theoretical models of timing, focusing primarily on the state-dependent network model, which proposes that timing in the subsecond range relies on the inherent time-dependent properties of neurons and the active neural dynamics within recurrent circuits. Within this framework, time is naturally encoded in populations of neurons whose pattern of activity is dynamically changing in time. Together, we argue that current experimental and theoretical studies provide sufficient evidence to conclude that at least some forms of temporal processing reflect intrinsic computations based on local neural network dynamics.

Temporal Perceptual Learning

Our interaction with the environment and each other is inherently time-varying in nature. It is thus not surprising that the nervous systems of animals have evolved sophisticated mechanisms to not only tell time, but to learn to discriminate and produce temporal patterns. Indeed some of the most sophisticated human behaviors, such as speech and music, would not exist if the human brain was unable to learn to discriminate and produce temporal patterns. Compared to the study of other forms of learning, such as visual perceptual learning, the study of the learning of interval and temporal pattern discrimination in the subsecond range is relatively recent. A growing number of studies over the past 15 years, however, have established that perceptual and motor timing undergo robust learning. One of the principles to have emerged from these studies is that temporal learning is generally specific to the trained interval, an observation that has important implications to the neural mechanisms underlying our ability to tell time.

Fast modulation of visual perception by basal forebrain cholinergic neurons

The basal forebrain provides the primary source of cholinergic input to the cortex, and it has a crucial function in promoting wakefulness and arousal. However, whether rapid changes in basal forebrain neuron spiking in awake animals can dynamically influence sensory perception is unclear. Here we show that basal forebrain cholinergic neurons rapidly regulate cortical activity and visual perception in awake, behaving mice. Optogenetic activation of the cholinergic neurons or their V1 axon terminals improved performance of a visual discrimination task on a trial-by-trial basis. In V1, basal forebrain activation enhanced visual responses and desynchronized neuronal spiking; these changes could partly account for the behavioral improvement. Conversely, optogenetic basal forebrain inactivation decreased behavioral performance, synchronized cortical activity and impaired visual responses, indicating the importance of cholinergic activity in normal visual processing. These results underscore the causal role of basal forebrain cholinergic neurons in fast, bidirectional modulation of cortical processing and sensory perception.

Plasticity in the human visual cortex: an ophthalmology-based perspective

Neuroplasticity refers to the ability of the brain to reorganize the function and structure of its connections in response to changes in the environment. Adult human visual cortex shows several manifestations of plasticity, such as perceptual learning and adaptation, working under the top-down influence of attention. Plasticity results from the interplay of several mechanisms, including the GABAergic system, epigenetic factors, mitochondrial activity, and structural remodeling of synaptic connectivity. There is also a downside of plasticity, that is, maladaptive plasticity, in which there are behavioral losses resulting from plasticity changes in the human brain. Understanding plasticity mechanisms could have major implications in the diagnosis and treatment of ocular diseases, such as retinal disorders, cataract and refractive surgery, amblyopia, and in the evaluation of surgical materials and techniques. Furthermore, eliciting plasticity could open new perspectives in the development of strategies that trigger plasticity for better medical and surgical outcomes.