Hippocampal Interneurons are Required for Trace Eyeblink Conditioning in Mice.

While the hippocampus has been implicated in supporting the association among time-separated events, the underlying cellular mechanisms have not been fully clarified. Here, we combined in vivo multi-channel recording and optogenetics to investigate the activity of hippocampal interneurons in freely-moving mice performing a trace eyeblink conditioning (tEBC) task. We found that the hippocampal interneurons exhibited conditioned stimulus (CS)-evoked sustained activity, which predicted the performance of conditioned eyeblink responses (CRs) in the early acquisition of the tEBC. Consistent with this, greater proportions of hippocampal pyramidal cells showed CS-evoked decreased activity in the early acquisition of the tEBC. Moreover, optogenetic suppression of the sustained activity in hippocampal interneurons severely impaired acquisition of the tEBC. In contrast, suppression of the sustained activity of hippocampal interneurons had no effect on the performance of well-learned CRs. Our findings highlight the role of hippocampal interneurons in the tEBC, and point to a potential cellular mechanism subserving associative learning.

Hippocampal Interneurons are Required for Trace Eyeblink Conditioning in Mice / 神经科学通报·英文版

While the hippocampus has been implicated in supporting the association among time-separated events, the underlying cellular mechanisms have not been fully clarified. Here, we combined in vivo multi-channel recording and optogenetics to investigate the activity of hippocampal interneurons in freely-moving mice performing a trace eyeblink conditioning (tEBC) task. We found that the hippocampal interneurons exhibited conditioned stimulus (CS)-evoked sustained activity, which predicted the performance of conditioned eyeblink responses (CRs) in the early acquisition of the tEBC. Consistent with this, greater proportions of hippocampal pyramidal cells showed CS-evoked decreased activity in the early acquisition of the tEBC. Moreover, optogenetic suppression of the sustained activity in hippocampal interneurons severely impaired acquisition of the tEBC. In contrast, suppression of the sustained activity of hippocampal interneurons had no effect on the performance of well-learned CRs. Our findings highlight the role of hippocampal interneurons in the tEBC, and point to a potential cellular mechanism subserving associative learning.

A Slow Short-Term Depression at Purkinje to Deep Cerebellar Nuclear Neuron Synapses Supports Gain-Control and Linear Encoding over Second-Long Time Windows.

Modifications in the sensitivity of neural elements allow the brain to adapt its functions to varying demands. Frequency-dependent short-term synaptic depression (STD) provides a dynamic gain-control mechanism enabling adaptation to different background conditions alongside enhanced sensitivity to input-driven changes in activity. In contrast, synapses displaying frequency-invariant transmission can faithfully transfer ongoing presynaptic rates enabling linear processing, deemed critical for many functions. However, rigid frequency-invariant transmission may lead to runaway dynamics and low sensitivity to changes in rate. Here, I investigated the Purkinje cell to deep cerebellar nuclei neuron synapses (PC_DCNs), which display frequency invariance, and yet, PCs maintain background activity at disparate rates, even at rest. Using protracted PC_DCN activation (120 s) to mimic background activity in cerebellar slices from mature mice of both sexes, I identified a previously unrecognized, frequency-dependent, slow STD (S-STD), adapting IPSC amplitudes in tens of seconds to minutes. However, after changes in activation rates, over a behavior-relevant second-long time window, S-STD enabled scaled linear encoding of PC rates in synaptic charge transfer and DCN spiking activity. Combined electrophysiology, optogenetics, and statistical analysis suggested that S-STD mechanism is input-specific, involving decreased ready-to-release quanta, and distinct from faster short-term plasticity (f-STP). Accordingly, an S-STD component with a scaling effect (i.e., activity-dependent release sites inactivation), extending a model explaining PC_DCN release on shorter timescales using balanced f-STP, reproduced the experimental results. Thus, these results elucidates a novel slow gain-control mechanism able to support linear transfer of behavior-driven/learned PC rates concurrently with background activity adaptation, and furthermore, provides an alternative pathway to refine PC output.SIGNIFICANCE STATEMENT The brain can adapt to varying demands by dynamically changing the gain of its synapses; however, some tasks require ongoing linear transfer of presynaptic rates, seemingly incompatible with nonlinear gain adaptation. Here, I report a novel slow gain-control mechanism enabling scaled linear encoding of presynaptic rates over behavior-relevant time windows, and adaptation to background activity at the Purkinje to deep cerebellar nuclear neurons synapses (PC_DCNs). A previously unrecognized PC_DCNs slow and frequency-dependent short-term synaptic depression (S-STD) mediates this process. Experimental evidence and simulations suggested that scaled linear encoding emerges from the combination of S-STD slow dynamics and frequency-invariant transmission at faster timescales. These results demonstrate a mechanism reconciling rate code with background activity adaptation and suitable for flexibly tuning PCs output via background activity modulation.

Neural signatures of temporal regularity processing in sounds differ between younger and older adults.

Sensitivity to temporal regularity (e.g., recurring modulation in amplitude) is crucial for speech perception. Degradation of the auditory periphery due to aging and hearing loss may lead to increased responsiveness to sound in the auditory cortex, with potential consequences for the processing of temporal regularities. We used electroencephalography recorded from younger (19-33 years) and older adults (55-76 years) to investigate whether younger and older listeners differ in responsiveness to sound and sensitivity to amplitude modulation in sounds. Aging was associated with reduced adaptation in the auditory cortex, suggesting an age-related increase in responsiveness. Furthermore, neural synchronization in the auditory cortex to 4-Hz amplitude-modulated narrow-band noises was enhanced in ∼30% of older individuals. Despite enhanced responsiveness and synchronization in the auditory cortex, sustained neural activity (likely involving auditory and higher-order regions) in response to amplitude modulation was absent in older people. Aging appears to be associated with over-responsiveness to amplitude modulation in the auditory cortex, but with diminished regularity sensitivity in higher-order areas.

Considerable differences between auditory medulla, auditory midbrain, and hippocampal synapses during sustained high-frequency stimulation: Exceptional vesicle replenishment restricted to sound localization circuit.

Reliable synaptic transmission is essential for interneuronal communication. Synaptic inputs to auditory brainstem neurons, particularly those involved in sound localization, are characterized by resilience during sustained activity and temporal precision in the sub-millisecond range. Both features are obtained by synchronous release of a high number of synaptic vesicles following a single action potential. Here, we compare transmission behavior of three heterogeneous types of inputs in the auditory midbrain and medulla. The first terminate in the central inferior colliculus (ICc) and are glutamatergic (activated from the lateral lemniscus, LL). The medullary inputs terminate in the lateral superior olive (LSO) and are glutamatergic (from the cochlear nuclear complex, CN) or glycinergic (from the medial nucleus of the trapezoid body, MNTB). LSO neurons are the first to integrate binaural information and compute interaural level differences, whereas ICc neurons receive information from almost all auditory brainstem nuclei and construct an initial auditory image used for reflexive behavior. We hypothesized that CN-LSO and MNTB-LSO inputs are more resilient to synaptic fatigue during sustained stimulation than LL-ICc inputs. To test the hypothesis, we performed whole-cell patch-clamp recordings in acute brainstem slices of juvenile mice. We investigated the synaptic performance during prolonged periods of high-frequency stimulation (60 s, up to 200 Hz) and assessed several features, e.g. depression, recovery, latency, temporal precision, quantal size and content, readily releasable pool size, release probability, and replenishment rate. Overall, LL-ICc inputs performed less robustly and temporally precisely than CN-LSO and MNTB-LSO inputs. When stimulated at ≥50 Hz, the former depressed completely within a few seconds. In contrast, CN-LSO and MNTB-LSO inputs transmitted faithfully up to 200 Hz, indicative of very efficient replenishment mechanisms. LSO inputs also displayed considerably lower latency jitter than LL-ICc inputs. The latter behaved similarly to two types of input in the hippocampus for which we performed a meta-analysis. Mechanistically, the high-fidelity behavior of LSO inputs, particularly MNTB-LSO synapses, is based on exceptional release properties not present at auditory midbrain or hippocampal inputs. We conclude that robustness and temporal precision are hallmarks of auditory synapses in the medullary brainstem. These key features are less eminent at higher stations, such as the ICc, and they are also absent outside the central auditory system, namely the hippocampal formation.

Neural Signatures of the Processing of Temporal Patterns in Sound.

The ability to detect regularities in sound (i.e., recurring structure) is critical for effective perception, enabling, for example, change detection and prediction. Two seemingly unconnected lines of research concern the neural operations involved in processing regularities: one investigates how neural activity synchronizes with temporal regularities (e.g., frequency modulation; FM) in sounds, whereas the other focuses on increases in sustained activity during stimulation with repeating tone-frequency patterns. In three electroencephalography studies with male and female human participants, we investigated whether neural synchronization and sustained neural activity are dissociable, or whether they are functionally interdependent. Experiment I demonstrated that neural activity synchronizes with temporal regularity (FM) in sounds, and that sustained activity increases concomitantly. In Experiment II, phase coherence of FM in sounds was parametrically varied. Although neural synchronization was more sensitive to changes in FM coherence, such changes led to a systematic modulation of both neural synchronization and sustained activity, with magnitude increasing as coherence increased. In Experiment III, participants either performed a duration categorization task on the sounds, or a visual object tracking task to distract attention. Neural synchronization was observed regardless of task, whereas the sustained response was observed only when attention was on the auditory task, not under (visual) distraction. The results suggest that neural synchronization and sustained activity levels are functionally linked: both are sensitive to regularities in sounds. However, neural synchronization might reflect a more sensory-driven response to regularity, compared with sustained activity which may be influenced by attentional, contextual, or other experiential factors.SIGNIFICANCE STATEMENT Optimal perception requires that the auditory system detects regularities in sounds. Synchronized neural activity and increases in sustained neural activity both appear to index the detection of a regularity, but the functional interrelation of these two neural signatures is unknown. In three electroencephalography experiments, we measured both signatures concomitantly while listeners were presented with sounds containing frequency modulations that differed in their regularity. We observed that both neural signatures are sensitive to temporal regularity in sounds, although they functionally decouple when a listener is distracted by a demanding visual task. Our data suggest that neural synchronization reflects a more automatic response to regularity compared with sustained activity, which may be influenced by attentional, contextual, or other experiential factors.

Test-retest reliability of wide-pulse high-frequency neuromuscular electrical stimulation evoked force.

INTRODUCTION: We compare forces evoked by wide-pulse high-frequency (WPHF) neuromuscular electrical stimulation (NMES) delivered to a nerve trunk versus muscle belly and assess their test-retest intraindividual and interindividual reliability. METHODS: Forces evoked during 2 sessions with WPHF NMES delivered over the tibial nerve trunk and 2 sessions over the triceps surae muscle belly were compared. Ten individuals participated in 4 sessions involving ten 20-s WPHF NMES contractions interspaced by 40-s recovery. Mean evoked force and force time integral of each contraction were quantified. RESULTS: For both nerve trunk and muscle belly stimulation, intraindividual test-retest reliability was good (intraclass correlation coefficient > 0.9), and interindividual variability was large (coefficient of variation between 140% and 180%). Nerve trunk and muscle belly stimulation resulted in similar evoked forces. DISCUSSION: WPHF NMES locations might be chosen by individual preference because intraindividual reliability was relatively good for both locations. Muscle Nerve 57: E70-E77, 2018.

Sustained Activity Encoding Working Memories: Not Fully Distributed.

Working memory (WM) is the ability to remember and manipulate information for short time intervals. Recent studies have proposed that sustained firing encoding the contents of WM is ubiquitous across cortical neurons. We review here the collective evidence supporting this claim. A variety of studies report that neurons in prefrontal, parietal, and inferotemporal association cortices show robust sustained activity encoding the location and features of memoranda during WM tasks. However, reports of WM-related sustained activity in early sensory areas are rare, and typically lack stimulus specificity. We propose that robust sustained activity that can support WM coding arises as a property of association cortices downstream from the early stages of sensory processing.

Tonotopic and Field-Specific Representation of Long-Lasting Sustained Activity in Rat Auditory Cortex.

Cortical information processing of the onset, offset, and continuous plateau of an acoustic stimulus should play an important role in acoustic object perception. To date, transient activities responding to the onset and offset of a sound have been well investigated and cortical subfields and topographic representation in these subfields, such as place code of sound frequency, have been well characterized. However, whether these cortical subfields with tonotopic representation are inherited in the sustained activities that follow transient activities and persist during the presentation of a long-lasting stimulus remains unknown, because sustained activities do not exhibit distinct, reproducible, and time-locked responses in their amplitude to be characterized by grand averaging. To address this gap in understanding, we attempted to decode sound information from densely mapped sustained activities in the rat auditory cortex using a sparse parameter estimation method called sparse logistic regression (SLR), and investigated whether and how these activities represent sound information. A microelectrode array with a grid of 10 × 10 recording sites within an area of 4.0 mm × 4.0 mm was implanted in the fourth layer of the auditory cortex in rats under isoflurane anesthesia. Sustained activities in response to long-lasting constant pure tones were recorded. SLR then was applied to discriminate the sound-induced band-specific power or phase-locking value from those of spontaneous activities. The highest decoding performance was achieved in the high-gamma band, indicating that cortical inhibitory interneurons may contribute to the sparse tonotopic representation in sustained activities by mediating synchronous activities. The estimated parameter in the SLR decoding revealed that the informative recording site had a characteristic frequency close to the test frequency. In addition, decoding of the four test frequencies demonstrated that the decoding performance of the SLR deteriorated when the test frequencies were close, supporting the hypothesis that the sustained activities were organized in a tonotopic manner. Finally, unlike transient activities, sustained activities were more informative in the belt than in the core region, indicating that higher-order auditory areas predominate over lower-order areas during sustained activities. Taken together, our results indicate that the auditory cortex processes sound information tonotopically and in a hierarchical manner.

Mechanisms of Self-Sustained Oscillatory States in Hierarchical Modular Networks with Mixtures of Electrophysiological Cell Types.

In a network with a mixture of different electrophysiological types of neurons linked by excitatory and inhibitory connections, temporal evolution leads through repeated epochs of intensive global activity separated by intervals with low activity level. This behavior mimics "up" and "down" states, experimentally observed in cortical tissues in absence of external stimuli. We interpret global dynamical features in terms of individual dynamics of the neurons. In particular, we observe that the crucial role both in interruption and in resumption of global activity is played by distributions of the membrane recovery variable within the network. We also demonstrate that the behavior of neurons is more influenced by their presynaptic environment in the network than by their formal types, assigned in accordance with their response to constant current.

Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses.

Random networks of integrate-and-fire neurons with strong current-based synapses can, unlike previously believed, assume stable states of sustained asynchronous and irregular firing, even without external random background or pacemaker neurons. We analyze the mechanisms underlying the emergence, lifetime and irregularity of such self-sustained activity states. We first demonstrate how the competition between the mean and the variance of the synaptic input leads to a non-monotonic firing-rate transfer in the network. Thus, by increasing the synaptic coupling strength, the system can become bistable: In addition to the quiescent state, a second stable fixed-point at moderate firing rates can emerge by a saddle-node bifurcation. Inherently generated fluctuations of the population firing rate around this non-trivial fixed-point can trigger transitions into the quiescent state. Hence, the trade-off between the magnitude of the population-rate fluctuations and the size of the basin of attraction of the non-trivial rate fixed-point determines the onset and the lifetime of self-sustained activity states. During self-sustained activity, individual neuronal activity is moreover highly irregular, switching between long periods of low firing rate to short burst-like states. We show that this is an effect of the strong synaptic weights and the finite time constant of synaptic and neuronal integration, and can actually serve to stabilize the self-sustained state.

Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types.

The cerebral cortex exhibits neural activity even in the absence of external stimuli. This self-sustained activity is characterized by irregular firing of individual neurons and population oscillations with a broad frequency range. Questions that arise in this context, are: What are the mechanisms responsible for the existence of neuronal spiking activity in the cortex without external input? Do these mechanisms depend on the structural organization of the cortical connections? Do they depend on intrinsic characteristics of the cortical neurons? To approach the answers to these questions, we have used computer simulations of cortical network models. Our networks have hierarchical modular architecture and are composed of combinations of neuron models that reproduce the firing behavior of the five main cortical electrophysiological cell classes: regular spiking (RS), chattering (CH), intrinsically bursting (IB), low threshold spiking (LTS), and fast spiking (FS). The population of excitatory neurons is built of RS cells (always present) and either CH or IB cells. Inhibitory neurons belong to the same class, either LTS or FS. Long-lived self-sustained activity states in our network simulations display irregular single neuron firing and oscillatory activity similar to experimentally measured ones. The duration of self-sustained activity strongly depends on the initial conditions, suggesting a transient chaotic regime. Extensive analysis of the self-sustained activity states showed that their lifetime expectancy increases with the number of network modules and is favored when the network is composed of excitatory neurons of the RS and CH classes combined with inhibitory neurons of the LTS class. These results indicate that the existence and properties of the self-sustained cortical activity states depend on both the topology of the network and the neuronal mixture that comprises the network.

Building blocks of self-sustained activity in a simple deterministic model of excitable neural networks.

Understanding the interplay of topology and dynamics of excitable neural networks is one of the major challenges in computational neuroscience. Here we employ a simple deterministic excitable model to explore how network-wide activation patterns are shaped by network architecture. Our observables are co-activation patterns, together with the average activity of the network and the periodicities in the excitation density. Our main results are: (1) the dependence of the correlation between the adjacency matrix and the instantaneous (zero time delay) co-activation matrix on global network features (clustering, modularity, scale-free degree distribution), (2) a correlation between the average activity and the amount of small cycles in the graph, and (3) a microscopic understanding of the contributions by 3-node and 4-node cycles to sustained activity.

Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations.

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information processing.