A morphologically present but non-functioning synapse is termed a silent synapse. Silent synapses are categorized into \"postsynaptically silent synapses,\" where AMPA receptors are either absent or non-functional, and \"presynaptically silent synapses,\" where neurotransmitters cannot be released from nerve terminals. The presence of presynaptically silent synapses remains enigmatic, and their physiological significance is highly intriguing. In this study, we examined the distribution and developmental changes of presynaptically active and silent synapses in individual neurons. Our findings show a gradual increase in the number of excitatory synapses, along with a corresponding decrease in the percentage of presynaptically silent synapses during neuronal development. To pinpoint the distribution of presynaptically active and silent synapses, i.e., their positional information, we employed Sholl analysis. Our results indicate that the distribution of presynaptically silent synapses within a single neuron does not exhibit a distinct pattern during synapse development in different distance from the cell body. However, irrespective of neuronal development, the proportion of presynaptically silent synapses tends to rise as the projection site moves farther from the cell body, suggesting that synapses near the cell body may exhibit higher synaptic transmission efficiency. This study represents the first observation of changes in the distribution of presynaptically active and silent synapses within a single neuron.

The pedunculopontine tegmental nucleus of the brainstem (PPTg) has extensive interconnections and neuronal-behavioural correlates. It is implicated in movement control and sensorimotor integration. We investigated whether single neuron activity in freely moving rats is correlated with components of skilled forelimb movement, and whether individual neurons respond to both motor and sensory events. We found that individual PPTg neurons showed changes in firing rate at different times during the reach. This type of temporally specific modulation is like activity seen elsewhere in voluntary movement control circuits, such as the motor cortex, and suggests that PPTg neural activity is related to different specific events occurring during the reach. In particular, many neuronal modulations were time-locked to the end of the extension phase of the reach, when fine distal movements related to food grasping occur, indicating strong engagement of PPTg in this phase of skilled individual forelimb movements. In addition, some neurons showed brief periods of apparent oscillatory firing in the theta range at specific phases of the reach-to-grasp movement. When movement-related neurons were tested with tone stimuli, many also responded to this auditory input, allowing for sensorimotor integration at the cellular level. Together, these data extend the concept of the PPTg as an integrative structure in generation of complex movements, by showing that this function extends to the highly coordinated control of the forelimb during skilled reach to grasp movement, and that sensory and motor-related information converges on single neurons, allowing for direct integration at the cellular level.

How the human cortex integrates (\"binds\") information encoded by spatially distributed neurons remains largely unknown. One hypothesis suggests that synchronous bursts of high-frequency oscillations (\"ripples\") contribute to binding by facilitating integration of neuronal firing across different cortical locations. While studies have demonstrated that ripples modulate local activity in the cortex, it is not known whether their co-occurrence coordinates neural firing across larger distances. We tested this hypothesis using local field-potentials and single-unit firing from four 96-channel microelectrode arrays in the supragranular cortex of 3 patients. Neurons in co-rippling locations showed increased short-latency co-firing, prediction of each other\'s firing, and co-participation in neural assemblies. Effects were similar for putative pyramidal and interneurons, during non-rapid eye movement sleep and waking, in temporal and Rolandic cortices, and at distances up to 16 mm (the longest tested). Increased co-prediction during co-ripples was maintained when firing-rate changes were equated, indicating that it was not secondary to non-oscillatory activation. Co-rippling enhanced prediction was strongly modulated by ripple phase, supporting the most common posited mechanism for binding-by-synchrony. Co-ripple enhanced prediction is reciprocal, synergistic with local upstates, and further enhanced when multiple sites co-ripple, supporting re-entrant facilitation. Together, these results support the hypothesis that trans-cortical co-occurring ripples increase the integration of neuronal firing of neurons in different cortical locations and do so in part through phase-modulation rather than unstructured activation.

The overrepresentation of centrifugal motion in the middle temporal visual area (area MT) has long been thought to provide an efficient coding strategy for optic flow processing. However, this overrepresentation compromises the detection of approaching objects, which is essential for survival. In the present study, we revisited this long-held notion by reanalyzing motion selectivity in area MT of three macaque monkeys (two males, one female) using random-dot stimuli instead of spot stimuli. We found no differences in the number of neurons tuned to centrifugal versus centripetal motion; however, centrifugally tuned neurons showed stronger tuning than centripetally tuned neurons. This was attributed to the heightened suppression of responses in centrifugal neurons to centripetal motion compared with that of centripetal neurons to centrifugal motion. Our modeling implies that this intensified suppression accounts for superior detection performance for weak centripetal motion stimuli. Moreover, through Fisher information analysis, we establish that the population sensitivity to motion direction in peripheral vision corresponds well with retinal motion statistics during forward locomotion. While these results challenge established concepts, considering the interplay of logarithmic Gaussian receptive fields and spot stimuli can shed light on the previously documented overrepresentation of centrifugal motion. Significantly, our findings reconcile a previously found discrepancy between MT activity and human behavior, highlighting the proficiency of peripheral MT neurons in encoding motion direction efficiently.SIGNIFICANCE STATEMENT The efficient coding hypothesis states that sensory neurons are tuned to specific, frequently experienced stimuli. Whereas previous work has found that neurons in the middle temporal (MT) area favor centrifugal motion, which results from forward locomotion, we show here that there is no such bias. Moreover, we found that the response of centrifugal neurons for centripetal motion was more suppressed than that of centripetal neurons for centrifugal motion. Combined with modeling, this provides a solution to a previously known discrepancy between reported centrifugal bias in MT and better detection of centripetal motion by human observers. Additionally, we show that population sensitivity in peripheral MT neurons conforms to an efficient code of retinal motion statistics during forward locomotion.

UNASSIGNED: The spike train output correlation with pairwise neurons determines the neural population coding, which depends on the average firing rate of individual neurons. Spike frequency adaptation (SFA), which serves as an essential cellular encoding strategy, modulates the firing rates of individual neurons. However, the mechanism by which the SFA modulates the output correlation of the spike trains remains unclear.
UNASSIGNED: We introduce a pairwise neuron model that receives correlated inputs to generate spike trains, and the output correlation is qualified using Pearson correlation coefficient. The SFA is modeled using adaptation currents to examine its effect on the output correlation. Moreover, we use dynamic thresholds to explore the effect of SFA on output correlation. Furthermore, a simple phenomenological neuron model with a threshold-linear transfer function is utilized to confirm the effect of SFA on decreasing the output correlation.
UNASSIGNED: The results show that the adaptation currents decreased the output correlation by reducing the firing rate of a single neuron. At the onset of a correlated input, a transient process shows a decrease in interspike intervals (ISIs), resulting in a temporary increase in the correlation. When the adaptation current is sufficiently activated, the correlation reached a steady state, and the ISIs are maintained at higher values. The enhanced adaptation current achieved by increasing the adaptation conductance further reduces the pairwise correlation. While the time and slide windows influence the correlation, they make no difference in the effect of SFA on decreasing the output correlation. Moreover, SFA simulated by dynamic thresholds also decreases the output correlation. Furthermore, the simple phenomenological neuron model with a threshold-linear transfer function confirms the effect of SFA on decreasing the output correlation. The strength of the signal input and the slope of the linear component of the transfer function, the latter of which can be decreased by SFA, could together modulate the strength of the output correlation. Stronger SFA will decrease the slope and hence decrease the output correlation.
UNASSIGNED: The results reveal that the SFA reduces the output correlation with pairwise neurons in the network by reducing the firing rate of individual neurons. This study provides a link between cellular non-linear mechanisms and network coding strategies.

The focus of my research efforts rests with determining dysfunctional neural systems underlying disorders of sleep, and identifying interventions to overcome those disorders. Aberrant central and physiological control during sleep exerts serious consequences, including disruptions in breathing, motor control, blood pressure, mood, and cognition, and plays a major role in sudden infant death syndrome, congenital central hypoventilation, and sudden unexpected death in epilepsy, among other concerns. The disruptions can be traced to brain structural injury, leading to inappropriate outcomes. Identification of failing systems arose from the assessment of single neuron discharge in intact, freely moving and state-changing human and animal preparations within multiple systems, including serotonergic action and motor control sites. Optical imaging of chemosensitive, blood pressure and other breathing regulatory areas, especially during development, were useful to show integration of regional cellular action in modifying neural output. Identification of damaged neural sites in control and afflicted humans through structural and functional magnetic resonance imaging procedures helped to identify the sources of injury, and the nature of interactions between brain sites that compromise physiological systems and lead to failure. Interventions to overcome flawed regulatory processes were developed, and incorporate noninvasive neuromodulatory means to recruit ancient reflexes or provide peripheral sensory stimulation to assist breathing drive to overcome apnea, reduce the frequency of seizures, and support blood pressure in conditions where a failure to perfuse can lead to death.

Recent studies suggest the hippocampus is involved in working memory (WM). Slotnick (this issue) critically reviewed relevant fMRI findings and concludes WM \'does not activate the hippocampus.\' We extend Slotnick\'s review by discussing observations from human intracranial and lesion research. These studies do suggest hippocampal contributions to WM (beyond novelty encoding), which however are difficult to capture with conventional fMRI. Still, the advent of new fMRI techniques combined with a stronger emphasis on shared hippocampal mechanisms across short- and long-term memory pave an exciting path forward.

Objective.Methods for the detection of neural signals involve a compromise between invasiveness, spatiotemporal resolution, and the number of neurons or brain regions recorded. Electrode-based probes provide excellent response but usually require transcranial wiring and capture activity from limited neuronal populations. Noninvasive methods such as electroencephalography and magnetoencephalography offer fast readouts of field potentials or biomagnetic signals, respectively, but have spatial constraints that prohibit recording from single neurons. A cell-sized device that enhances neurogenic magnetic fields can be used as anin situsensor for magnetic-based modalities and increase the ability to detect diverse signals across multiple brain regions.Approach.We designed and modeled a device capable of forming a tight electromagnetic junction with single neurons, thereby transducing changes in cellular potential to magnetic field perturbations by driving current through a nanofabricated inductor element.Main results.We present detailed quantification of the device performance using realistic finite element simulations with signals and geometries acquired from patch-clamped neuronsin vitroand demonstrate the capability of the device to produce magnetic signals readable via existing modalities. We compare the magnetic output of the device to intrinsic neuronal magnetic fields (NMFs) and show that the transduced magnetic field intensity from a single neuron is more than three-fold higher at its peak (1.62 nT vs 0.51 nT). Importantly, we report on a large spatial enhancement of the transduced magnetic field output within a typical voxel (40 × 40 × 10µm) over 250 times higher than the intrinsic NMF strength (0.64 nT vs 2.5 pT). We use this framework to perform optimizations of device performance based on nanofabrication constraints and material choices.Significance.Our quantifications institute a foundation for synthesizing and applying electromagnetic sensors for detecting brain activity and can serve as a general method for quantifying recording devices at the single cell level.

The left and right primate hippocampi (LH and RH) are thought to support distinct functions, but little is known about differences between the hemispheres at the neuronal level. We recorded single-neuron and local field potentials from the human hippocampus in epilepsy patients implanted with depth electrodes. We detected theta-frequency bouts of oscillatory activity while patients performed a visual recognition memory task. Theta appeared in bouts of 3.16 cycles, with sawtooth-shaped oscillations that had a prolonged downswing period. Outside the seizure onset zone, the average frequency of theta bouts was higher in the RH compared to the LH (6.0 vs. 5.3 Hz). LH theta bouts had lower amplitudes and a higher prevalence compared to the RH (26% vs. 21% of total time). Additionally, the RH contained a population of thin spiking visually tuned neurons that were not present in the LH. These data show that human theta appears in short oscillatory bouts whose properties vary between hemispheres, thereby revealing neurophysiological properties of the hippocampus that differ between the hemispheres.

Cantor coding provides an information coding scheme for temporal sequences of events. In the hippocampal CA3-CA1 network, Cantor coding-like mechanism was observed in pyramidal neurons and the relationship between input pattern and recorded responses could be described as an iterated function system. However, detailed physiological properties of the system in CA1 remain unclear. Here, we performed a detailed analysis of the properties of the system related to the physiological basis of learning and memory. First, we investigated whether the system could be simply based on a series of on-off responses of excitatory postsynaptic potential (EPSP) amplitudes. We applied a series of three spatially distinct input patterns with similar EPSP peak amplitudes. The membrane responses showed significant differences in spatial clustering properties related to the iterated function system. These results suggest that existence of some factors, which do not simply depend on a series of on-off responses but on spatial patterns in the system. Second, to confirm whether the system is dependent on the interval of sequential input, we applied spatiotemporal sequential inputs at several intervals. The optimal interval was 30 ms, similar to the physiological input from CA3 to CA1. Third, we analyzed the inhibitory network dependency of the system. After GABAA receptor blocker (gabazine) application, quality of code discrimination in the system was lower under subthreshold conditions and higher under suprathreshold conditions. These results suggest that the inhibitory network increase the difference between the responses under sub- and suprathreshold conditions. In summary, Cantor coding-like iterated function system appears to be suitable for information expression in relation to learning and memory in CA1 network.