Rates and rhythms: a synergistic view of frequency and temporal coding in neuronal networks

Optimization of Temporal Coding of Tactile Information in Rat Thalamus by Locus Coeruleus Activation

The brainstem noradrenergic nucleus, the locus coeruleus (LC), exerts heavy influences on sensory processing, perception, and cognition through its diffuse projections throughout the brain. Previous studies have demonstrated that LC activation modulates the response and feature selectivity of thalamic relay neurons. However, the extent to which LC modulates the temporal coding of sensory information in the thalamus remains mostly unknown. Here, we found that LC stimulation significantly altered the temporal structure of the responses of the thalamic relay neurons to repeated whisker stimulation. A substantial portion of events (i.e., time points where the stimulus reliably evoked spikes as evidenced by dramatic elevations in the firing rate of the spike density function) were removed during LC stimulation, but many new events emerged. Interestingly, spikes within the emerged events have a higher feature selectivity, and therefore transmit more information about a tactile stimulus, than spikes within the removed events. This suggests that LC stimulation optimized the temporal coding of tactile information to improve information transmission. We further reconstructed the original whisker stimulus from a population of thalamic relay neurons' responses and corresponding feature selectivity. As expected, we found that reconstruction from thalamic responses was more accurate using spike trains of thalamic neurons recorded during LC stimulation than without LC stimulation, functionally confirming LC optimization of the thalamic temporal code. Together, our results demonstrated that activation of the LC-NE system optimizes temporal coding of sensory stimulus in the thalamus, presumably allowing for more accurate decoding of the stimulus in the downstream brain structures.

Modulation of SK Channels via Calcium Buffering Tunes Intrinsic Excitability of Parvalbumin Interneurons in Neuropathic Pain: A Computational and Experimental Investigation

Parvalbumin-expressing interneurons (PVINs) play a crucial role within the dorsal horn of the spinal cord by preventing touch inputs from activating pain circuits. In both male and female mice, nerve injury decreases PVINs' output via mechanisms that are not fully understood. In this study, we show that PVINs from nerve-injured male mice change their firing pattern from tonic to adaptive. To examine the ionic mechanisms responsible for this decreased output, we used a reparametrized Hodgkin-Huxley type model of PVINs, which predicted (1) the firing pattern transition is because of an increased contribution of small conductance calcium-activated potassium (SK) channels, enabled by (2) impairment in intracellular calcium buffering systems. Analyzing the dynamics of the Hodgkin-Huxley type model further demonstrated that a generalized Hopf bifurcation differentiates the two types of state transitions observed in the transient firing of PVINs. Importantly, this predicted mechanism holds true when we embed the PVIN model within the neuronal circuit model of the spinal dorsal horn. To experimentally validate this hypothesized mechanism, we used pharmacological modulators of SK channels and demonstrated that (1) tonic firing PVINs from naive male mice become adaptive when exposed to an SK channel activator, and (2) adapting PVINs from nerve-injured male mice return to tonic firing on SK channel blockade. Our work provides important insights into the cellular mechanism underlying the decreased output of PVINs in the spinal dorsal horn after nerve injury and highlights potential pharmacological targets for new and effective treatment approaches to neuropathic pain.SIGNIFICANCE STATEMENT Parvalbumin-expressing interneurons (PVINs) exert crucial inhibitory control over Aβ fiber-mediated nociceptive pathways at the spinal dorsal horn. The loss of their inhibitory tone leads to neuropathic symptoms, such as mechanical allodynia, via mechanisms that are not fully understood. This study identifies the reduced intrinsic excitability of PVINs as a potential cause for their decreased inhibitory output in nerve-injured condition. Combining computational and experimental approaches, we predict a calcium-dependent mechanism that modulates PVINs' electrical activity following nerve injury: a depletion of cytosolic calcium buffer allows for the rapid accumulation of intracellular calcium through the active membranes, which in turn potentiates SK channels and impedes spike generation. Our results therefore pinpoint SK channels as potential therapeutic targets for treating neuropathic symptoms.

Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics

Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.

Pallidal GABA B receptors: involvement in cortex beta dynamics and thalamic reticular nucleus activity

The external globus pallidus (GP) firing rate synchronizes the basal ganglia-thalamus-cortex network controlling GABAergic output to different nuclei. In this context, two findings are significant: the activity and GABAergic transmission of the GP modulated by GABA B receptors and the presence of the GP-thalamic reticular nucleus (RTn) pathway, the functionality of which is unknown. The functional participation of GABA B receptors through this network in cortical dynamics is feasible because the RTn controls transmission between the thalamus and cortex. To analyze this hypothesis, we used single-unit recordings of RTn neurons and electroencephalograms of the motor cortex (MCx) before and after GP injection of the GABA B agonist baclofen and the antagonist saclofen in anesthetized rats. We found that GABA B agonists increase the spiking rate of the RTn and that this response decreases the spectral density of beta frequency bands in the MCx. Additionally, injections of GABA B antagonists decreased the firing activity of the RTn and reversed the effects in the power spectra of beta frequency bands in the MCx. Our results proved that the GP modulates cortical oscillation dynamics through the GP-RTn network via tonic modulation of RTn activity.

Coexisting neuronal coding strategies in the barrel cortex

During tactile sensation by rodents, whisker movements across surfaces generate complex whisker motions, including discrete, transient stick-slip events, which carry information about surface properties. The characteristics of these events and how the brain encodes this tactile information remain enigmatic. We found that cortical neurons show a mixture of synchronized and nontemporally correlated spikes in their tactile responses. Synchronous spikes convey the magnitude of stick-slip events by numerous aspects of temporal coding. These spikes show preferential selectivity for kinetic and kinematic whisker motion. By contrast, asynchronous spikes in each neuron convey the magnitude of stick-slip events by their discharge rates, response probability, and interspike intervals. We further show that the differentiation between these two types of activity is highly dependent on the magnitude of stick-slip events and stimulus and response history. These results suggest that cortical neurons transmit multiple components of tactile information through numerous coding strategies.

Mixed synapses reconcile violations of the size principle in zebrafish spinal cord

Mixed electrical-chemical synapses potentially complicate electrophysiological interpretations of neuronal excitability and connectivity. Here, we disentangle the impact of mixed synapses within the spinal locomotor circuitry of larval zebrafish. We demonstrate that soma size is not linked to input resistance for interneurons, contrary to the biophysical predictions of the ‘size principle’ for motor neurons. Next, we show that time constants are faster, excitatory currents stronger, and mixed potentials larger in lower resistance neurons, linking mixed synapse density to resting excitability. Using a computational model, we verify the impact of weighted electrical synapses on membrane properties, synaptic integration and the low-pass filtering and distribution of coupling potentials. We conclude differences in mixed synapse density can contribute to excitability underestimations and connectivity overestimations. The contribution of mixed synaptic inputs to resting excitability helps explain ‘violations’ of the size principle, where neuron size, resistance and recruitment order are unrelated.

Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement

The development of technologies for brain stimulation provides a means for scientists and clinicians to directly actuate the brain and nervous system. Brain stimulation has shown intriguing potential in terms of modifying particular symptom clusters in patients and behavioral characteristics of subjects. The stage is thus set for optimization of these techniques and the pursuit of more nuanced stimulation objectives, including the modification of complex cognitive functions such as memory and attention. Control theory and engineering will play a key role in the development of these methods, guiding computational and algorithmic strategies for stimulation. In particular, realizing this goal will require new development of frameworks that allow for controlling not only brain activity, but also latent dynamics that underlie neural computation and information processing. In the current opinion, we review recent progress in brain stimulation and outline challenges and potential research pathways associated with exogenous control of cognitive function.

Spike-Gamma Phase Relationship in the Visual Cortex

Gamma oscillations (30-70 Hz) have been hypothesized to play a role in cortical function. Most of the proposed mechanisms involve rhythmic modulation of neuronal excitability at gamma frequencies, leading to modulation of spike timing relative to the rhythm. I first show that the gamma band could be more privileged than other frequencies in observing spike-field interactions even in the absence of genuine gamma rhythmicity and discuss several biases in spike-gamma phase estimation. I then discuss the expected spike-gamma phase according to several hypotheses. Inconsistent with the phase-coding hypothesis (but not with others), the spike-gamma phase does not change with changes in stimulus intensity or attentional state, with spikes preferentially occurring 2-4 ms before the trough, but with substantial variability. However, this phase relationship is expected even when gamma is a byproduct of excitatory-inhibitory interactions. Given that gamma occurs in short bursts, I argue that the debate over the role of gamma is a matter of semantics. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Polarization-perceptual anisotropic two-dimensional ReS2 neuro-transistor with reconfigurable neuromorphic vision

Polarization is a common and unique phenomenon in nature, which reveals more camouflage features of objects. However, current polarization-perceptual devices based on conventional physical architectures face enormous challenges for high-performance computation due to the traditional von Neumann bottleneck. In this work, a novel polarization-perceptual neuro-transistor with reconfigurable anisotropic vision is proposed based on a two-dimensional ReS₂ phototransistor. The device exhibits excellent photodetection ability and superior polarization sensitivity due to its direct band gap semiconductor property and strong anisotropic crystal structure, respectively. The fascinating polarization-sensitive neuromorphic behavior, such as polarization memory consolidation and reconfigurable visual imaging, are successfully realized. In particular, the regulated polarization responsivity and dichroic ratio are successfully emulated through our artificial compound eyes. More importantly, two intriguing polarization-perceptual applications for polarized navigation with reconfigurable adaptive learning abilities and three-dimensional visual polarization imaging are also experimentally demonstrated. The proposed device may provide a promising opportunity for future polarization perception systems in intelligent humanoid robots and autonomous vehicles.

Consciousness Beyond Neural Fields: Expanding the Possibilities of What Has Not Yet Happened

In the field theories in physics, any particular region of the presumed space-time continuum and all interactions between elementary objects therein can be objectively measured and/or accounted for mathematically. Since this does not apply to any of the field theories, or any other neural theory, of consciousness, their explanatory power is limited. As discussed in detail herein, the matter is complicated further by the facts than any scientifically operational definition of consciousness is inevitably partial, and that the phenomenon has no spatial dimensionality. Under the light of insights from research on meditation and expanded consciousness, chronic pain syndrome, healthy aging, and eudaimonic well-being, we may conceive consciousness as a source of potential energy that has no clearly defined spatial dimensionality, but can produce significant changes in others and in the world, observable in terms of changes in time. It is argued that consciousness may have evolved to enable the human species to generate such changes in order to cope with unprecedented and/or unpredictable adversity. Such coping could, ultimately, include the conscious planning of our own extinction when survival on the planet is no longer an acceptable option.

Low-cost and easy-fabrication lightweight drivable electrode array for multiple-regions electrophysiological recording in free-moving mice

OBJECTIVE

Extracellular electrophysiology has been widely applied to neural circuit dissections. However, long-term multiregional recording in free-moving mice remains a challenge. Low-cost and easy-fabrication of elaborate drivable electrodes is required for their prevalence.

APPROACH

A three-layer nested construct (OD ~1.80 mm, length ~10 mm, <0.1g) was recruited as a drivable component, which consisted of an ethylene-vinyl acetate copolymer (EVA) heat-shrinkable tube, non-closed loop ceramic bushing, and stainless ferrule with a bulge twining silver wire. The supporting and working components were equipped with drivable components to be assembled into a drivable microwire electrode array with a nested structure (drivable MEANS). Two drivable microwire electrode arrays were independently implanted for chronic recording in different brain areas at respective angles. An optic fiber was easily loaded into the drivable MEANS to achieve optogenetic modulation and electrophysiological recording simultaneously.

MAIN RESULTS

The drivable MEANS had lightweight (~ 0.37 g), small (~ 15 mm ×15 mm × 4 mm), and low cost (≤ $64.62). Two drivable MEANS were simultaneously implanted in mice, and high-quality electrophysiological recordings could be applied ≥ 5 months after implantation in freely behaving animals. Electrophysiological recordings and analysis of the lateral septum (LS) and lateral hypothalamus (LH) in food-seeking behavior demonstrated that our drivable MEANS can be used to dissect the function of neural circuits. An optical fiber-integrated drivable MEANS (~ 0.47 g) was used to stimulate and record LS neurons, which suggested that changes in working components can achieve more functions than electrophysiological recordings, such as optical stimulation, drug release, and calcium imaging.

SIGNIFICANCE

Drivable MEANS is an easily fabricated, lightweight drivable microwire electrode array for multiple-region electrophysiological recording in free-moving mice. Our design is likely to be a valuable platform for both current and prospective users, as well as for developers of multifunctional electrodes for free-moving mice.

Synaptic Dynamics Convey Differential Sensitivity to Input Pattern Changes in Two Muscles Innervated by the Same Motor Neurons

Postsynaptic responses depend on input patterns as well as short-term synaptic plasticity, summation, and postsynaptic membrane properties, but the interactions of those dynamics with realistic input patterns are not well understood. We recorded the responses of the two pyloric dilator (PD) muscles, cpv2a and cpv2b, that are innervated by and receive identical periodic bursting input from the same two motor neurons in the lobster Homarus americanus. Cpv2a and cpv2b showed quantitative differences in membrane nonlinearities and synaptic summation. At a short timescale, responses in both muscles were dominated by facilitation, albeit with different frequency and time dependence. Realistic burst stimulations revealed more substantial differences. Across bursts, cpv2a showed transient depression, whereas cpv2b showed transient facilitation. Steady-state responses to bursting input also differed substantially. Neither muscle had a monotonic dependence on frequency, but cpv2b showed particularly pronounced bandpass filtering. Cpv2a was sensitive to changes in both burst frequency and intra-burst spike frequency, whereas, despite its much slower responses, cpv2b was largely insensitive to changes in burst frequency. Cpv2a was sensitive to both burst duration and number of spikes per burst, whereas cpv2b was sensitive only to the former parameter. Neither muscle showed consistent sensitivity to changes in the overall spike interval structure, but cpv2b was surprisingly sensitive to changes in the first intervals in each burst, a parameter known to be regulated by dopamine (DA) modulation of spike propagation of the presynaptic axon. These findings highlight how seemingly minor circuit output changes mediated by neuromodulation could be read out differentially at the two synapses.

A Model of the Early Visual System Based on Parallel Spike-Sequence Detection, Showing Orientation Selectivity

Simple Summary

A computational model of primates’ early visual processing, showing orientation selectivity, is presented. The system importantly integrates two key elements: (1) a neuromorphic spike-decoding structure that considerably resembles the circuitry between layers IV and II/III of the primary visual cortex, both in topology and operation; (2) the plasticity of intrinsic excitability, to embed recent findings about the operation of the same area. The model is proposed as a tool for the analysis and reproduction of the orientation selectivity phenomenon, whose underlying neuronal-level computational mechanisms are today the subject of intense scrutiny. In response to rotated Gabor patches the model is able to exhibit realistic orientation tuning curves and to reproduce responses similar to those found in neurophysiological recordings from the primary visual cortex obtained under the same task, considering different stages of the network. This demonstrates its aptness to capture the mechanisms underlying the evoked response in the primary visual cortex. Our tool is available online, and can be expanded to other experiments using a dedicated software library developed by the authors, to elucidate the computational mechanisms underlying orientation selectivity.

Abstract

Since the first half of the twentieth century, numerous studies have been conducted on how the visual cortex encodes basic image features. One of the hallmarks of basic feature extraction is the phenomenon of orientation selectivity, of which the underlying neuronal-level computational mechanisms remain partially unclear despite being intensively investigated. In this work we present a reduced visual system model (RVSM) of the first level of scene analysis, involving the retina, the lateral geniculate nucleus and the primary visual cortex (V1), showing orientation selectivity. The detection core of the RVSM is the neuromorphic spike-decoding structure MNSD, which is able to learn and recognize parallel spike sequences and considerably resembles the neuronal microcircuits of V1 in both topology and operation. This structure is equipped with plasticity of intrinsic excitability to embed recent findings about V1 operation. The RVSM, which embeds 81 groups of MNSD arranged in 4 oriented columns, is tested using sets of rotated Gabor patches as input. Finally, synthetic visual evoked activity generated by the RVSM is compared with real neurophysiological signal from V1 area: (1) postsynaptic activity of human subjects obtained by magnetoencephalography and (2) spiking activity of macaques obtained by multi-tetrode arrays. The system is implemented using the NEST simulator. The results attest to a good level of resemblance between the model response and real neurophysiological recordings. As the RVSM is available online, and the model parameters can be customized by the user, we propose it as a tool to elucidate the computational mechanisms underlying orientation selectivity.

Cell assembly formation and structure in a piriform cortex model

The piriform cortex is rich in recurrent excitatory synaptic connections between pyramidal neurons. We asked how such connections could shape cortical responses to olfactory lateral olfactory tract (LOT) inputs. For this, we constructed a computational network model of anterior piriform cortex with 2000 multicompartment, multiconductance neurons (500 semilunar, 1000 layer 2 and 500 layer 3 pyramids; 200 superficial interneurons of two types; 500 deep interneurons of three types; 500 LOT afferents), incorporating published and unpublished data. With a given distribution of LOT firing patterns, and increasing the strength of recurrent excitation, a small number of firing patterns were observed in pyramidal cell networks: first, sparse firings; then temporally and spatially concentrated epochs of action potentials, wherein each neuron fires one or two spikes; then more synchronized events, associated with bursts of action potentials in some pyramidal neurons. We suggest that one function of anterior piriform cortex is to transform ongoing streams of input spikes into temporally focused spike patterns, called here "cell assemblies", that are salient for downstream projection areas.

Variability and Randomness of the Instantaneous Firing Rate

The apparent stochastic nature of neuronal activity significantly affects the reliability of neuronal coding. To quantify the encountered fluctuations, both in neural data and simulations, the notions of variability and randomness of inter-spike intervals have been proposed and studied. In this article we focus on the concept of the instantaneous firing rate, which is also based on the spike timing. We use several classical statistical models of neuronal activity and we study the corresponding probability distributions of the instantaneous firing rate. To characterize the firing rate variability and randomness under different spiking regimes, we use different indices of statistical dispersion. We find that the relationship between the variability of interspike intervals and the instantaneous firing rate is not straightforward in general. Counter-intuitively, an increase in the randomness (based on entropy) of spike times may either decrease or increase the randomness of instantaneous firing rate, in dependence on the neuronal firing model. Finally, we apply our methods to experimental data, establishing that instantaneous rate analysis can indeed provide additional information about the spiking activity.

Information diversity in individual auditory cortical neurons is associated with functionally distinct coordinated neuronal ensembles

Neuronal activity in auditory cortex is often highly synchronous between neighboring neurons. Such coordinated activity is thought to be crucial for information processing. We determined the functional properties of coordinated neuronal ensembles (cNEs) within primary auditory cortical (AI) columns relative to the contributing neurons. Nearly half of AI cNEs showed robust spectro-temporal receptive fields whereas the remaining cNEs showed little or no acoustic feature selectivity. cNEs can therefore capture either specific, time-locked information of spectro-temporal stimulus features or reflect stimulus-unspecific, less-time specific processing aspects. By contrast, we show that individual neurons can represent both of those aspects through membership in multiple cNEs with either high or absent feature selectivity. These associations produce functionally heterogeneous spikes identifiable by instantaneous association with different cNEs. This demonstrates that single neuron spike trains can sequentially convey multiple aspects that contribute to cortical processing, including stimulus-specific and unspecific information.

Periaxonal and nodal plasticities modulate action potential conduction in the adult mouse brain

Central nervous system myelination increases action potential conduction velocity. However, it is unclear how myelination is coordinated to ensure the temporally precise arrival of action potentials and facilitate information processing within cortical and associative circuits. Here, we show that myelin sheaths, supported by mature oligodendrocytes, remain plastic in the adult mouse brain and undergo subtle structural modifications to influence action potential conduction velocity. Repetitive transcranial magnetic stimulation and spatial learning, two stimuli that modify neuronal activity, alter the length of the nodes of Ranvier and the size of the periaxonal space within active brain regions. This change in the axon-glial configuration is independent of oligodendrogenesis and robustly alters action potential conduction velocity. Because aptitude in the spatial learning task was found to correlate with action potential conduction velocity in the fimbria-fornix pathway, modifying the axon-glial configuration may be a mechanism that facilitates learning in the adult mouse brain.

Visual Stimulus Content in V4 Is Conveyed by Gamma-Rhythmic Information Packages

Selective visual attention allows the brain to focus on behaviorally relevant information while ignoring irrelevant signals. As a possible mechanism, routing-by-synchronization was proposed: neural populations receiving attended signals align their gamma-rhythmic activity to that of the sending populations, such that incoming spikes arrive at excitability peaks of receiving populations, enhancing signal transfer. Conversely, non-attended signals arrive unaligned to the receiver's oscillation, reducing signal transfer. Therefore, visual signals should be transferred through gamma-rhythmic bursts of information, resulting in a modulation of the stimulus content within the receiving population's activity by its gamma phase and amplitude. To test this prediction, we quantified gamma-phase-dependent stimulus content within neural activity from area V4 of two male macaques performing a visual attention task. For the attended stimulus, we find highest stimulus information content near excitability peaks, an effect that increases with oscillation amplitude, establishing a functional link between selective processing and gamma-activity.SIGNIFICANCE STATEMENT The ability to focus on the behaviorally relevant signals is essential for the brain to cope with the continuous, high-dimensional stream of sensory information it receives. What are the neural mechanisms which allow such selective processing in the visual system? We analyzed data from area V4 and found that the amount of visual signal information content is tightly linked to the phase of local gamma-rhythmic activity, with maximal signal content occurring near peaks of neural excitability. Our investigations provide direct evidence that selective attention relies on rhythmic temporal coordination between visual areas, and establish novel methods for pinpointing pulsed transmission schemes in neural data.

A dynamical model for the basal ganglia-thalamo-cortical oscillatory activity and its implications in Parkinson's disease

We propose to investigate brain electrophysiological alterations associated with Parkinson’s disease through a novel adaptive dynamical model of the network of the basal ganglia, the cortex and the thalamus. The model uniquely unifies the influence of dopamine in the regulation of the activity of all basal ganglia nuclei, the self-organised neuronal interdependent activity of basal ganglia-thalamo-cortical circuits and the generation of subcortical background oscillations. Variations in the amount of dopamine produced in the neurons of the substantia nigra pars compacta are key both in the onset of Parkinson’s disease and in the basal ganglia action selection. We model these dopamine-induced relationships, and Parkinsonian states are interpreted as spontaneous emergent behaviours associated with different rhythms of oscillatory activity patterns of the basal ganglia-thalamo-cortical network. These results are significant because: (1) the neural populations are built upon single-neuron models that have been robustly designed to have eletrophysiologically-realistic responses, and (2) our model distinctively links changes in the oscillatory activity in subcortical structures, dopamine levels in the basal ganglia and pathological synchronisation neuronal patterns compatible with Parkinsonian states, this still remains an open problem and is crucial to better understand the progression of the disease.

Absorption and Emission Spectroscopic Investigation of the Thermal Dynamics of the Archaerhodopsin 3 Based Fluorescent Voltage Sensor Archon2

Archon2 is a fluorescent voltage sensor derived from Archaerhodopsin 3 (Arch) of Halorubrum sodomense using robotic multidimensional directed evolution approach. Here we report absorption and emission spectroscopic studies of Archon2 in Tris buffer at pH 8. Absorption cross-section spectra, fluorescence quantum distributions, fluorescence quantum yields, and fluorescence excitation spectra were determined. The thermal stability of Archon2 was studied by long-time attenuation coefficient measurements at room temperature (21 ± 1 °C) and at refrigerator temperature (3 ± 1 °C). The apparent melting temperature was determined by stepwise sample heating up and cooling down (obtained apparent melting temperature: 63 ± 3 °C). In the protein melting process protonated retinal Schiff base (PRSB) with absorption maximum at 586 nm converted to de-protonated retinal Schiff base (RSB) with absorption maximum at 380 nm. Storage of Archon2 at room temperature and refrigerator temperature caused absorption coefficient decrease because of partial protein clustering to aggregates at condensation nuclei and sedimentation. At room temperature an onset of light scattering was observed after two days because of the beginning of protein unfolding. During the period of observation (18 days at 21 °C, 22 days at 3 °C) no change of retinal isomer composition was observed indicating a high potential energy barrier of S₀ ground-state isomerization.

Neural Coding of Thermal Preferences in the Nematode Caenorhabditis elegans

Animals are capable to modify sensory preferences according to past experiences. Surrounded by ever-changing environments, they continue assigning a hedonic value to a sensory stimulus. It remains to be elucidated however how such alteration of sensory preference is encoded in the nervous system. Here we show that past experiences alter temporal interaction between the calcium responses of sensory neurons and their postsynaptic interneurons in the nematode Caenorhabditis elegans. C. elegans exhibits thermotaxis, in which its temperature preference is modified by the past feeding experience: well-fed animals are attracted toward their past cultivation temperature on a thermal gradient, whereas starved animals lose that attraction. By monitoring calcium responses simultaneously from both AFD thermosensory neurons and their postsynaptic AIY interneurons in well-fed and starved animals under time-varying thermal stimuli, we found that past feeding experiences alter phase shift between AFD and AIY calcium responses. Furthermore, the difference in neuronal activities between well-fed and starved animals observed here are able to explain the difference in the behavioral output on a thermal gradient between well-fed and starved animals. Although previous studies have shown that C. elegans executes thermotaxis by regulating amplitude or frequency of the AIY response, our results proposed a new mechanism by which thermal preference is encoded by phase shift between AFD and AIY activities. Given these observations, thermal preference is likely to be computed on synapses between AFD and AIY neurons. Such a neural strategy may enable animals to enrich information processing within defined connectivity via dynamic alterations of synaptic communication.

Layer 4 pyramidal neuron dendritic bursting underlies a post-stimulus visual cortical alpha rhythm

Alpha rhythms (9–11 Hz) are a dominant feature of EEG recordings, particularly over occipital cortex on cessation of a visual stimulation. Little is known about underlying neocortical mechanisms so here we constructed alpha rhythm models that follow cessation of cortical stimulation. The rhythm manifests following a period of gamma frequency activity in local V1 networks in layer 4. It associates with network level bias of excitatory synaptic activity in favour of NMDA- rather than AMPA-mediated signalling and reorganisation of synaptic inhibition in favour of fast GABA_A receptor-mediated events. At the cellular level the alpha rhythm depended upon the generation of layer 4 pyramidal neuron dendritic bursting mediated primarily by PPDA-sensitive NR2C/D-containing NMDA receptors, which lack the magnesium-dependent open channel block. Subthreshold potassium conductances are also critical. The rhythm dynamically filters outputs from sensory relay neurons (stellate neurons in layer 4) such that they become temporally uncoupled from downstream population activity.

The authors combine computational and electrophysiological approaches to study the neocortical mechanisms underlying alpha rhythms generated after visual stimuli cessation. They show that layer 4 pyramidal neuron bursting, as well as a shift towards NMDA- and GABA_A- receptor transmission is critical for the generation of these alpha oscillations.

The globus pallidus orchestrates abnormal network dynamics in a model of Parkinsonism

The dynamical properties of cortico-basal ganglia (CBG) circuits are dramatically altered following the loss of dopamine in Parkinson’s disease (PD). The neural circuit dysfunctions associated with PD include spike-rate alteration concomitant with excessive oscillatory spike-synchronization in the beta frequency range (12–30 Hz). Which neuronal circuits orchestrate and propagate these abnormal neural dynamics in CBG remains unknown. In this work, we combine in vivo electrophysiological recordings with advanced optogenetic manipulations in normal and 6-OHDA rats to shed light on the mechanistic principle underlying circuit dysfunction in PD. Our results show that abnormal neural dynamics present in a rat model of PD do not rely on cortical or subthalamic nucleus activity but critically dependent on globus pallidus (GP) integrity. Our findings highlight the pivotal role played by the GP which operates as a hub nucleus capable of orchestrating firing rate and synchronization changes across CBG circuits both in normal and pathological conditions.

Oscillatory changes between basal ganglia nuclei occur in Parkinson’s disease. Here the authors determine that the globus pallidus is the source of beta oscillation generation in a rodent model of the disease.

All-Optical Detection of Neuronal Membrane Depolarization in Live Cells Using Colloidal Quantum Dots

Luminescent semiconductor quantum dots (QDs) have recently been suggested as novel probes for imaging and sensing cell membrane voltages. However, a key bottleneck for their development is a lack of techniques to assess QD responses to voltages generated in the aqueous electrolytic environments typical of biological systems. Even more generally, there have been relatively few efforts to assess the response of QDs to voltage changes in live cells. Here, we develop a platform for monitoring the photoluminescence (PL) response of QDs under AC and DC voltage changes within aqueous ionic environments. We evaluate both traditional CdSe/CdS and more biologically compatible InP/ZnS QDs at a range of ion concentrations to establish their PL/voltage characteristics on chip. Wide-field, few-particle PL measurements with neuronal cells show the QDs can be used to track local voltage changes with greater sensitivity (ΔPL up to twice as large) than state-of-the-art calcium imaging dyes, making them particularly appealing for tracking subthreshold events. Additional physiological observation studies showed that while CdSe/CdS dots have greater PL responses on membrane depolarization, their lower cytotoxicity makes InP/ZnS far more suitable for voltage sensing in living systems. Our results provide a methodology for the rational development of QD voltage sensors and highlight their potential for imaging changes in cell membrane voltage.

The neural circuitry supporting successful spatial navigation despite variable movement speeds

Ants who have successfully navigated the long distance between their foraging spot and their nest dozens of times will drastically overshoot their destination if the size of their legs is doubled by the addition of stilts. This observation reflects a navigational strategy called path integration, a strategy also utilized by mammals. Path integration necessitates that animals keep track of their movement speed and use it to precisely and instantly modify where they think they are and where they want to go. Here we review the neural circuitry that has evolved to integrate speed and space. We start with the rate and temporal codes for speed in the hippocampus and work backwards towards the motor and sensory systems. We highlight the need for experiments designed to differentiate the respective contributions of motor efference copy versus sensory inputs. In particular, we discuss the importance of high-resolution tracking of the latency of speed-encoding as a precise way to disentangle the sensory versus motor computations that enable successful spatial navigation at very different speeds.

Acetylcholine Mediates Dynamic Switching Between Information Coding Schemes in Neuronal Networks

Rate coding and phase coding are the two major coding modes seen in the brain. For these two modes, network dynamics must either have a wide distribution of frequencies for rate coding, or a narrow one to achieve stability in phase dynamics for phase coding. Acetylcholine (ACh) is a potent regulator of neural excitability. Acting through the muscarinic receptor, ACh reduces the magnitude of the potassium M-current, a hyperpolarizing current that builds up as neurons fire. The M-current contributes to several excitability features of neurons, becoming a major player in facilitating the transition between Type 1 (integrator) and Type 2 (resonator) excitability. In this paper we argue that this transition enables a dynamic switch between rate coding and phase coding as levels of ACh release change. When a network is in a high ACh state variations in synaptic inputs will lead to a wider distribution of firing rates across the network and this distribution will reflect the network structure or pattern of external input to the network. When ACh is low, network frequencies become narrowly distributed and the structure of a network or pattern of external inputs will be represented through phase relationships between firing neurons. This work provides insights into how modulation of neuronal features influences network dynamics and information processing across brain states.

An image-computable model for the stimulus selectivity of gamma oscillations

Gamma oscillations in visual cortex have been hypothesized to be critical for perception, cognition, and information transfer. However, observations of these oscillations in visual cortex vary widely; some studies report little to no stimulus-induced narrowband gamma oscillations, others report oscillations for only some stimuli, and yet others report large oscillations for most stimuli. To better understand this signal, we developed a model that predicts gamma responses for arbitrary images and validated this model on electrocorticography (ECoG) data from human visual cortex. The model computes variance across the outputs of spatially pooled orientation channels, and accurately predicts gamma amplitude across 86 images. Gamma responses were large for a small subset of stimuli, differing dramatically from fMRI and ECoG broadband (non-oscillatory) responses. We propose that gamma oscillations in visual cortex serve as a biomarker of gain control rather than being a fundamental mechanism for communicating visual information.

Absorption and Emission Spectroscopic Investigation of the Thermal Dynamics of the Archaerhodopsin 3 Based Fluorescent Voltage Sensor QuasAr1

QuasAr1 is a fluorescent voltage sensor derived from Archaerhodopsin 3 (Arch) of Halorubrum sodomense by directed evolution. Here we report absorption and emission spectroscopic studies of QuasAr1 in Tris buffer at pH 8. Absorption cross-section spectra, fluorescence quantum distributions, fluorescence quantum yields, and fluorescence excitation spectra were determined. The thermal stability of QuasAr1 was studied by long-time attenuation coefficient measurements at room temperature (23 ± 2 °C) and at 2.5 ± 0.5 °C. The apparent melting temperature was determined by stepwise sample heating up and cooling down (obtained apparent melting temperature: 65 ± 3 °C). In the protein melting process the originally present protonated retinal Schiff base (PRSB) with absorption maximum at 580 nm converted to de-protonated retinal Schiff base (RSB) with absorption maximum at 380 nm. Long-time storage of QuasAr1 at temperatures around 2.5 °C and around 23 °C caused gradual protonated retinal Schiff base isomer changes to other isomer conformations, de-protonation to retinal Schiff base isomers, and apoprotein structure changes showing up in ultraviolet absorption increase. Reaction coordinate schemes are presented for the thermal protonated retinal Schiff base isomerizations and deprotonations in parallel with the dynamic apoprotein restructurings.

The function of connectomes in encoding sensory stimuli

The enormous number of neurons and the massive sum of connecting fibers linking them make the neural processes of encoding sensory signals extraordinarily complex, and this challenge is far from being elucidated. Simply stated, for the present paper, the question is - how does the brain encode complex images? Our proposal argues that modulation of strengths of functional relationships between firing neurons in relation to an input results in the formation of stimulus-salient functional connectomes. This type of connection/coupling strength is computed by performing cross correlograms (CCG) of spike trains between simultaneously firing cells. Significantly, the strength is dependent upon stimuli characteristics, inferring that cells may join or leave particular ensembles, thus creating signature emergent connectomes for different images, thereby, allowing their discrimination. We observed in an ensemble that functionally connected cells exhibited synergistic excitatory activity, increased coherence, and augmented gamma oscillations within a window-of-opportunity contrasting with unconnected neighboring neuronal companions. We suggest that investigating and revealing such stimulus-salient emergent connectomes is a realistic and promising pursuit toward answering how the brain processes complex images.

Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity

The rate and temporal pattern of neural spiking each have the potential to influence computation. In the cerebellum, it has been hypothesized that the irregularity of interspike intervals in Purkinje cells affects their ability to transmit information to downstream neurons. Accordingly, during oculomotor behavior in mice and rhesus monkeys, mean irregularity of Purkinje cell spiking varied with mean eye velocity. However, moment-to-moment variations revealed a tight correlation between eye velocity and spike rate, with no additional information conveyed by spike irregularity. Moreover, when spike rate and irregularity were independently controlled using optogenetic stimulation, the eye movements elicited were well-described by a linear population rate code with 3-5 ms temporal precision. Biophysical and random-walk models identified biologically realistic parameter ranges that determine whether spike irregularity influences responses downstream. The results demonstrate cerebellar control of movements through a remarkably rapid rate code, with no evidence for an additional contribution of spike irregularity.

A future for neuronal oscillation research

Neuronal oscillations represent the most obvious feature of electrical activity in the brain. They are linked in general with global brain state (awake, asleep, etc.) and specifically with organisation of neuronal outputs during sensory perception and cognitive processing. Oscillations can be generated by individual neurons on the basis of interaction between inputs and intrinsic conductances but are far more commonly seen at the local network level in populations of interconnected neurons with diverse arrays of functional properties. It is at this level that the brain’s rich and diverse library of oscillatory time constants serve to temporally organise large-scale neural activity patterns. The discipline is relatively mature at the microscopic (cell, local network) level – although novel discoveries are still commonplace – but requires a far greater understanding of mesoscopic and macroscopic brain dynamics than we currently hold. Without this, extrapolation from the temporal properties of neurons and their communication strategies up to whole brain function will remain largely theoretical. However, recent advances in large-scale neuronal population recordings and more direct, higher fidelity, non-invasive measurement of whole brain function suggest much progress is just around the corner.

The Gonadotropin-Releasing Hormone Pulse Generator

The pulsatile release of GnRH and LH secretion is essential for fertility in all mammals. Pulses of LH occur approximately every hour in follicular-phase females and every 2 to 3 hours in luteal-phase females and males. Many studies over the last 50 years have sought to identify the nature and mechanism of the "GnRH pulse generator" responsible for pulsatile LH release. This review examines the characteristics of pulsatile hormone release and summarizes investigations that have led to our present understanding of the GnRH pulse generator. There is presently little compelling evidence for an intrinsic mechanism of pulse generation involving interactions between GnRH neuron cell bodies. Rather, data support the presence of an extrinsic pulse generator located within the arcuate nucleus, and attention has focused on the kisspeptin neurons and their projections to GnRH neuron dendrons concentrated around the median eminence. Sufficient evidence has been gathered in rodents to conclude that a subpopulation of arcuate kisspeptin neurons is, indeed, the GnRH pulse generator. Findings in other species are generally compatible with this view and suggest that arcuate/infundibular kisspeptin neurons represent the mammalian GnRH pulse generator. With hindsight, it is likely that past arcuate nucleus multiunit activity recordings have been from kisspeptin neurons. Despite advances in identifying the cells forming the pulse generator, almost nothing is known about their mechanisms of synchronicity and the afferent hormonal and transmitter modulation required to establish the normal patterns of LH pulsatility in mammals.

Variability and stability of large-scale cortical oscillation patterns

Individual differences in brain organization exist at many spatiotemporal scales and underlie the diversity of human thought and behavior. Oscillatory neural activity is crucial for these processes, but how such rhythms are expressed across the cortex within and across individuals is poorly understood. We conducted a systematic characterization of brain-wide activity across frequency bands and oscillatory features during rest and task execution. We found that oscillatory profiles exhibit sizable group-level similarities, indicating the presence of common templates of oscillatory organization. Nonetheless, well-defined subject-specific network profiles were discernible beyond the structure shared across individuals. These individualized patterns were sufficiently stable to recognize individuals several months later. Moreover, network structure of rhythmic activity varied considerably across distinct oscillatory frequencies and features, indicating the existence of several parallel information processing streams embedded in distributed electrophysiological activity. These findings suggest that network similarity analyses may be useful for understanding the role of large-scale brain oscillations in physiology and behavior.

Neural oscillations are critical for the human brain’s ability to optimally respond to complex environmental input. However, relatively little is known about the network properties of these oscillatory rhythms. We used electroencephalography (EEG) to analyze large-scale brain wave patterns, focusing on multiple frequency bands and several key features of oscillatory communication. We show that networks defined in this manner are, in fact, distinct, suggesting that EEG activity encompasses multiple, parallel information processing streams. Remarkably, the same networks can be used to uniquely identify individuals over a period of approximately half a year, thus serving as neural fingerprints. These findings indicate that investigating oscillatory dynamics from a network perspective holds considerable promise as a tool to understand human cognition and behavior.

Uncovering Neuronal Networks Defined by Consistent Between-Neuron Spike Timing from Neuronal Spike Recordings

It is widely assumed that distributed neuronal networks are fundamental to the functioning of the brain. Consistent spike timing between neurons is thought to be one of the key principles for the formation of these networks. This can involve synchronous spiking or spiking with time delays, forming spike sequences when the order of spiking is consistent. Finding networks defined by their sequence of time-shifted spikes, denoted here as spike timing networks, is a tremendous challenge. As neurons can participate in multiple spike sequences at multiple between-spike time delays, the possible complexity of networks is prohibitively large. We present a novel approach that is capable of (1) extracting spike timing networks regardless of their sequence complexity, and (2) that describes their spiking sequences with high temporal precision. We achieve this by decomposing frequency-transformed neuronal spiking into separate networks, characterizing each network’s spike sequence by a time delay per neuron, forming a spike sequence timeline. These networks provide a detailed template for an investigation of the experimental relevance of their spike sequences. Using simulated spike timing networks, we show network extraction is robust to spiking noise, spike timing jitter, and partial occurrences of the involved spike sequences. Using rat multineuron recordings, we demonstrate the approach is capable of revealing real spike timing networks with sub-millisecond temporal precision. By uncovering spike timing networks, the prevalence, structure, and function of complex spike sequences can be investigated in greater detail, allowing us to gain a better understanding of their role in neuronal functioning.

Embedded ensemble encoding hypothesis: The role of the "Prepared" cell

We here reconsider current theories of neural ensembles in the context of recent discoveries about neuronal dendritic physiology. The key physiological observation is that the dendritic plateau potential produces sustained depolarization of the cell body (amplitude 10-20 mV, duration 200-500 ms). Our central hypothesis is that synaptically-evoked dendritic plateau potentials lead to a prepared state of a neuron that favors spike generation. The plateau both depolarizes the cell toward spike threshold, and provides faster response to inputs through a shortened membrane time constant. As a result, the speed of synaptic-to-action potential (AP) transfer is faster during the plateau phase. Our hypothesis relates the changes from "resting" to "depolarized" neuronal state to changes in ensemble dynamics and in network information flow. The plateau provides the Prepared state (sustained depolarization of the cell body) with a time window of 200-500 ms. During this time, a neuron can tune into ongoing network activity and synchronize spiking with other neurons to provide a coordinated Active state (robust firing of somatic APs), which would permit "binding" of signals through coordination of neural activity across a population. The transient Active ensemble of neurons is embedded in the longer-lasting Prepared ensemble of neurons. We hypothesize that "embedded ensemble encoding" may be an important organizing principle in networks of neurons.

Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks

Networks of neurons need to reliably encode and replay patterns and sequences of activity. In the brain, sequences of spatially coding neurons are replayed in both the forward and reverse direction in time with respect to their order in recent experience. As of yet there is no network-level or biophysical mechanism known that can produce both modes of replay within the same network. Here we propose that resonance, a property of neurons, paired with subthreshold oscillations in neural input facilitate network-level learning of fixed and sequential activity patterns and lead to both forward and reverse replay.

Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations’ spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections. Using a neuronal network model, we find that due to an input current-dependent shift in their resonance response, individual neurons in a network will arrange their phases of firing to represent varying strengths of their respective inputs. As networks encode information, neurons fire more synchronously, and this effect limits the extent to which further “learning” (in the form of changes in synaptic strength) can occur. We also demonstrate that sequential patterns of neuronal firing can be accurately stored in the network; these sequences are later reproduced without external input (in the context of subthreshold oscillations) in both the forward and reverse directions (as has been observed following learning in vivo). To test whether a similar mechanism could act in vivo, we show that periodic stimulation of hippocampal neurons coordinates network activity and functional connectivity in a frequency-dependent manner. We conclude that resonance with subthreshold oscillations provides a plausible network-level mechanism to accurately encode and retrieve information without overstrengthening connections between neurons.

A New, High-Efficacy, Noninvasive Transcranial Electric Stimulation Tuned to Local Neurodynamics

In this paper, we pose the following working hypothesis: in humans, transcranial electric stimulation (tES) with a time course that mimics the endogenous activity of its target is capable of altering the target's excitability. In our case, the target was the primary motor cortex (M1). We identified the endogenous neurodynamics of hand M1's subgroups of pyramidal neuronal pools in each of our subjects by applying Functional Source Separation (FSS) to their EEG recordings. We then tested whether the corticospinal excitability of the hand representation under the above described stimulation, which we named transcranial individual neurodynamics stimulation (tIDS), was higher than in the absence of stimulation (baseline). As a check, we compared tIDS with the most efficient noninvasive facilitatory corticospinal tES known so far, which is 20 Hz transcranial alternating current stimulation (tACS). The control conditions were as follows: (1) sham, (2) transcranial random noise stimulation (tRNS) in the same frequency range as tIDS (1-250 Hz), and (3) a low current tIDS (tIDS_low). Corticospinal excitability was measured with motor-evoked potentials under transcranial magnetic stimulation. The mean motor-evoked potential amplitude increase was 31% of the baseline during tIDS (p < 0.001), and it was 15% during tACS (p = 0.096). tRNS, tIDS_low, and sham induced no effects. Whereas tACS did not produce an enhancement in any subject at the individual level, tIDS was successful in producing an enhancement in 8 of the 16 subjects. The results of the present proof-of-principle study showed that proper exploitation of local neurodynamics can enhance the efficacy of personalized tES.SIGNIFICANCE STATEMENT This study demonstrated that, in humans, transcranial individual neurodynamics stimulation (tIDS), which mimics the endogenous dynamics of the target neuronal pools, effectively changes the excitability of these pools. tIDS holds promise for high-efficacy personalized neuromodulations based on individual local neurodynamics.

Early Development of Network Oscillations in the Ferret Visual Cortex

Although oscillations during development have been characterized in a wide range of neural systems, little is known about the interaction between these network oscillations and neuronal spiking, and the interactions among different oscillation frequencies. Here we recorded the spontaneous and visual-elicited local field potential (LFP) and multi-unit activity (MUA) in the visual cortex of freely-moving juvenile ferrets before and after eye-opening. We found that both the spontaneous and visually-elicited LFP power was increased after eye-opening, especially in higher frequency bands (>30 Hz). Spike LFP phase coupling was decreased for lower frequency bands (theta and alpha) but slightly increased for higher frequencies (high-gamma band). A similar shift towards faster frequencies also occurred for phase-amplitude coupling; with maturation, the coupling of the theta/alpha/beta band amplitude to the delta phase was decreased and the high-gamma amplitude coupling to theta/alpha phase was increased. This shift towards higher frequencies was also reflected in the visual responses; the LFP oscillation became more entrained by visual stimulation with higher frequencies (>10 Hz). Taken together, these results suggest gamma oscillation as a signature of the maturation of cortical circuitry.

Gap junctional coupling between retinal amacrine and ganglion cells underlies coherent activity integral to global object perception

Coherent spike activity occurs between widely separated retinal ganglion cells (RGCs) in response to a large, contiguous object, but not to disjointed objects. Since the large spatial separation between the RGCs precludes common excitatory inputs from bipolar cells, the mechanism underlying this long-range coherence remains unclear. Here, we show that electrical coupling between RGCs and polyaxonal amacrine cells in mouse retina forms the synaptic mechanism responsible for long-range coherent activity in the retina. Pharmacological blockade of gap junctions or genetic ablation of connexin 36 (Cx36) subunits eliminates the long-range correlated spiking between RGCs. Moreover, we find that blockade of gap junctions or ablation of Cx36 significantly reduces the ability of mice to discriminate large, global objects from small, disjointed stimuli. Our results indicate that synchronous activity of RGCs, derived from electrical coupling with amacrine cells, encodes information critical to global object perception.

Definition of the hypothalamic GnRH pulse generator in mice

The pulsatile release of luteinizing hormone (LH) is critical for mammalian fertility. However, despite several decades of investigation, the identity of the neuronal network generating pulsatile reproductive hormone secretion remains unproven. We use here a variety of optogenetic approaches in freely behaving mice to evaluate the role of the arcuate nucleus kisspeptin (ARN^KISS) neurons in LH pulse generation. Using GCaMP6 fiber photometry, we find that the ARN^KISS neuron population exhibits brief (∼1 min) synchronized episodes of calcium activity occurring as frequently as every 9 min in gonadectomized mice. These ARN^KISS population events were found to be near-perfectly correlated with pulsatile LH secretion. The selective optogenetic activation of ARN^KISS neurons for 1 min generated pulses of LH in freely behaving mice, whereas inhibition with archaerhodopsin for 30 min suppressed LH pulsatility. Experiments aimed at resetting the activity of the ARN^KISS neuron population with halorhodopsin were found to reset ongoing LH pulsatility. These observations indicate the ARN^KISS neurons as the long-elusive hypothalamic pulse generator driving fertility.

The rate of transient beta frequency events predicts behavior across tasks and species

Beta oscillations (15-29Hz) are among the most prominent signatures of brain activity. Beta power is predictive of healthy and abnormal behaviors, including perception, attention and motor action. In non-averaged signals, beta can emerge as transient high-power 'events'. As such, functionally relevant differences in averaged power across time and trials can reflect changes in event number, power, duration, and/or frequency span. We show that functionally relevant differences in averaged beta power in primary somatosensory neocortex reflect a difference in the number of high-power beta events per trial, i.e. event rate. Further, beta events occurring close to the stimulus were more likely to impair perception. These results are consistent across detection and attention tasks in human magnetoencephalography, and in local field potentials from mice performing a detection task. These results imply that an increased propensity of beta events predicts the failure to effectively transmit information through specific neocortical representations.

Systems and synthetic biology approaches in understanding biological oscillators

Background

Self-sustained oscillations are a ubiquitous and vital phenomenon in living systems. From primitive single-cellular bacteria to the most sophisticated organisms, periodicities have been observed in a broad spectrum of biological processes such as neuron firing, heart beats, cell cycles, circadian rhythms, etc. Defects in these oscillators can cause diseases from insomnia to cancer. Elucidating their fundamental mechanisms is of great significance to diseases, and yet challenging, due to the complexity and diversity of these oscillators.

Results

Approaches in quantitative systems biology and synthetic biology have been most effective by simplifying the systems to contain only the most essential regulators. Here, we will review major progress that has been made in understanding biological oscillators using these approaches. The quantitative systems biology approach allows for identification of the essential components of an oscillator in an endogenous system. The synthetic biology approach makes use of the knowledge to design the simplest, de novo oscillators in both live cells and cell-free systems. These synthetic oscillators are tractable to further detailed analysis and manipulations.

Conclusion

With the recent development of biological and computational tools, both approaches have made significant achievements.

High-Precision Fast-Spiking Basket Cell Discharges during Complex Events in the Human Neocortex

In the human neocortex, solitary action potentials in some layer 2–3 pyramidal cells (PCs) trigger brief episodes of network activity known as complex events through strong excitatory synapses that specifically innervate GABAergic interneurons. Yet, how these “master PCs” configure the local network activity is not well understood. We report that single spikes in the PCs, studied here in synaptically connected cell pairs in frontal or temporal neocortical areas of both males and females, elicit firing of fast-spiking basket cells (FSBCs) with a short delay (on average 2.7 ms). The FSBC discharge is triggered by 13 mV (on average) monosynaptic EPSPs, and the action potential is time locked to the master PC spike with high temporal precision, showing little jitter in delay. In the complex events, the FSBC discharge occurs in the beginning of the activity episode, forming the first wave of the complex event activity. Firing of FSBCs generates GABAergic IPSCs with fast kinetics in layer 2–3 PCs, and similar IPSCs regularly occur time locked to master PC spikes in the beginning of the complex events with high probability and short (median 4.1 ms) delay with little jitter. In comparison, discharge of nonfast spiking interneurons (non-FSINs) investigated here appears inconsistently in the complex events and shows low probability. Thus, firing of layer 2–3 FSBCs with high temporal fidelity characterizes early phase of the complex events in the human neocortex.

Different roles for inhibition in the rhythm-generating respiratory network

Unraveling the interplay of excitation and inhibition within rhythm-generating networks remains a fundamental issue in neuroscience. We use a biophysical model to investigate the different roles of local and long-range inhibition in the respiratory network, a key component of which is the pre-Bötzinger complex inspiratory microcircuit. Increasing inhibition within the microcircuit results in a limited number of out-of-phase neurons before rhythmicity and synchrony degenerate. Thus unstructured local inhibition is destabilizing and cannot support the generation of more than one rhythm. A two-phase rhythm requires restructuring the network into two microcircuits coupled by long-range inhibition in the manner of a half-center. In this context, inhibition leads to greater stability of the two out-of-phase rhythms. We support our computational results with in vitro recordings from mouse pre-Bötzinger complex. Partial excitation block leads to increased rhythmic variability, but this recovers after blockade of inhibition. Our results support the idea that local inhibition in the pre-Bötzinger complex is present to allow for descending control of synchrony or robustness to adverse conditions like hypoxia. We conclude that the balance of inhibition and excitation determines the stability of rhythmogenesis, but with opposite roles within and between areas. These different inhibitory roles may apply to a variety of rhythmic behaviors that emerge in widespread pattern-generating circuits of the nervous system.NEW & NOTEWORTHY The roles of inhibition within the pre-Bötzinger complex (preBötC) are a matter of debate. Using a combination of modeling and experiment, we demonstrate that inhibition affects synchrony, period variability, and overall frequency of the preBötC and coupled rhythmogenic networks. This work expands our understanding of ubiquitous motor and cognitive oscillatory networks.

Efficiency of rate and latency coding with respect to metabolic cost and time

Recent studies on the theoretical performance of latency and rate code in single neurons have revealed that the ultimate accuracy is affected in a nontrivial way by aspects such as the level of spontaneous activity of presynaptic neurons, amount of neuronal noise or the duration of the time window used to determine the firing rate. This study explores how the optimal decoding performance and the corresponding conditions change when the energy expenditure of a neuron in order to spike and maintain the resting membrane potential is accounted for. It is shown that a nonzero amount of spontaneous activity remains essential for both the latency and the rate coding. Moreover, the optimal level of spontaneous activity does not change so much with respect to the intensity of the applied stimulus. Furthermore, the efficiency of the temporal and the rate code converge to an identical finite value if the neuronal activity is observed for an unlimited period of time.

Sleep-Dependent Oscillatory Synchronization: A Role in Fear Memory Consolidation

Sleep plays an important role in memory consolidation through the facilitation of neuronal plasticity; however, how sleep accomplishes this remains to be completely understood. It has previously been demonstrated that neural oscillations are an intrinsic mechanism by which the brain precisely controls neural ensembles. Inter-regional synchronization of these oscillations is also known to facilitate long-range communication and long-term potentiation (LTP). In the present study, we investigated how the characteristic rhythms found in local field potentials (LFPs) during non-REM and REM sleep play a role in emotional memory consolidation. Chronically implanted bipolar electrodes in the lateral amygdala (LA), dorsal and ventral hippocampus (DH, VH), and the infra-limbic (IL), and pre-limbic (PL) prefrontal cortex were used to record LFPs across sleep-wake activity following each day of a Pavlovian cued fear conditioning paradigm. This resulted in three principle findings: (1) theta rhythms during REM sleep are highly synchronized between regions; (2) the extent of inter-regional synchronization during REM and non-REM sleep is altered by FC and EX; (3) the mean phase difference of synchronization between the LA and VH during REM sleep predicts changes in freezing after cued fear extinction. These results both oppose a currently proposed model of sleep-dependent memory consolidation and provide a novel finding which suggests that the role of REM sleep theta rhythms in memory consolidation may rely more on the relative phase-shift between neural oscillations, rather than the extent of phase synchronization.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.

Aberrant Network Activity in Schizophrenia

Brain dynamic changes associated with schizophrenia are largely equivocal, with interpretation complicated by many factors, such as the presence of therapeutic agents and the complex nature of the syndrome itself. Evidence for a brain-wide change in individual network oscillations, shared by all patients, is largely equivocal, but stronger for lower (delta) than for higher (gamma) bands. However, region-specific changes in rhythms across multiple, interdependent, nested frequencies may correlate better with pathology. Changes in synaptic excitation and inhibition in schizophrenia disrupt delta rhythm-mediated cortico-cortical communication, while enhancing thalamocortical communication in this frequency band. The contrasting relationships between delta and higher frequencies in thalamus and cortex generate frequency mismatches in inter-regional connectivity, leading to a disruption in temporal communication between higher-order brain regions associated with mental time travel.