Firing Patterns Research Articles

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Abstract Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant 7D neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum and two-dimensional dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

One-shot entorhinal maps enable flexible navigation in novel environments.

Animals must navigate changing environments to find food, shelter or mates. In mammals, grid cells in the medial entorhinal cortex construct a neural spatial map of the external environment1-5. However, how grid cell firing patterns rapidly adapt to novel or changing environmental features on a timescale relevant to behaviour remains unknown. Here, by recording over 15,000 grid cells in mice navigating virtual environments, we tracked the real-time state of the grid cell network. This allowed us to observe and predict how altering environmental features influenced grid cell firing patterns on a nearly instantaneous timescale. We found evidence that visual landmarks provide inputs to fixed points in the grid cell network. This resulted in stable grid cell firing patterns in novel and altered environments after a single exposure. Fixed visual landmark inputs also influenced the grid cell network such that altering landmarks induced distortions in grid cell firing patterns. Such distortions could be predicted by a computational model with a fixed landmark to grid cell network architecture. Finally, a medial entorhinal cortex-dependent task revealed that although grid cell firing patterns are distorted by landmark changes, behaviour can adapt via a downstream region implementing behavioural timescale synaptic plasticity6. Overall, our findings reveal how the navigational system of the brain constructs spatial maps that balance rapidity and accuracy. Fixed connections between landmarks and grid cells enable the brain to quickly generate stable spatial maps, essential for navigation in novel or changing environments. Conversely, plasticity in regions downstream from grid cells allows the spatial maps of the brain to more accurately mirror the external spatial environment. More generally, these findings raise the possibility of a broader neural principle: by allocating fixed and plastic connectivity across different networks, the brain can solve problems requiring both rapidity and representational accuracy.

Neuronal Vulnerability of the Entorhinal Cortex to Tau Pathology in Alzheimer's Disease.

This review delves into the entorhinal cortex (EC) as a central player in the pathogenesis of Alzheimer's Disease (AD), emphasizing its role in the accumulation and propagation of tau pathology. It elucidates the multifaceted functions of the EC, encompassing memory formation, spatial navigation, and olfactory processing, while exploring how disruptions in these processes contribute to cognitive decline in AD. The review discusses the intricate interplay between tau pathology and EC vulnerability, highlighting how alterations in neuronal firing patterns and synaptic function within the EC exacerbate cognitive impairments. Furthermore, it elucidates how specific neuronal subtypes within the EC exhibit differential susceptibility to tau-induced damage, contributing to disease progression. Early detection methods, such as imaging techniques and assessments of EC blood flow, are examined as potential tools for identifying tau pathology in the preclinical stages of AD. These approaches offer promise for improving diagnostic accuracy and enabling timely intervention. Therapeutic strategies targeting tau pathology within the EC are explored, including the clearance of pathological tau aggregates and the inhibition of tau aggregation processes. By understanding the molecular and cellular mechanisms underlying EC vulnerability, researchers can develop more targeted and effective interventions to slow disease progression. The review underscores the importance of reliable biomarkers to assess disease progression and therapeutic efficacy in clinical trials targeting the EC. Ultimately, it aims to contribute to the development of more effective management strategies for AD, emphasizing the translation of research findings into clinical practice to address the growing societal burden of the disease.

A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs

In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm2, and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.

7170 Plasticity of the Hypothalamic Tuberoinfundibular Dopaminergic Neuronal Network in Lactating Rats

Abstract Disclosure: P. Papaioannou: None. T. Georgescu: None. D.R. Grattan: None. S. Bunn: None. S. Yip: None. The tuberoinfundibular dopaminergic (TIDA) neurons play a crucial role in regulating prolactin secretion. Their synchronized network activity with slow but highly rhythmic firing pattern is important for dopamine release in rats[1]. While this unique TIDA network activity is vital for prolactin negative feedback in non-lactating conditions, its behaviour during lactation, when prolactin demand is high, remains unknown. Our hypothesis posited that the TIDA neuronal network in lactating rats becomes desynchronized, disrupting the negative feedback loop. To test this, we utilized ex-vivo Ca2+ imaging to simultaneously monitor population-wide TIDA neuron activity in non-lactating (NL; n=13) and lactating (L; n=10) rats injected with a cre-inducible adeno-associated virus (AAV) containing the Ca2+ indicator, GCaMP6s, into their arcuate nucleus. Analysis of neuronal network synchronicity revealed significant desynchronization in the TIDA network during lactation, as indicated by a markedly lower mean correlation coefficient matrix (CM) compared to non-lactating conditions (NL: 0.87±0.02, n= 26 sections vs L: 0.22±0.03, n=29 sections; p<0.001, Student’s t-test). Furthermore, the oscillatory activity patterns of these desynchronized TIDA neurons displayed a significantly lower cell rhythmicity index (RI) in lactating animals compared to non-lactating ones (NL: 0.17±0.01, n=186 cells vs L: 0.70±0.02, n=77 cells; p<0.001, Student’s t-test). Interestingly, within these low rhythmic neurons, some exhibited higher while others showed lower firing frequencies compared to non-lactating TIDA neurons. After 7 days post-weaning (n=4 animals), the TIDA network activity returned to non-lactating behaviour (CM = 0.85 ± 0.04, n=6 sections and RI = 0.17±0.01; n=47 cells). In summary, our findings demonstrate a reversible reconfiguration of the TIDA neuronal networks during lactation, characterized by low rhythmic oscillations and individual unique firing patterns, leading to diminished intercellular synchrony that may impede dopamine release, ultimately facilitating prolactin release to support lactation. Once weaned, the TIDA neuronal network reset to NL state to allow a new reproductive cycle. [1] Lyons, D. J., Horjales-Araujo, E. & Broberger, C. Synchronized network oscillations in rat tuberoinfundibular dopamine neurons: switch to tonic discharge by thyrotropin-releasing hormone. Neuron 65, 217-229, doi:10.1016/j.neuron.2009.12.024 (2010). Presentation: 6/2/2024

The Effects of Omeprazole on the Neuron-like Spiking of the Electrical Potential of Proteinoid Microspheres.

This study examines a new approach to hybrid neuromorphic devices by studying the impact of omeprazole-proteinoid complexes on Izhikevich neuron models. We investigate the influence of these metabolic structures on five specific patterns of neuronal firing: accommodation, chattering, triggered spiking, phasic spiking, and tonic spiking. By combining omeprazole, a proton pump inhibitor, with proteinoids, we create a unique substrate that interfaces with neuromorphic models. The Izhikevich neuron model is used because it is computationally efficient and can accurately simulate the various behaviours of cortical neurons. The results of our simulations show that omeprazole-proteinoid complexes have the ability to affect neuronal dynamics in different ways. This suggests that they could be used as adjustable components in bio-inspired computer systems. We noticed a notable alteration in the frequency of spikes, patterns of bursts, and rates of adaptation, especially in chattering and triggered spiking behaviours. The findings indicate that omeprazole-proteinoid complexes have the potential to serve as adaptable elements in neuromorphic systems, presenting novel opportunities for information processing and computation that have origins in neurobiological principles. This study makes a valuable contribution to the expanding field of biochemical neuromorphic devices and establishes a basis for the development of hybrid bio-synthetic computational systems.

Unraveling the dynamics of firing patterns for neurons with impairment of sodium channels.

Various factors such as mechanical trauma, chemical trauma, local ischemia, and inflammation can impair voltage-gated sodium channels (Nav) in neurons. These impairments lead to a distinctive leftward shift in the activation and inactivation curves of voltage-gated sodium channels. The resulting sodium channel impairments in neurons are known to affect firing patterns, which play a significant role in neuronal activities within the nervous system. However, the underlying dynamic mechanism for the emergence of these firing patterns remains unclear. In this study, we systematically investigated the effects of sodium channel dysfunction on individual neuronal dynamics and firing patterns. By employing codimension-1 bifurcation analysis, we revealed the underlying dynamical mechanism responsible for the generation of different firing patterns. Additionally, through codimension-2 bifurcation analysis, we theoretically determined the distribution of firing patterns on different parameter planes. Our results indicate that the firing patterns of impaired neurons are regulated by multiple parameters, with firing pattern transitions caused by the degree of sodium channel impairment being more diverse than those caused by the ratio of impaired sodium channel and current. Furthermore, we observed that the firing pattern of tonic firing is more likely to be the norm in impaired sodium channel neurons, providing valuable insights into the signaling of impaired neurons. Overall, our findings highlight the intricate relationships among sodium channel impairments, neuronal dynamics, and firing patterns, shedding light on the impact of disruptions in ion concentration gradients on neuronal function.

The role of NMDA receptors in fish stress response: Assessments based on physiology of the caudal neurosecretory system and defensive behavior.

Stress strongly influences the physiology and behavior of animals, and leads into a pathological condition and disease. NMDA receptors (NMDARs) play a crucial role in the modulation of neural activity. To understand the role of NMDARs in fish stress response, we used NMDARs agonist aspartate to test the functional role of its input on the Dahlgren cell population in the caudal neurosecretory system (CNSS) of the olive flounder. In addition, the effect of the NMDARs antagonist D-AP5 on the expression of genes of the main secretory products of the CNSS after stress was investigated by using qPCR technology and the effect of the NMDARs antagonist D-AP5 on post-stress behavior was explored by behavioral methods. Ex vivo electrophysiological experiments showed that the NMDARs agonist aspartate enhanced the firing frequency of Dahlgren cells. Additionally, aspartate treatment increased the incidence of cells exhibiting bursting firing pattern, this result is corroborated by the observed upregulation in the expression of ion channels and major hormone genes in the CNSS. Furthermore, the excitatory influence of aspartate was effectively counteracted by NMDARs antagonist D-AP5. Interestingly, NMDARs antagonist D-AP5 treatment also significantly decreased the plasma cortisol levels and the expression of CRH, UI, and UII in CNSS after acute stress. Treatment with D-AP5 effectively attenuated the stress response, as evidenced by alterations in respiratory metabolism, sand-burying behavior, swimming distance, simulated capture, and escape response. In conclusion, modulation of Dahlgren cell excitability in the CNSS by NMDARs contributes to the regulation of the stress response, NMDARs antagonist D-AP5 can effectively suppress stress response in flounder by regulating the stress hormone expression and secretion. CLINICAL TRIAL REGISTRATION: Project code SHOU-DW-2022-032.

Spatiotemporal analysis of wildfires and their relationship with climate and land use in the Gran Chaco and Pantanal ecoregions

The Gran Chaco and Pantanal ecoregions are the largest remaining dry forest areas in South America. Supporting diverse savanna, woodland and wetland ecosystems, these ecoregions are experiencing rapid changes in land use and fire occurrence with implications for ecosystem integrity. Our study characterizes the spatiotemporal patterns of wildfires in the Gran Chaco and Pantanal, and then examines the relationship between patterns of fire occurrence and climatic and anthropogenic drivers. We evaluated fire data of the last two decades (2001-2020) using the MODIS Collection 6.1 and the Global Fire Atlas products. Results of the fire pattern characterization were then used to model the probability of fire occurrence across each ecoregion (Random Forest, Generalized Linear Model, and Generalized Additive Model).Our results indicated that most of the total burned area belonged to the Humid Chaco, while the largest individual burned areas were mainly observed in the Pantanal. Fires primarily occurred during the dry season, with the majority of burned areas recorded during this period. Findings from the three modelling approaches consistently illustrated the spatial distribution of fire occurrence, depicting a declining probability of fire occurrence from East to West. All models underscored the importance of three variables to predict fire occurrence: temperature, livestock abundance and forest cover. Fire occurrence increased with increasing maximum temperatures and livestock presence and decreased with tree cover. This research helps to clarify the potential consequences of changes in land use, rainfall regime and temperature, and uncontrolled burning practices on the current fire activity in the Gran Chaco and Pantanal ecoregions. Understanding the spatiotemporal patterns of fire occurrence and their relationship with climatic, environmental and anthropogenic drivers can help to design more effective management strategies to mitigate fire impacts and to preserve the ecological integrity of these highly diverse regions

Assessing Fire Regimes in the Paraguayan Chaco: Implications for Ecological and Fire Management

This study analyzed the fire regime in the highly diverse Paraguayan Chaco, focusing on different aspects of fire patterns, including spatial (area burned) and temporal (frequency) aspects and magnitude (severity). We focused on fire as it is a natural phenomenon that drives ecosystem change and has significant economic, ecological and social impacts of particular concern in vulnerable ecosystems. Using the K-means clustering technique, we identified four distinct fire regimes in the study region: High (H), Moderately High (MH), Moderately Low (ML) and Low (L). On the one hand, the Dry Chaco predominantly featured Low and Moderately High regimes, characterized by a low fire frequency due to arid conditions. On the other hand, the Humid Chaco was particularly affected by agricultural burning, driven by extensive livestock activity and higher biomass productivity. Finally, in the Pantanal, the variations in fire intensity were influenced by flood pulses and rainfall patterns. Our findings highlight the distinct fire regimes across the Paraguayan Chaco and detail the differences in the regimes. The study’s findings are valuable for developing efficient management strategies that account for fire behaviour during agricultural burning in this poorly studied region.

Motor control of the palatoglossus and genioglossus during changes in breathing route.

High activity of upper airway dilator muscles is thought to be critical in preventing sleep-related upper airway collapse. To date, most of the research regarding upper airway dilator muscles has focused on the genioglossus muscle, which protrudes the tongue and opens the retroglossal airway. However, collapse commonly occurs in the retropalatal region. We therefore aimed to examine the motor control of the palatoglossus muscle as well as investigate breathing route-related changes in genioglossus and palatoglossus motor units. Single motor unit recordings of the genioglossus and palatoglossus were made simultaneously in healthy individuals during wakefulness while breathing through the nose with the mouth closed (NMC), nose with mouth open (NMO) or orally (OMO). The palatoglossus was found to have all 5 motor unit firing patterns that have been observed in other upper airway dilator muscles, but during nasal breathing had a higher proportion of tonically active but inspiratory modulated motor units as compared to the genioglossus (67% vs 30%). When still breathing nasally but with the mouth open, the units with an expiratory firing pattern in genioglossus, and all firing patterns in palatoglossal, increased their firing rates compared to nasal breathing with the mouth closed (GG: 17.8±4.9 vs 23.1±4.8 Hz, PG: 17.0±4.0 vs 19.3±4.0 Hz). Finally, oral breathing resulted in dramatic reductions in the number of palatoglossal motor units that were firing (35 units vs 92 during nasal breathing). Palatoglossus activity may contribute importantly to airway collapsibility and may provide an alternate pathway for preventing sleep-related airway collapse.

Firing properties of single axons with cardiac rhythmicity in the human cervical vagus nerve.

Microneurographic recordings of the human cervical vagus nerve have revealed the presence of multi-unit neural activity with measurable cardiac rhythmicity. This suggests that the physiology of vagal neurones with cardiovascular regulatory function can be studied using this method. Here, the activity of cardiac rhythmic single units was discriminated from human cervical vagus nerve recordings using template-based waveform matching. The activity of 44 cardiac rhythmic neurones (22 with myelinated axons and 22 with unmyelinated axons) was isolated. By consideration of each unit's firing pattern with respect to the cardiac and respiratory cycles, the functional identification of each unit was attempted. Of note is the observation of seven cardiac rhythmic neurones with myelinated axons whose activity was recruited or enhanced by slow, deep breathing, was maximal during the nadir of respiratory sinus arrhythmia, and showed an expiratory peak. This is characteristic of cardioinhibitory efferent neurones, which are responsible for respiratory sinus arrhythmia. The remaining 15 cardiac rhythmic neurones with myelinated axons were categorised as cardiopulmonary receptors or arterial baroreceptors based on the position of their peak in firing with respect to the R-wave of the cardiac cycle. This latter method is not viable for neurones with unmyelinated axons due to their slow and unknown conduction velocities. With the exception of three neurones whose expiratory modulation implicates them as cardiac-projecting efferent neurones, this population is likely dominated by arterial baroreceptors. In conclusion, the activity of single units with cardiovascular function has been discriminated within the human cervical vagus, enabling their systematic study. KEY POINTS: Recordings of the electrical activity of the vagus nerve have recently been made at the level of the neck in humans. Examination of the gross activity of this nerve reveals subpopulations of neurones whose activity fluctuates in time with the heart's beat, suggesting that the neurones that monitor or modify cardiac function can be studied using this method. Here, the activity of individual cardiac rhythmic neurones was isolated from human vagus nerve recordings using template-based spike sorting. The relationship between this activity and the cardiac and respiratory cycles was used as a means of classifying each neurone. Neuronal firing patterns that are consistent with that of neurones that modify cardiac function, including heart-slowing 'cardioinhibitory' neurones, as well as neurones that inform the brain of cardiovascular status were observed. This approach enables, for the first time, the systematic study of the function of these neurones in humans in both health and disease.

A Rectified Linear Unit-Based Memristor-Enhanced Morris–Lecar Neuron Model

This paper introduces a modified Morris–Lecar neuron model that incorporates a memristor with a ReLU-based activation function. The impact of the memristor on the dynamics of the ML neuron model is analyzed using bifurcation diagrams and Lyapunov exponents. The findings reveal chaotic behavior within specific parameter ranges, while increased magnetic strength tends to maintain periodic dynamics. The emergence of various firing patterns, including periodic and chaotic spiking as well as square-wave and triangle-wave bursting is also evident. The modified model also demonstrates multistability across certain parameter ranges. Additionally, the dynamics of a network of these modified models are explored. This study shows that synchronization depends on the strength of the magnetic flux, with synchronization occurring at lower coupling strengths as the magnetic flux increases. The network patterns also reveal the formation of different chimera states, such as traveling and non-stationary chimera states.

Vegetation phenology as a key driver for fire occurrence in the UK and comparable humid temperate regions

Background Fire activity in the UK and comparable regions of northwest Europe is generally out of phase with peak fire weather conditions. Aims Here, we assess the potential effect of phenology on fire occurrence patterns for the UK. Methods We examined fire occurrence and vegetation phenology in the UK for 2012–2023, mapped onto the main fire-affected vegetation cover types within distinct precipitation regions, allowing the fire occurrence for fuels in different phenological phases to be explored across distinct ‘fuel’ types and regions. Key results The UK’s fire regime is characterised by burning in semi-natural grasslands and evergreen dwarf shrub ecosystems in early spring when vegetation is still dormant. During the high-greenness phase in late spring and summer, fire activity is reduced by a factor of 5–6 despite typically elevated fire weather conditions within that period. Conclusions and implications Semi-natural vegetation in the UK is very resistant to burning during the high-greenness phase. However, this ‘fire barrier’ is diminished during severe drought episodes, which are predicted to become more extreme in the coming decades. Incorporating phenology information into models therefore has great potential for improving future fire danger and behaviour predictions in the UK and comparable humid temperate regions.

Predicting the firing behaviour of neural network through the bifurcation analysis of derivative mean-field model

The mean-field model is an important method for understanding the complex dynamics of the nervous system at different spatial levels and for simulating and theoretically analysing the collective dynamic behaviour of large neural populations. In the work, we construct an improved mean-field system of neural networks coupled with quadratic integrate-and-fire neurons and examine the discharge patterns for networks by analysing such a model, which is a three-dimensional smooth differential system. Bifurcation analysis of the mean-field model is conducted from both theoretical and simulation perspectives, we obtain the bifurcation conditions of some co-dimension-two bifurcations and divide the parameter space into different regimes by simulating two parameters bifurcation diagrams. We find a close correspondence, though with some variance, between the mean-field model and neural network when comparing the firing patterns of the two models in various parameter regimes. In summary, the obtained mean-field description builds the bridge between the parameters of neurons or networks and that of a mean-field system to ensure we can compare them and understand the connections between them. Specifically, the mean-field model can reflect the dynamics of neural networks from a macroscopic perspective, and its bifurcation can predict the behaviour of neural networks to a certain extent and understand the mechanisms behind them, such as bursting dynamics of neural networks.

Bionic firing activities in a dual mem-elements based CNN cell

Firing activities provide the potential possibility for achieving bio-brain functionality with high energy-efficient and high-speed information processing performance. This inspires the design of bionic circuits to generate firing activities and develop brain-like applications. In this paper, a dual mem-elements based cellular neural network (CNN) cell is constructed to produce bionic firing activities, in which a non-ideal memcapacitor and an N-type locally active memristor are employed to emulate the functions of the neuronal membrane. The proposed CNN cell has an excitation-dependent equilibrium trajectory and stability. Numerical analysis shows that the dual mem-elements based CNN cell has abundant dynamical behaviors of forward/reverse period-doubling bifurcation routes, chaos crisis, tangent bifurcation, and bubbles with the change of model parameters of the CNN cell, memcapacitor, and exciting source. As a result, the rich firing patterns’ transition can be observed from the two-dimensional dynamics evolution. The analog circuit of the proposed CNN cell is designed, and then a PCB-based hardware circuit is implemented. The experimental results certify the accuracy of the theoretical and numerical analysis.

Unraveling the Complexity of Parkinson's Disease: Insights into Pathogenesis and Precision Interventions.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron loss, leading to motor and non-motor symptoms. Early detection before symptom onset is crucial but challenging. This study presents a framework integrating circuit modeling, non-equilibrium dynamics, and optimization to understand PD pathogenesis and enable precision interventions. Neuronal firing patterns, particularly oscillatory activity, play a critical role in PD pathology. The basal ganglia network, specifically the subthalamic nucleus-external globus pallidus (STN-GPe) circuitry, exhibits abnormal activity associated with motor dysfunction. The framework leverages the non-equilibrium landscape and flux theory to identify key connections generating pathological activity, providing insights into disease progression and potential intervention points. The intricate STN-GPe interplay is highlighted, shedding light on compensatory mechanisms within this circuitry may initially counteract changes but later contribute to pathological alterations as disease progresses. The framework addresses the need for comprehensive evaluation methods to assess intervention outcomes. Cross-correlations between state variables provide superior early warning signals compared to traditional indicators relying on critical slowing down. By elucidating compensatory mechanisms and circuit dynamics, the framework contributes to improved management, early detection, risk assessment, and potential prevention/delay of PD development. This pioneering research paves the way for precision medicine in neurodegenerativedisorders.

Social odors drive hippocampal CA2 place cell responses to social stimuli.

Hippocampal region CA2 is essential for social memory processing. Interaction with social stimuli induces changes in CA2 place cell firing during active exploration and sharp wave-ripples during rest following a social interaction. However, it is unknown whether these changes in firing patterns are caused by integration of multimodal social stimuli or by a specific sensory modality associated with a social interaction. Rodents rely heavily on chemosensory cues in the form of olfactory signals for social recognition processes. To determine the extent to which social olfactory signals contribute to CA2 place cell responses to social stimuli, we recorded CA2 place cells in rats freely exploring environments containing stimuli that included or lacked olfactory content. We found that CA2 place cell firing patterns significantly changed only when social odors were prominent. Also, place cells that increased their firing in the presence of social odors alone preferentially increased their firing during subsequent sharp wave-ripples. Our results suggest that social olfactory cues are essential for changing CA2 place cell firing patterns during and after social interactions. These results support prior work suggesting CA2 performs social functions and shed light on processes underlying CA2 responses to social stimuli.

Cell-type-specific origins of locomotor rhythmicity at different speeds in larval zebrafish

Different speeds of locomotion require heterogeneous spinal populations, but a common mode of rhythm generation is presumed to exist. Here, we explore the cellular versus synaptic origins of spinal rhythmicity at different speeds by performing electrophysiological recordings from premotor excitatory interneurons in larval zebrafish. Chx10-labeled V2a neurons are divided into at least two morphological subtypes proposed to play distinct roles in timing and intensity control. Consistent with distinct rhythm generating and output patterning functions within the spinal V2a population, we find that descending subtypes are recruited exclusively at slow or fast speeds and exhibit intrinsic cellular properties suitable for rhythmogenesis at those speeds, while bifurcating subtypes are recruited more reliably at all speeds and lack appropriate rhythmogenic cellular properties. Unexpectedly, however, phasic firing patterns during locomotion in rhythmogenic and non-rhythmogenic V2a neurons alike are best explained by distinct modes of synaptic inhibition linked to cell type and speed. At fast speeds reciprocal inhibition in descending V2a neurons supports phasic firing, while recurrent inhibition in bifurcating V2a neurons helps pattern motor output. In contrast, at slow speeds recurrent inhibition in descending V2a neurons supports phasic firing, while bifurcating V2a neurons rely on reciprocal inhibition alone to pattern output. Our findings suggest cell-type-specific, not common, modes of rhythmogenesis generate and coordinate different speeds of locomotion.

Realistic mossy fiber input patterns to unipolar brush cells evoke a continuum of temporal responses comprised of components mediated by different glutamate receptors.

Unipolar brush cells (UBCs) are excitatory interneurons in the cerebellar cortex that receive mossy fiber (MF) inputs and excite granule cells. The UBC population responds to brief burst activation of MFs with a continuum of temporal transformations, but it is not known how UBCs transform the diverse range of MF input patterns that occur in vivo. Here we use cell-attached recordings from UBCs in acute cerebellar slices to examine responses to MF firing patterns that are based on in vivo recordings. We find that MFs evoke a continuum of responses in the UBC population, mediated by three different types of glutamate receptors that each convey a specialized component. AMPARs transmit timing information for single stimuli at up to 5 spikes/s, and for very brief bursts. A combination of mGluR2/3s (inhibitory) and mGluR1s (excitatory) mediates a continuum of delayed, and broadened responses to longer bursts, and to sustained high frequency activation. Variability in the mGluR2/3 component controls the time course of the onset of firing, and variability in the mGluR1 component controls the duration of prolonged firing. We conclude that the combination of glutamate receptor types allows each UBC to simultaneously convey different aspects of MF firing. These findings establish that UBCs are highly flexible circuit elements that provide diverse temporal transformations that are well suited to contribute to specialized processing in different regions of the cerebellar cortex.