Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways

The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the computation of Purkinje cells (PCs), the only output cells of the cerebellar cortex, is implemented through various types of neural pathways interactively routing excitation and inhibition converged to PCs. Such tuning of excitation and inhibition, coming from the gating of specific pathways as well as short-term plasticity (STP) of the synapses, plays a dominant role in controlling the PC dynamics in terms of firing rate and spike timing. PCs receive cascade feedforward inputs from two major neural pathways: the first one is the feedforward excitatory pathway from granule cells (GCs) to PCs; the second one is the feedforward inhibition pathway from GCs, via molecular layer interneurons (MLIs), to PCs. The GC-PC pathway, together with short-term dynamics of excitatory synapses, has been a focus over past decades, whereas recent experimental evidence shows that MLIs also greatly contribute to controlling PC activity. Therefore, it is expected that the diversity of excitation gated by STP of GC-PC synapses, modulated by strong inhibition from MLI-PC synapses, can promote the computation performed by PCs. However, it remains unclear how these two neural pathways are interacted to modulate PC dynamics. Here using a computational model of PC network installed with these two neural pathways, we addressed this question to investigate the change of PC firing dynamics at the level of single cell and network. We show that the nonlinear characteristics of excitatory STP dynamics can significantly modulate PC spiking dynamics mediated by inhibition. The changes in PC firing rate, firing phase, and temporal spike pattern, are strongly modulated by these two factors in different ways. MLIs mainly contribute to variable delays in the postsynaptic action potentials of PCs while modulated by excitation STP. Notably, the diversity of synchronization and pause response in the PC network is governed not only by the balance of excitation and inhibition, but also by the synaptic STP, depending on input burst patterns. Especially, the pause response shown in the PC network can only emerge with the interaction of both pathways. Together with other recent findings, our results show that the interaction of feedforward pathways of excitation and inhibition, incorporated with synaptic short-term dynamics, can dramatically regulate the PC activities that consequently change the network dynamics of the cerebellar circuit.     

Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network.     

Balancing feed-forward excitation and inhibition via Hebbian inhibitory synaptic plasticity

It has been suggested that excitatory and inhibitory inputs to cortical cells are balanced, and that this balance is important for the highly irregular firing observed in the cortex. There are two hypotheses as to the origin of this balance. One assumes that it results from a stable solution of the recurrent neuronal dynamics. This model can account for a balance of steady state excitation and inhibition without fine tuning of parameters, but not for transient inputs. The second hypothesis suggests that the feed forward excitatory and inhibitory inputs to a postsynaptic cell are already balanced. This latter hypothesis thus does account for the balance of transient inputs. However, it remains unclear what mechanism underlies the fine tuning required for balancing feed forward excitatory and inhibitory inputs. Here we investigated whether inhibitory synaptic plasticity is responsible for the balance of transient feed forward excitation and inhibition. We address this issue in the framework of a model characterizing the stochastic dynamics of temporally anti-symmetric Hebbian spike timing dependent plasticity of feed forward excitatory and inhibitory synaptic inputs to a single post-synaptic cell. Our analysis shows that inhibitory Hebbian plasticity generates 'negative feedback' that balances excitation and inhibition, which contrasts with the 'positive feedback' of excitatory Hebbian synaptic plasticity. As a result, this balance may increase the sensitivity of the learning dynamics to the correlation structure of the excitatory inputs.     

Induction and propagation of transient synchronous activity in neural networks endowed with short-term plasticity

Transient, task related synchronous activity within neural populations has been recognized as the substrate of temporal coding in the brain. The mechanisms underlying inducing and propagation of transient synchronous activity are still unknown, and we propose that short-term plasticity (STP) of neural circuits may serve as a supplemental mechanism therein. By computational modeling, we showed that short-term facilitation greatly increases the reactivation rate of population spikes and decreases the latency of response to reactivation stimuli in local recurrent neural networks. Meanwhile, the timing of population spike reactivation is controlled by the memory effect of STP, and it is mediated primarily by the facilitation time constant. Furthermore, we demonstrated that synaptic facilitation dramatically enhances synchrony propagation in feedforward neural networks and that response timing mediated by synaptic facilitation offers a scheme for information routing. In addition, we verified that synaptic strengthening of intralayer or interlayer coupling enhances synchrony propagation, and we verified that other factors such as the delay of synaptic transmission and the mode of synaptic connectivity are also involved in regulating synchronous activity propagation. Overall, our results highlight the functional role of STP in regulating the inducing and propagation of transient synchronous activity, and they may inspire testable hypotheses for future experimental studies.     

Towards biologically constrained attractor models of schizophrenia

Alterations in neuromodulation or synaptic transmission in biophysical attractor network models, as proposed by the dominant dopaminergic and glutamatergic theories of schizophrenia, successfully mimic working memory (WM) deficits in people with schizophrenia (PSZ). Yet, multiple, often opposing alterations in memory circuits can lead to the same behavioral patterns in these network models. Here, we critically revise the computational and experimental literature that links NMDAR hypofunction to WM precision loss in PSZ. We show in network simulations that currently available experimental evidence cannot set apart competing biophysical accounts. Critical points to resolve are the effects of increases vs. decreases in E/I ratio (e.g. through NMDAR blockade) on firing rate tuning and shared noise modulations and possible concomitant deficits in short-term plasticity. We argue that these concerted experimental and computational efforts will lead to a better understanding of the neurobiology underlying cognitive deficits in PSZ.     

Synaptic clustering by dendritic signalling mechanisms

Dendritic signal integration is one of the fundamental building blocks of information processing in the brain. Dendrites are endowed with mechanisms of nonlinear summation of synaptic inputs leading to regenerative dendritic events including local sodium, NMDA and calcium spikes. The generation of these events requires distinct spatio-temporal activation patterns of synaptic inputs. We hypothesise that the recent findings on dendritic spikes and local synaptic plasticity rules suggest clustering of common inputs along a subregion of a dendritic branch. These clusters may enable dendrites to separately threshold groups of functionally similar inputs, thus allowing single neurons to act as a superposition of many separate integrate and fire units. Ultimately, these properties expand our understanding about the computational power of neuronal networks.     

Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: Computational study

Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input–output data. Results show that the non-parametric models accurately and efficiently replicate the input–output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP.     

Mechanisms of experience-dependent plasticity in the auditory localization pathway of the barn owl

Sound localization is a computational process that requires the central nervous system to measure various auditory cues and then associate particular cue values with appropriate locations in space. Behavioral experiments show that barn owls learn to associate values of cues with locations in space based on experience. The capacity for experience-driven changes in sound localization behavior is particularly great during a sensitive period that lasts until the approach of adulthood. Neurophysiological techniques have been used to determine underlying sites of plasticity in the auditory space-processing pathway. The external nucleus of the inferior colliculus (ICX), where a map of auditory space is synthesized, is a major site of plasticity. Experience during the sensitive period can cause large-scale, adaptive changes in the tuning of ICX neurons for sound localization cues. Large-scale physiological changes are accompanied by anatomical remodeling of afferent axons to the ICX. Changes in the tuning of ICX neurons for cue values involve two stages: (1) the instructed acquisition of neuronal responses to novel cue values and (2) the elimination of responses to inappropriate cue values. Newly acquired neuronal responses depend differentially on NMDA receptor currents for their expression. A model is presented that can account for this adaptive plasticity in terms of plausible cellular mechanisms. Accepted: 17 April 1999     

Homeostatic plasticity and excitation-inhibition balance: The good,the bad,and the ugly

In this review, we discuss the significance of the synaptic excitation/inhibition (E/I) balance in the context of homeostatic plasticity, whose primary goal is thought to maintain neuronal firing rates at a set point. We first provide an overview of the processes through which patterned input activity drives synaptic E/I tuning and maturation of circuits during development. Next, we emphasize the importance of the E/I balance at the synaptic level (homeostatic control of message reception) as a means to achieve the goal (homeostatic control of information transmission) at the network level and consider how compromised homeostatic plasticity associated with neurological diseases leads to hyperactivity, network instability, and ultimately improper information processing. Lastly, we highlight several pathological conditions related to sensory deafferentation and describe how, in some cases, homeostatic compensation without appropriate sensory inputs can result in phantom perceptions.     

Short-term plasticity at primary afferent synapse in rat spinal dorsal horn and its biological function

Short-term plasticity (STP) is an important element of information processing in neuronal networks. As the first synaptic relay between primary afferent fibers (PAFs) and central neurons, primary afferent synapses in spinal dorsal horn (DH) are essential to the initial processing of somatosensory information. In this research, we examined the STP between Adelta-PAFs and spinal DH neurons by patch-clamp recording. Our results showed that depression dominated the STP at primary afferent synapses. The curves of STP had no significant changes in the presence of bicuculline, CTZ or AP-5. Lowering extracellular Ca(2+) concentration ([Ca(2+)](o)) from 2.4 to 0.8 mM reduced the depression of synaptic responses at all stimulus rates, while raising [Ca(2+)](o) from 2.4 to 4.0 mM increased the synaptic depression. Increasing the bath temperature from 24 to 32 degrees C clearly reduced the depression of all responses. These results indicate that the observed STP is of presynaptic origin and depends on transmitter release. By fitting the experimental data recorded under different conditions, a model of STP was used to quantitatively characterize the observed STP and to analyze the possible mechanisms underlying the effects of [Ca(2+)](o) and temperature. Furthermore, using a model neuron receiving synaptic inputs, we found that with this form of STP, postsynaptic DH neurons could detect rate changes in both rapidly- and slowly-firing afferents with equal sensitivity. The present study links the intrinsic STP properties of primary afferent synapses with their role in processing neural information, and provides a basis for further research on the STP in spinal DH and its biological function under in vivo conditions.     

Fine discrimination training alters the causal contribution of macaque area MT to depth perception

When a new perceptual task is learned, plasticity occurs in the brain to mediate improvements in performance with training. How do these changes affect the neural substrates of previously learned tasks? We addressed this question by examining the effect of fine discrimination training on the causal contribution of area MT to coarse depth discrimination. When monkeys are trained to discriminate between two coarse absolute disparities (near versus far) embedded in noise, reversible inactivation of area MT devastates performance. In contrast, after animals are trained to discriminate fine differences in relative disparity, MT inactivation no longer impairs coarse depth discrimination. This effect does not result from changes in the disparity tuning of MT neurons, suggesting plasticity in the flow of disparity signals to decision circuitry. These findings show that the contribution of particular brain area to task performance can change dramatically as a result of learning new tasks.     

Spike-timing-dependent plasticity: common themes and divergent vistas

Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of these experiments lies in the observation that synapses activated shortly after the occurrence of a postsynaptic spike are weakened. Thus, synaptic plasticity is sensitive to the temporal ordering of pre- and postsynaptic activation. This temporal asymmetry has been suggested to underlie a range of learning tasks. In the first part of this review we highlight some of the common themes from a range of findings in the framework of predictive coding. As an example of how this principle can be used in a learning task, we discuss a recent model of cortical map formation. In the second part of the review, we point out some of the differences in STDP models and their functional consequences. We discuss how differences in the weight-dependence, the time-constants and the non-linear properties of learning rules give rise to distinct computational functions. In light of these computational issues raised, we review current experimental findings and suggest further experiments to resolve some controversies.     

Excitatory and feed-forward inhibitory hippocampal synapses work synergistically as an adaptive filter of natural spike trains

Short-term synaptic plasticity (STP) is an important mechanism for modifying neural circuits during computation. Although STP is much studied, its role in the processing of complex natural spike patterns is unknown. Here we analyze the responses of excitatory and inhibitory hippocampal synapses to natural spike trains at near-physiological temperatures. Our results show that excitatory and inhibitory synapses express complementary sets of STP components that selectively change synaptic strength during epochs of high-frequency discharge associated with hippocampal place fields. In both types of synapses, synaptic strength rapidly alternates between a near-constant level during low activity and another near-constant, but elevated (for excitatory synapses) or reduced (for inhibitory synapses) level during high-frequency epochs. These history-dependent changes in synaptic strength are largely independent of the particular temporal pattern within the discharges, and occur concomitantly in the two types of synapses. When excitatory and feed-forward inhibitory synapses are co-activated within the hippocampal feed-forward circuit unit, the net effect of their complementary STP is an additional increase in the gain of excitatory synapses during high-frequency discharges via selective disinhibition. Thus, excitatory and feed-forward inhibitory hippocampal synapses in vitro act synergistically as an adaptive filter that operates in a switch-like manner and is selective for high-frequency epochs.     

Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.     

Impaired associative learning in schizophrenia: behavioral and computational studies

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto–hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia.     

A computational model of cellular mechanisms of temporal coding in the medial geniculate body (MGB)

Acoustic stimuli are often represented in the early auditory pathway as patterns of neural activity synchronized to time-varying features. This phase-locking predominates until the level of the medial geniculate body (MGB), where previous studies have identified two main, largely segregated response types: Stimulus-synchronized responses faithfully preserve the temporal coding from its afferent inputs, and Non-synchronized responses, which are not phase locked to the inputs, represent changes in temporal modulation by a rate code. The cellular mechanisms underlying this transformation from phase-locked to rate code are not well understood. We use a computational model of a MGB thalamocortical neuron to test the hypothesis that these response classes arise from inferior colliculus (IC) excitatory afferents with divergent properties similar to those observed in brain slice studies. Large-conductance inputs exhibiting synaptic depression preserved input synchrony as short as 12.5 ms interclick intervals, while maintaining low firing rates and low-pass filtering responses. By contrast, small-conductance inputs with Mixed plasticity (depression of AMPA-receptor component and facilitation of NMDA-receptor component) desynchronized afferent inputs, generated a click-rate dependent increase in firing rate, and high-pass filtered the inputs. Synaptic inputs with facilitation often permitted band-pass synchrony along with band-pass rate tuning. These responses could be tuned by changes in membrane potential, strength of the NMDA component, and characteristics of synaptic plasticity. These results demonstrate how the same synchronized input spike trains from the inferior colliculus can be transformed into different representations of temporal modulation by divergent synaptic properties.     

Robustness of neuronal tuning to binaural sound localization cues against age-related loss of inhibitory synaptic inputs

Sound localization relies on minute differences in the timing and intensity of sound arriving at both ears. Neurons of the lateral superior olive (LSO) in the brainstem process these interaural disparities by precisely detecting excitatory and inhibitory synaptic inputs. Aging generally induces selective loss of inhibitory synaptic transmission along the entire auditory pathways, including the reduction of inhibitory afferents to LSO. Electrophysiological recordings in animals, however, reported only minor functional changes in aged LSO. The perplexing discrepancy between anatomical and physiological observations suggests a role for activity-dependent plasticity that would help neurons retain their binaural tuning function despite loss of inhibitory inputs. To explore this hypothesis, we use a computational model of LSO to investigate mechanisms underlying the observed functional robustness against age-related loss of inhibitory inputs. The LSO model is an integrate-and-fire type enhanced with a small amount of low-voltage activated potassium conductance and driven with (in)homogeneous Poissonian inputs. Without synaptic input loss, model spike rates varied smoothly with interaural time and level differences, replicating empirical tuning properties of LSO. By reducing the number of inhibitory afferents to mimic age-related loss of inhibition, overall spike rates increased, which negatively impacted binaural tuning performance, measured as modulation depth and neuronal discriminability. To simulate a recovery process compensating for the loss of inhibitory fibers, the strength of remaining inhibitory inputs was increased. By this modification, effects of inhibition loss on binaural tuning were considerably weakened, leading to an improvement of functional performance. These neuron-level observations were further confirmed by population modeling, in which binaural tuning properties of multiple LSO neurons were varied according to empirical measurements. These results demonstrate the plausibility that homeostatic plasticity could effectively counteract known age-dependent loss of inhibitory fibers in LSO and suggest that behavioral degradation of sound localization might originate from changes occurring more centrally.     

Response properties of the auditory telencephalon in songbirds change with recent experience and season

The caudomedial nidopallium (NCM) is a telencephalic auditory area that is selectively activated by conspecific vocalizations in zebra finches and canaries. We recently demonstrated that temporal and spectral dynamics of auditory tuning in NCM differ between these species [1]. In order to determine whether these differences reflect recent experience, we exposed separate groups of each species and sex to different housing conditions. Adult birds were housed either in an aviary with conspecifics (NORM), with heterospecifics (canary subjects in a zebra finch aviary, and vice versa: (CROSS)), or in isolation (ISO) for 9 days prior to testing. We then recorded extracellular multi-unit electrophysiological responses to simple pure tone stimuli (250-5000 Hz) in awake birds from each group and analyzed auditory tuning width using methods from our earlier studies. Relative to NORM birds, tuning was narrower in CROSS birds, and wider in ISO birds. The trend was greater in canaries, especially females. The date of recording was also included as a covariate in ANCOVAs that analyzed a larger set of the canary data, including data from birds tested outside of the breeding season, and treated housing condition and sex as independent variables. These tests show that tuning width was narrower early in the year and broader later. This effect was most pronounced in CROSS males. The degree of the short-term neural plasticity described here differs across sexes and species, and may reflect differences in NCM's anatomical and functional organization related to species differences in song characteristics, adult plasticity and/or social factors. More generally, NCM tuning is labile and may be modulated by recent experience to reflect the auditory processing required for behavioral adaptation to the current acoustic, social or seasonal context.     

生态环境承载力评价研究——以河南省为例

测度人类活动是否处于生态环境的承载力范围之内是生态环境承载力评价的核心内容,是区域国土开发科学决策规划的需要,但是生态环境可承载能力的阈值和人类活动对生态环境施加压力的大小均是难以定量计算的,为了解决生态环境承载力评价面临的这一难题,本文研究提出了一种生态环境承载力评价的新方法,即"生态环境质量指数动态评价法"。该方法依据生态平衡理论和生态稳定性原理,将承载力评价转化为运行状态评价,以"生态环境系统是否处于良性运行状态"作为生态环境是否处于可承载状态的评价标准;首先应用生态环境质量指数(EI)模型计算出评价区多年的EI值,然后再依据EI的动态变化趋势对"生态环境系统的运行状态"进行评定,以达到对区域生态环境承载力进行评价之目的,以河南省为例,介绍了评价的方法与过程。结果表明:(1)生态环境质量现状评价不是承载力评价,但是可通过对生态环境质量的动态变化趋势进行回归分析以达到承载力评价之目的;(2)该方法紧扣生态环境承载力的内涵,避开了生态环境系统承载力阈值测算的难点,方法简明易行,结果意义明确、实用性强,为承载力评价提供了一种新思路和新方法;(3)生态环境系统质量现状不能反映其承载状态,信阳市的生态环境质量在河南是最好的,目前却处于超载、退化状态,而濮阳和安阳市的情况则与信阳市正好相反,应引起政府决策部门的重视。     

Biophysical characterization of the enzyme I of the Streptomyces coelicolor phosphoenolpyruvate:sugar phosphotransferase system

The first protein in the bacterial phosphoenolpyruvate (PEP):sugar phosphotransferase system is the homodimeric 60-kDa enzyme I (EI), which autophosphorylates in the presence of PEP and Mg2+. The conformational stability and structure of the EI from Streptomyces coelicolor, EI(sc), were explored in the absence and in the presence of its effectors by using several biophysical probes (namely, fluorescence, far-ultraviolet circular dichroism, Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry) and computational approaches. The structure of EI(sc) was obtained by homology modeling of the isolated N- and C-terminal domains of other EI proteins. The experimental results indicate that at physiological pH, the dimeric EI(sc) had a well-folded structure; however, at low pH, EI(sc) showed a partially unfolded state with the features of a molten globule, as suggested by fluorescence, far-ultraviolet circular dichroism, FTIR, and 8-anilino-1-naphthalene-sulfonic acid binding. The thermal stability of EI(sc), in the absence of PEP and Mg2+, was maximal at pH 7. The presence of PEP and Mg2+ did not change substantially the secondary structure of the protein, as indicated by FTIR measurements. However, quenching experiments and proteolysis patterns suggest conformational changes in the presence of PEP; furthermore, the thermal stability of EI(sc) was modified depending on the effector added. Our approach suggests that thermodynamical analysis might reveal subtle conformational changes.