Synaptic depression and cortical gain control

A General Procedure to Study Subcellular Models of Transsynaptic Signaling at Inhibitory Synapses

Computational modeling of brain circuits requires the definition of many parameters that are difficult to determine from experimental findings. One way to help interpret these data is to fit them using a particular kinetic model. In this paper, we propose a general procedure to fit individual synaptic events recorded from voltage clamp experiments. Starting from any given model description (mod file) in the NEURON simulation environment, the procedure exploits user-defined constraints, dependencies, and rules for the parameters of the model to fit the time course of individual spontaneous synaptic events that are recorded experimentally. The procedure, implemented in NEURON, is currently available in ModelDB. A Python version is installed, and will be soon available for public use, as a standalone task in the Collaboratory Portal of the Human Brain Project. To illustrate the potential application of the procedure, we tested its use with various sets of experimental data on GABAergic synapses; gephyrin and gephyrin-dependent pathways were chosen as a suitable example of a kinetic model of synaptic transmission. For individual spontaneous inhibitory events in hippocampal pyramidal CA1 neurons, we found that gephyrin-dependent subcellular pathways may shape synaptic events at different levels, and can be correlated with cell- or event-specific activity history and/or pathological conditions.

Goji fruit (Lycium barbarum) protects sciatic nerve function against crush injury in a model of diabetic stress

Excess fructose consumption causes changes in functioning of the central and peripheral nervous systems, which increase the vulnerability of peripheral nerves to traumatic injury. The aim of this study was to evaluate the electrophysiological parameters of responses of motoneurons of the spinal cord at high-frequency stimulation of the distal part of the injured sciatic nerve in a model of diabetic stress under action of Lycium barbarum (LB). Male albino rats were given with drinking water with 50% concentration of dietary fructose for 6 weeks. Starting on the 7th week a crush injury of the left sciatic nerve was carried out. Some of the animals received fructose post-injury for 3 weeks and some of the animals received fructose+dry LB fruits for 3 weeks. In the fructose+crush+LВ group a relatively proportional division of tetanic and posttetanic potentiation and depression in responses of ipsilateral and contralateral motoneurons was observed, which would suggest the modulatory role of LB in short-term synaptic plasticity formation. Generally, LB fruit is able to modulate central nervous system reorganization, amplifying positive adaptive changes that improve functional recovery and promote selective target reinnervation in high fructose-diet rats with sciatic nerve crush-injury.

Superpriming of synaptic vesicles as a common basis for intersynapse variability and modulation of synaptic strength

Glutamatergic synapses show large variations in strength and short-term plasticity (STP). We show here that synapses displaying an increased strength either after posttetanic potentiation (PTP) or through activation of the phospholipase-C-diacylglycerol pathway share characteristic properties with intrinsically strong synapses, such as (i) pronounced short-term depression (STD) during high-frequency stimulation; (ii) a conversion of that STD into a sequence of facilitation followed by STD after a few conditioning stimuli at low frequency; (iii) an equalizing effect of such conditioning stimulation, which reduces differences among synapses and abolishes potentiation; and (iv) a requirement of long periods of rest for reconstitution of the original STP pattern. These phenomena are quantitatively described by assuming that a small fraction of "superprimed" synaptic vesicles are in a state of elevated release probability (p ∼ 0.5). This fraction is variable in size among synapses (typically about 30%), but increases after application of phorbol ester or during PTP. The majority of vesicles, released during repetitive stimulation, have low release probability (p ∼ 0.1), are relatively uniform in number across synapses, and are rapidly recruited. In contrast, superprimed vesicles need several seconds to be regenerated. They mediate enhanced synaptic strength at the onset of burst-like activity, the impact of which is subject to modulation by slow modulatory transmitter systems.

LKB1 Regulates Mitochondria-Dependent Presynaptic Calcium Clearance and Neurotransmitter Release Properties at Excitatory Synapses along Cortical Axons

Individual synapses vary significantly in their neurotransmitter release properties, which underlie complex information processing in neural circuits. Presynaptic Ca2+ homeostasis plays a critical role in specifying neurotransmitter release properties, but the mechanisms regulating synapse-specific Ca2+ homeostasis in the mammalian brain are still poorly understood. Using electrophysiology and genetically encoded Ca2+ sensors targeted to the mitochondrial matrix or to presynaptic boutons of cortical pyramidal neurons, we demonstrate that the presence or absence of mitochondria at presynaptic boutons dictates neurotransmitter release properties through Mitochondrial Calcium Uniporter (MCU)-dependent Ca2+ clearance. We demonstrate that the serine/threonine kinase LKB1 regulates MCU expression, mitochondria-dependent Ca2+ clearance, and thereby, presynaptic release properties. Re-establishment of MCU-dependent mitochondrial Ca2+ uptake at glutamatergic synapses rescues the altered neurotransmitter release properties characterizing LKB1-null cortical axons. Our results provide novel insights into the cellular and molecular mechanisms whereby mitochondria control neurotransmitter release properties in a bouton-specific way through presynaptic Ca2+ clearance.

Anisotropic Black Phosphorus Synaptic Device for Neuromorphic Applications

The first black-phosphorus synaptic device is demonstrated, which offers intrinsic anisotropy in its synaptic characteristics directly resulting from its low crystalline symmetry. Key features of biological synapses, such as long-term plasticity with heterogeneity, including long-term potentiation/depression and spike-timing-dependent plasticity, are mimicked. This demonstration represents an important step toward introducing intrinsic heterogeneity to artificial neuromorphic systems.

Competitive Dynamics in MSTd: A Mechanism for Robust Heading Perception Based on Optic Flow

Human heading perception based on optic flow is not only accurate, it is also remarkably robust and stable. These qualities are especially apparent when observers move through environments containing other moving objects, which introduce optic flow that is inconsistent with observer self-motion and therefore uninformative about heading direction. Moving objects may also occupy large portions of the visual field and occlude regions of the background optic flow that are most informative about heading perception. The fact that heading perception is biased by no more than a few degrees under such conditions attests to the robustness of the visual system and warrants further investigation. The aim of the present study was to investigate whether recurrent, competitive dynamics among MSTd neurons that serve to reduce uncertainty about heading over time offer a plausible mechanism for capturing the robustness of human heading perception. Simulations of existing heading models that do not contain competitive dynamics yield heading estimates that are far more erratic and unstable than human judgments. We present a dynamical model of primate visual areas V1, MT, and MSTd based on that of Layton, Mingolla, and Browning that is similar to the other models, except that the model includes recurrent interactions among model MSTd neurons. Competitive dynamics stabilize the model's heading estimate over time, even when a moving object crosses the future path. Soft winner-take-all dynamics enhance units that code a heading direction consistent with the time history and suppress responses to transient changes to the optic flow field. Our findings support recurrent competitive temporal dynamics as a crucial mechanism underlying the robustness and stability of perception of heading.

Bio-inspired feedback-circuit implementation of discrete, free energy optimizing, winner-take-all computations

Bayesian inference and bounded rational decision-making require the accumulation of evidence or utility, respectively, to transform a prior belief or strategy into a posterior probability distribution over hypotheses or actions. Crucially, this process cannot be simply realized by independent integrators, since the different hypotheses and actions also compete with each other. In continuous time, this competitive integration process can be described by a special case of the replicator equation. Here we investigate simple analog electric circuits that implement the underlying differential equation under the constraint that we only permit a limited set of building blocks that we regard as biologically interpretable, such as capacitors, resistors, voltage-dependent conductances and voltage- or current-controlled current and voltage sources. The appeal of these circuits is that they intrinsically perform normalization without requiring an explicit divisive normalization. However, even in idealized simulations, we find that these circuits are very sensitive to internal noise as they accumulate error over time. We discuss in how far neural circuits could implement these operations that might provide a generic competitive principle underlying both perception and action.

How Does the Cerebral Cortex Work? Development, Learning, Attention, and 3-D Vision by Laminar Circuits of Visual Cortex

A key goal of behavioral and cognitive neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress toward explaining how the visual cortex sees. Visual cortex, like many parts of perceptual and cognitive neocortex, is organized into six main layers of cells, as well as characteristic sublamina. Here it is proposed how these layered circuits help to realize processes of development, learning, perceptual grouping, attention, and 3-D vision through a combination of bottom-up, horizontal, and top-down interactions. A main theme is that the mechanisms which enable development and learning to occur in a stable way imply properties of adult behavior. These results thus begin to unify three fields: infant cortical development, adult cortical neurophysiology and anatomy, and adult visual perception. The identified cortical mechanisms promise to generalize to explain how other perceptual and cognitive processes work.

Quantifying Repetitive Transmission at Chemical Synapses: A Generative-Model Approach

The dependence of the synaptic responses on the history of activation and their large variability are both distinctive features of repetitive transmission at chemical synapses. Quantitative investigations have mostly focused on trial-averaged responses to characterize dynamic aspects of the transmission—thus disregarding variability—or on the fluctuations of the responses in steady conditions to characterize variability—thus disregarding dynamics. We present a statistically principled framework to quantify the dynamics of the probability distribution of synaptic responses under arbitrary patterns of activation. This is achieved by constructing a generative model of repetitive transmission, which includes an explicit description of the sources of stochasticity present in the process. The underlying parameters are then selected via an expectation-maximization algorithm that is exact for a large class of models of synaptic transmission, so as to maximize the likelihood of the observed responses. The method exploits the information contained in the correlation between responses to produce highly accurate estimates of both quantal and dynamic parameters from the same recordings. The method also provides important conceptual and technical advances over existing state-of-the-art techniques. In particular, the repetition of the same stimulation in identical conditions becomes unnecessary. This paves the way to the design of optimal protocols to estimate synaptic parameters, to the quantitative comparison of synaptic models over benchmark datasets, and, most importantly, to the study of repetitive transmission under physiologically relevant patterns of synaptic activation.

Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1

Neurons in the primary visual cortex are known for responding vigorously but with high variability to classical stimuli such as drifting bars or gratings. By contrast, natural scenes are encoded more efficiently by sparse and temporal precise spiking responses. We used a conductance-based model of the visual system in higher mammals to investigate how two specific features of the thalamo-cortical pathway, namely push-pull receptive field organization and fast synaptic depression, can contribute to this contextual reshaping of V1 responses. By comparing cortical dynamics evoked respectively by natural vs. artificial stimuli in a comprehensive parametric space analysis, we demonstrate that the reliability and sparseness of the spiking responses during natural vision is not a mere consequence of the increased bandwidth in the sensory input spectrum. Rather, it results from the combined impacts of fast synaptic depression and push-pull inhibition, the later acting for natural scenes as a form of “effective” feed-forward inhibition as demonstrated in other sensory systems. Thus, the combination of feedforward-like inhibition with fast thalamo-cortical synaptic depression by simple cells receiving a direct structured input from thalamus composes a generic computational mechanism for generating a sparse and reliable encoding of natural sensory events.

Fast cerebellar reflex circuitry requires synaptic vesicle priming by munc13-3

Munc13-3 is a member of the Munc13 family of synaptic vesicle priming proteins and mainly expressed in cerebellar neurons. Munc13-3 null mutant (Munc13-3^−/−) mice show decreased synaptic release probability at parallel fiber to Purkinje cell, granule cell to Golgi cell, and granule cell to basket cell synapses and exhibit a motor learning deficit at highest rotarod speeds. Since we detected Munc13-3 immunoreactivity in the dentate gyrus, as reported here for the first time, and current studies indicated a crucial role for the cerebellum in hippocampus-dependent spatial memory, we systematically investigated Munc13-3^−/− mice versus wild-type littermates of both genders with respect to hippocampus-related cognition and a range of basic behaviors, including tests for anxiety, sensory functions, motor performance and balance, sensorimotor gating, social interaction and competence, and repetitive and compulsive behaviors. Neither basic behavior nor hippocampus-dependent cognitive performance, evaluated by Morris water maze, hole board working and reference memory, IntelliCage-based place learning including multiple reversals, and fear conditioning, showed any difference between genotypes. However, consistent with a disturbed cerebellar reflex circuitry, a reliable reduction in the acoustic startle response in both male and female Munc13-3^−/− mice was found. To conclude, complete deletion of Munc13-3 leads to a robust decrease in the acoustic startle response. This readout of a fast cerebellar reflex circuitry obviously requires synaptic vesicle priming by Munc13-3 for full functionality, in contrast to other behavioral or cognitive features, where a nearly perfect compensation of Munc13-3 deficiency by related synaptic proteins has to be assumed.

Fast response and high sensitivity to microsaccades in a cascading-adaptation neural network with short-term synaptic depression

Microsaccades are very small eye movements during fixation. Experimentally, they have been found to play an important role in visual information processing. However, neural responses induced by microsaccades are not yet well understood and are rarely studied theoretically. Here we propose a network model with a cascading adaptation including both retinal adaptation and short-term depression (STD) at thalamocortical synapses. In the neural network model, we compare the microsaccade-induced neural responses in the presence of STD and those without STD. It is found that the cascading with STD can give rise to faster and sharper responses to microsaccades. Moreover, STD can enhance response effectiveness and sensitivity to microsaccadic spatiotemporal changes, suggesting improved detection of small eye movements (or moving visual objects). We also explore the mechanism of the response properties in the model. Our studies strongly indicate that STD plays an important role in neural responses to microsaccades. Our model considers simultaneously retinal adaptation and STD at thalamocortical synapses in the study of microsaccade-induced neural activity, and may be useful for further investigation of the functional roles of microsaccades in visual information processing.

Short-term synaptic plasticity across topographic maps in the electrosensory system

The early pathways underlying the active electric sense of the weakly electric fish Apteronotus leptorhynchus involve three parallel processing streams. An array of tuberous electroreceptors distributed over the skin provides inputs to the electrosensory lateral line lobe (ELL), forming the basis for three topographic maps: LS (lateral segment), CLS (centrolateral segment), and CMS (centromedial segment). In addition, each map receives topographically preserved inputs from a direct feedback pathway. How this feedback contributes to the distinct spatiotemporal filtering properties of ELL pyramidal neurons across maps is not clear. We used an in vitro approach to characterize short-term plasticity (STP) in the direct feedback synapses onto pyramidal neurons in each map. Our findings indicated that the dynamics of STP varied across maps in a manner that was consistent with the temporal filtering properties of pyramidal neurons in vivo. Using a modeling approach, we found that the STP of direct feedback synapses in CMS was best described by a simple facilitation-depression model. On the other hand, STP in LS was best described by synaptic facilitation with a use-dependent recovery rate. These results suggest that differential regulation of overlapping STP processes in feedback pathways can contribute to the functional specialization of topographic sensory maps.

Determining synaptic parameters using high-frequency activation

BACKGROUND

The specific properties of a synapse determine how neuronal activity evokes neurotransmitter release. Evaluating changes in synaptic properties during sustained activity is essential to understanding how genetic manipulations and neuromodulators regulate neurotransmitter release. Analyses of postsynaptic responses to high-frequency stimulation have provided estimates of the size of the readily-releasable pool (RRP) of vesicles (N0) and the probability of vesicular release (p) at multiple synapses.

NEW METHOD

Here, we introduce a model-based approach at the calyx of Held synapse in which depletion and the rate of replenishment (R) determine the number of available vesicles, and facilitation leads to a use-dependent increase in p when initial p is low.

RESULTS

When p is high and R is low, we find excellent agreement between estimates based on all three methods and the model. However, when p is low or when significant replenishment occurs between stimuli, estimates of different methods diverge, and model estimates are between the extreme estimates provided by the other approaches.

COMPARISON WITH OTHER METHODS

We compare our model-based approach to three other approaches that rely on different simplifying assumptions. Our findings suggest that our model provides a better estimate of N0 and p than previously-established methods, likely due to inaccurate assumptions about replenishment. More generally, our findings suggest that approaches commonly used to estimate N0 and p at other synapses are often applied under experimental conditions that yield inaccurate estimates.

CONCLUSIONS

Careful application of appropriate methods can greatly improve estimates of synaptic parameters.

SYNAPTIC DEPRESSION IN DEEP NEURAL NETWORKS FOR SPEECH PROCESSING

A characteristic property of biological neurons is their ability to dynamically change the synaptic efficacy in response to variable input conditions. This mechanism, known as synaptic depression, significantly contributes to the formation of normalized representation of speech features. Synaptic depression also contributes to the robust performance of biological systems. In this paper, we describe how synaptic depression can be modeled and incorporated into deep neural network architectures to improve their generalization ability. We observed that when synaptic depression is added to the hidden layers of a neural network, it reduces the effect of changing background activity in the node activations. In addition, we show that when synaptic depression is included in a deep neural network trained for phoneme classification, the performance of the network improves under noisy conditions not included in the training phase. Our results suggest that more complete neuron models may further reduce the gap between the biological performance and artificial computing, resulting in networks that better generalize to novel signal conditions.

Model predictions of features in microsaccade-related neural responses in a feedforward network with short-term synaptic depression

Recently, the significant microsaccade-induced neural responses have been extensively observed in experiments. To explore the underlying mechanisms of the observed neural responses, a feedforward network model with short-term synaptic depression has been proposed [Yuan, W.-J., Dimigen, O., Sommer, W. and Zhou, C. Front. Comput. Neurosci. 7, 47 (2013)]. The depression model not only gave an explanation for microsaccades in counteracting visual fading, but also successfully reproduced several microsaccade-related features in experimental findings. These results strongly suggest that, the depression model is very useful to investigate microsaccade-related neural responses. In this paper, by using the model, we extensively study and predict the dependance of microsaccade-related neural responses on several key parameters, which could be tuned in experiments. Particularly, we provide a significant prediction that microsaccade-related neural response also complies with the property "sharper is better" observed in many contexts in neuroscience. Importantly, the property exhibits a power-law relationship between the width of input signal and the responsive effectiveness, which is robust against many parameters in the model. By using mean field theory, we analytically investigate the robust power-law property. Our predictions would give theoretical guidance for further experimental investigations of the functional role of microsaccades in visual information processing.

Resonant Cholinergic Dynamics in Cognitive and Motor Decision-Making: Attention, Category Learning, and Choice in Neocortex, Superior Colliculus, and Optic Tectum

Freely behaving organisms need to rapidly calibrate their perceptual, cognitive, and motor decisions based on continuously changing environmental conditions. These plastic changes include sharpening or broadening of cognitive and motor attention and learning to match the behavioral demands that are imposed by changing environmental statistics. This article proposes that a shared circuit design for such flexible decision-making is used in specific cognitive and motor circuits, and that both types of circuits use acetylcholine to modulate choice selectivity. Such task-sensitive control is proposed to control thalamocortical choice of the critical features that are cognitively attended and that are incorporated through learning into prototypes of visual recognition categories. A cholinergically-modulated process of vigilance control determines if a recognition category and its attended features are abstract (low vigilance) or concrete (high vigilance). Homologous neural mechanisms of cholinergic modulation are proposed to focus attention and learn a multimodal map within the deeper layers of superior colliculus. This map enables visual, auditory, and planned movement commands to compete for attention, leading to selection of a winning position that controls where the next saccadic eye movement will go. Such map learning may be viewed as a kind of attentive motor category learning. The article hereby explicates a link between attention, learning, and cholinergic modulation during decision making within both cognitive and motor systems. Homologs between the mammalian superior colliculus and the avian optic tectum lead to predictions about how multimodal map learning may occur in the mammalian and avian brain and how such learning may be modulated by acetycholine.

Interplay between Subthreshold Oscillations and Depressing Synapses in Single Neurons

In this paper we analyze the interplay between the subthreshold oscillations of a single neuron conductance-based model and the short-term plasticity of a dynamic synapse with a depressing mechanism. In previous research, the computational properties of subthreshold oscillations and dynamic synapses have been studied separately. Our results show that dynamic synapses can influence different aspects of the dynamics of neuronal subthreshold oscillations. Factors such as maximum hyperpolarization level, oscillation amplitude and frequency or the resulting firing threshold are modulated by synaptic depression, which can even make subthreshold oscillations disappear. This influence reshapes the postsynaptic neuron’s resonant properties arising from subthreshold oscillations and leads to specific input/output relations. We also study the neuron’s response to another simultaneous input in the context of this modulation, and show a distinct contextual processing as a function of the depression, in particular for detection of signals through weak synapses. Intrinsic oscillations dynamics can be combined with the characteristic time scale of the modulatory input received by a dynamic synapse to build cost-effective cell/channel-specific information discrimination mechanisms, beyond simple resonances. In this regard, we discuss the functional implications of synaptic depression modulation on intrinsic subthreshold dynamics.

Effects of dynamic synapses on noise-delayed response latency of a single neuron

The noise-delayed decay (NDD) phenomenon emerges when the first-spike latency of a periodically forced stochastic neuron exhibits a maximum for a particular range of noise intensity. Here, we investigate the latency response dynamics of a single Hodgkin-Huxley neuron that is subject to both a suprathreshold periodic stimulus and a background activity arriving through dynamic synapses. We study the first-spike latency response as a function of the presynaptic firing rate f. This constitutes a more realistic scenario than previous works, since f provides a suitable biophysically realistic parameter to control the level of activity in actual neural systems. We first report on the emergence of classical NDD behavior as a function of f for the limit of static synapses. Second, we show that when short-term depression and facilitation mechanisms are included at the synapses, different NDD features can be found due to their modulatory effect on synaptic current fluctuations. For example, an intriguing double NDD (DNDD) behavior occurs for different sets of relevant synaptic parameters. Moreover, depending on the balance between synaptic depression and synaptic facilitation, single NDD or DNDD can prevail, in such a way that synaptic facilitation favors the emergence of DNDD whereas synaptic depression favors the existence of single NDD. Here we report the existence of the DNDD effect in the response latency dynamics of a neuron.

Neuromorphic Functions in PEDOT:PSS Organic Electrochemical Transistors

Depressive short-term synaptic plasticity functions are implemented with a simple polymer poly(3,4ethylenedioxythiophene):poly(styrene sulfonate) (

PEDOT

PSS) organic electrochemical transistor device. These functions are a first step toward the realization of organic-based neuroinspired platforms with spatiotemporal information processing capabilities.

Synaptic plasticity as a cortical coding scheme

Processing of auditory information requires constant adjustment due to alterations of the environment and changing conditions in the nervous system with age, health, and experience. Consequently, patterns of activity in cortical networks have complex dynamics over a wide range of timescales, from milliseconds to days and longer. In the primary auditory cortex (AI), multiple forms of adaptation and plasticity shape synaptic input and action potential output. However, the variance of neuronal responses has made it difficult to characterize AI receptive fields and to determine the function of AI in processing auditory information such as vocalizations. Here we describe recent studies on the temporal modulation of cortical responses and consider the relation of synaptic plasticity to neural coding.

Visual Adaptation

Sensory systems continuously mold themselves to the widely varying contexts in which they must operate. Studies of these adaptations have played a long and central role in vision science. In part this is because the specific adaptations remain a powerful tool for dissecting vision, by exposing the mechanisms that are adapting. That is, "if it adapts, it's there." Many insights about vision have come from using adaptation in this way, as a method. A second important trend has been the realization that the processes of adaptation are themselves essential to how vision works, and thus are likely to operate at all levels. That is, "if it's there, it adapts." This has focused interest on the mechanisms of adaptation as the target rather than the probe. Together both approaches have led to an emerging insight of adaptation as a fundamental and ubiquitous coding strategy impacting all aspects of how we see.

Distinct recurrent versus afferent dynamics in cortical visual processing

How intracortical recurrent circuits in mammalian sensory cortex influence dynamics of sensory representation is not understood. Previous methods could not distinguish the relative contributions of recurrent circuits and thalamic afferents to cortical dynamics. We accomplish this by optogenetically manipulating thalamus and cortex. Over the initial 40 ms of visual stimulation, excitation from recurrent circuits in visual cortex progressively increased to exceed direct thalamocortical excitation. Even when recurrent excitation exceeded thalamic excitation, upon silencing thalamus, sensory-evoked activity in cortex decayed rapidly, with a time constant of 10 ms, which is similar to a neuron's integration time window. In awake mice, this cortical decay function predicted the time-locking of cortical activity to thalamic input at frequencies <15 Hz and attenuation of the cortical response to higher frequencies. Under anesthesia, depression at thalamocortical synapses disrupted the fidelity of sensory transmission. Thus, we determine dynamics intrinsic to cortical recurrent circuits that transform afferent input in time.

Single Canonical Model of Reflexive Memory and Spatial Attention

Many neurons in the dorsal and ventral visual stream have the property that after a brief visual stimulus presentation in their receptive field, the spiking activity in these neurons persists above their baseline levels for several seconds. This maintained activity is not always correlated with the monkey’s task and its origin is unknown. We have previously proposed a simple neural network model, based on shape selective neurons in monkey lateral intraparietal cortex, which predicts the valence and time course of reflexive (bottom-up) spatial attention. In the same simple model, we demonstrate here that passive maintained activity or short-term memory of specific visual events can result without need for an external or top-down modulatory signal. Mutual inhibition and neuronal adaptation play distinct roles in reflexive attention and memory. This modest 4-cell model provides the first simple and unified physiologically plausible mechanism of reflexive spatial attention and passive short-term memory processes.

Factors Influencing Short-term Synaptic Plasticity in the Avian Cochlear Nucleus Magnocellularis

Defined as reduced neural responses during high rates of activity, synaptic depression is a form of short-term plasticity important for the temporal filtering of sound. In the avian cochlear nucleus magnocellularis (NM), an auditory brainstem structure, mechanisms regulating short-term synaptic depression include pre-, post-, and extrasynaptic factors. Using varied paired-pulse stimulus intervals, we found that the time course of synaptic depression lasts up to four seconds at late-developing NM synapses. Synaptic depression was largely reliant on exogenous Ca²⁺-dependent probability of presynaptic neurotransmitter release, and to a lesser extent, on the desensitization of postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid-type glutamate receptor (AMPA-R). Interestingly, although extrasynaptic glutamate clearance did not play a significant role in regulating synaptic depression, blocking glutamate clearance at early-developing synapses altered synaptic dynamics, changing responses from depression to facilitation. These results suggest a developmental shift in the relative reliance on pre-, post-, and extrasynaptic factors in regulating short-term synaptic plasticity in NM.

Long-range recruitment of Martinotti cells causes surround suppression and promotes saliency in an attractor network model

Although the importance of long-range connections for cortical information processing has been acknowledged for a long time, most studies focused on the long-range interactions between excitatory cortical neurons. Inhibitory interneurons play an important role in cortical computation and have thus far been studied mainly with respect to their local synaptic interactions within the cortical microcircuitry. A recent study showed that long-range excitatory connections onto Martinotti cells (MC) mediate surround suppression. Here we have extended our previously reported attractor network of pyramidal cells (PC) and MC by introducing long-range connections targeting MC. We have demonstrated how the network with Martinotti cell-mediated long-range inhibition gives rise to surround suppression and also promotes saliency of locations at which simple non-uniformities in the stimulus field are introduced. Furthermore, our analysis suggests that the presynaptic dynamics of MC is only ancillary to its orientation tuning property in enabling the network with saliency detection. Lastly, we have also implemented a disinhibitory pathway mediated by another interneuron type (VIP interneurons), which inhibits MC and abolishes surround suppression.

An Adapting Auditory-motor Feedback Loop Can Contribute to Generating Vocal Repetition

Consecutive repetition of actions is common in behavioral sequences. Although integration of sensory feedback with internal motor programs is important for sequence generation, if and how feedback contributes to repetitive actions is poorly understood. Here we study how auditory feedback contributes to generating repetitive syllable sequences in songbirds. We propose that auditory signals provide positive feedback to ongoing motor commands, but this influence decays as feedback weakens from response adaptation during syllable repetitions. Computational models show that this mechanism explains repeat distributions observed in Bengalese finch song. We experimentally confirmed two predictions of this mechanism in Bengalese finches: removal of auditory feedback by deafening reduces syllable repetitions; and neural responses to auditory playback of repeated syllable sequences gradually adapt in sensory-motor nucleus HVC. Together, our results implicate a positive auditory-feedback loop with adaptation in generating repetitive vocalizations, and suggest sensory adaptation is important for feedback control of motor sequences.

Repetitions are common in animal vocalizations. Songs of many songbirds contain syllables that repeat a variable number of times, with non-Markovian distributions of repeat counts. The neural mechanism underlying such syllable repetitions is unknown. In this work, we show that auditory feedback plays an important role in sustaining syllable repetitions in the Bengalese finch. Deafening reduces syllable repetitions and skews the repeat number distribution towards short repeats. These effects are explained with our computational model, which suggests that syllable repeats are initially sustained by auditory feedback to the neural networks that drive the syllable production. The feedback strength weakens as the syllable repeats, increasing the likelihood that the syllable repetition stops. Neural recordings confirm such adaptation of auditory feedback to the auditory-motor circuit in the Bengalese finch. Our results suggests that sensory feedback can directly impact repetitions in motor sequences, and may provide insights into neural mechanisms of speech disorders such as stuttering.

Spike timing precision changes with spike rate adaptation in the owl's auditory space map

Spike rate adaptation (SRA) is a continuing change of responsiveness to ongoing stimuli, which is ubiquitous across species and levels of sensory systems. Under SRA, auditory responses to constant stimuli change over time, relaxing toward a long-term rate often over multiple timescales. With more variable stimuli, SRA causes the dependence of spike rate on sound pressure level to shift toward the mean level of recent stimulus history. A model based on subtractive adaptation (Benda J, Hennig RM. J Comput Neurosci 24: 113-136, 2008) shows that changes in spike rate and level dependence are mechanistically linked. Space-specific neurons in the barn owl's midbrain, when recorded under ketamine-diazepam anesthesia, showed these classical characteristics of SRA, while at the same time exhibiting changes in spike timing precision. Abrupt level increases of sinusoidally amplitude-modulated (SAM) noise initially led to spiking at higher rates with lower temporal precision. Spike rate and precision relaxed toward their long-term values with a time course similar to SRA, results that were also replicated by the subtractive model. Stimuli whose amplitude modulations (AMs) were not synchronous across carrier frequency evoked spikes in response to stimulus envelopes of a particular shape, characterized by the spectrotemporal receptive field (STRF). Again, abrupt stimulus level changes initially disrupted the temporal precision of spiking, which then relaxed along with SRA. We suggest that shifts in latency associated with stimulus level changes may differ between carrier frequency bands and underlie decreased spike precision. Thus SRA is manifest not simply as a change in spike rate but also as a change in the temporal precision of spiking.

Sensitivity of rat inferior colliculus neurons to frequency distributions

Stimulus-specific adaptation refers to a neural response reduction to a repeated stimulus that does not generalize to other stimuli. However, stimulus-specific adaptation appears to be influenced by additional factors. For example, the statistical distribution of tone frequencies has recently been shown to dynamically alter stimulus-specific adaptation in human auditory cortex. The present study investigated whether statistical stimulus distributions also affect stimulus-specific adaptation at an earlier stage of the auditory hierarchy. Neural spiking activity and local field potentials were recorded from inferior colliculus neurons of rats while tones were presented in oddball sequences that formed two different statistical contexts. Each sequence consisted of a repeatedly presented tone (standard) and three rare deviants of different magnitudes (small, moderate, large spectral change). The critical manipulation was the relative probability with which large spectral changes occurred. In one context the probability was high (relative to all deviants), while it was low in the other context. We observed larger responses for deviants compared with standards, confirming previous reports of increased response adaptation for frequently presented tones. Importantly, the statistical context in which tones were presented strongly modulated stimulus-specific adaptation. Physically and probabilistically identical stimuli (moderate deviants) in the two statistical contexts elicited different response magnitudes consistent with neural gain changes and thus neural sensitivity adjustments induced by the spectral range of a stimulus distribution. The data show that already at the level of the inferior colliculus stimulus-specific adaptation is dynamically altered by the statistical context in which stimuli occur.

Homeostatic role of heterosynaptic plasticity: models and experiments

Homosynaptic Hebbian-type plasticity provides a cellular mechanism of learning and refinement of connectivity during development in a variety of biological systems. In this review we argue that a complimentary form of plasticity-heterosynaptic plasticity-represents a necessary cellular component for homeostatic regulation of synaptic weights and neuronal activity. The required properties of a homeostatic mechanism which acutely constrains the runaway dynamics imposed by Hebbian associative plasticity have been well-articulated by theoretical and modeling studies. Such mechanism(s) should robustly support the stability of operation of neuronal networks and synaptic competition, include changes at non-active synapses, and operate on a similar time scale to Hebbian-type plasticity. The experimentally observed properties of heterosynaptic plasticity have introduced it as a strong candidate to fulfill this homeostatic role. Subsequent modeling studies which incorporate heterosynaptic plasticity into model neurons with Hebbian synapses (utilizing an STDP learning rule) have confirmed its ability to robustly provide stability and competition. In contrast, properties of homeostatic synaptic scaling, which is triggered by extreme and long lasting (hours and days) changes of neuronal activity, do not fit two crucial requirements for a hypothetical homeostatic mechanism needed to provide stability of operation in the face of on-going synaptic changes driven by Hebbian-type learning rules. Both the trigger and the time scale of homeostatic synaptic scaling are fundamentally different from those of the Hebbian-type plasticity. We conclude that heterosynaptic plasticity, which is triggered by the same episodes of strong postsynaptic activity and operates on the same time scale as Hebbian-type associative plasticity, is ideally suited to serve a homeostatic role during on-going synaptic plasticity.

Anatomy and physiology of the thick-tufted layer 5 pyramidal neuron

The thick-tufted layer 5 (TTL5) pyramidal neuron is one of the most extensively studied neuron types in the mammalian neocortex and has become a benchmark for understanding information processing in excitatory neurons. By virtue of having the widest local axonal and dendritic arborization, the TTL5 neuron encompasses various local neocortical neurons and thereby defines the dimensions of neocortical microcircuitry. The TTL5 neuron integrates input across all neocortical layers and is the principal output pathway funneling information flow to subcortical structures. Several studies over the past decades have investigated the anatomy, physiology, synaptology, and pathophysiology of the TTL5 neuron. This review summarizes key discoveries and identifies potential avenues of research to facilitate an integrated and unifying understanding on the role of a central neuron in the neocortex.

Fidelity of frequency and phase entrainment of circuit-level spike activity during DBS

High-frequency stimulation is known to entrain spike activity downstream and upstream of several clinical deep brain stimulation (DBS) targets, including the cerebellar-receiving area of thalamus (VPLo), subthalamic nucleus (STN), and globus pallidus (GP). Less understood are the fidelity of entrainment to each stimulus pulse, whether entrainment patterns are stationary over time, and how responses differ among DBS targets. In this study, three rhesus macaques were implanted with a single DBS lead in VPLo, STN, or GP. Single-unit spike activity was recorded in the resting state in motor cortex during VPLo DBS, in GP during STN DBS, and in STN and pallidal-receiving area of motor thalamus (VLo) during GP DBS. VPLo DBS induced time-locked spike activity in 25% (n = 15/61) of motor cortex cells, with entrained cells following 7.5 ± 7.4% of delivered pulses. STN DBS entrained spike activity in 26% (n = 8/27) of GP cells, which yielded time-locked spike activity for 8.7 ± 8.4% of stimulus pulses. GP DBS entrained 67% (n = 14/21) of STN cells and 32% (n = 19/59) of VLo cells, which showed a higher fraction of pulses effectively inhibiting spike activity (82.0 ± 9.6% and 86.1 ± 16.6%, respectively). Latency of phase-locked spike activity increased over time in motor cortex (58%, VPLo DBS) and to a lesser extent in GP (25%, STN DBS). In contrast, the initial inhibitory phase observed in VLo and STN during GP DBS remained stable following stimulation onset. Together, these data suggest that circuit-level entrainment is low-pass filtered during high-frequency stimulation, most notably for glutamatergic pathways. Moreover, phase entrainment is not stationary or consistent at the circuit level for all DBS targets.

Spatial specificity and inheritance of adaptation in human visual cortex

Adaptation at early stages of sensory processing can be propagated to downstream areas. Such inherited adaptation is a potential confound for functional magnetic resonance imaging (fMRI) techniques that use selectivity of adaptation to infer neuronal selectivity. However, the relative contributions of inherited and intrinsic adaptation at higher cortical stages, and the impact of inherited adaptation on downstream processing, remain unclear. Using fMRI, we investigated how adaptation to visual motion direction and orientation influences visually evoked responses in human V1 and extrastriate visual areas. To dissociate inherited from intrinsic adaptation, we quantified the spatial specificity of adaptation for each visual area as a measure of the receptive field sizes of the area where adaptation originated, predicting that adaptation originating in V1 should be more spatially specific than adaptation intrinsic to extrastriate visual cortex. In most extrastriate visual areas, the spatial specificity of adaptation did not differ from that in V1, suggesting that adaptation originated in V1. Only in one extrastriate area—MT—was the spatial specificity of direction-selective adaptation significantly broader than in V1, consistent with a combination of inherited V1 adaptation and intrinsic MT adaptation. Moreover, inherited adaptation effects could be both facilitatory and suppressive. These results suggest that adaptation at early visual processing stages can have widespread and profound effects on responses in extrastriate visual areas, placing important constraints on the use of fMRI adaptation techniques, while also demonstrating a general experimental strategy for systematically dissociating inherited from intrinsic adaptation by fMRI.

Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals.

Synaptic plasticity in the auditory system: a review

Synaptic transmission via chemical synapses is dynamic, i.e., the strength of postsynaptic responses may change considerably in response to repeated synaptic activation. Synaptic strength is increased during facilitation, augmentation and potentiation, whereas a decrease in synaptic strength is characteristic for depression and attenuation. This review attempts to discuss the literature on short-term and long-term synaptic plasticity in the auditory brainstem of mammals and birds. One hallmark of the auditory system, particularly the inner ear and lower brainstem stations, is information transfer through neurons that fire action potentials at very high frequency, thereby activating synapses >500 times per second. Some auditory synapses display morphological specializations of the presynaptic terminals, e.g., calyceal extensions, whereas other auditory synapses do not. The review focuses on short-term depression and short-term facilitation, i.e., plastic changes with durations in the millisecond range. Other types of short-term synaptic plasticity, e.g., posttetanic potentiation and depolarization-induced suppression of excitation, will be discussed much more briefly. The same holds true for subtypes of long-term plasticity, like prolonged depolarizations and spike-time-dependent plasticity. We also address forms of plasticity in the auditory brainstem that do not comprise synaptic plasticity in a strict sense, namely short-term suppression, paired tone facilitation, short-term adaptation, synaptic adaptation and neural adaptation. Finally, we perform a meta-analysis of 61 studies in which short-term depression (STD) in the auditory system is opposed to short-term depression at non-auditory synapses in order to compare high-frequency neurons with those that fire action potentials at a lower rate. This meta-analysis reveals considerably less STD in most auditory synapses than in non-auditory ones, enabling reliable, failure-free synaptic transmission even at frequencies >100 Hz. Surprisingly, the calyx of Held, arguably the best-investigated synapse in the central nervous system, depresses most robustly. It will be exciting to reveal the molecular mechanisms that set high-fidelity synapses apart from other synapses that function much less reliably.

Multidimensional gain control in image representation and processing in vision

A generic model of automatic gain control (AGC) is proposed as a general framework for multidimensional automatic contrast sensitivity adjustment in vision, as well as in other sensory modalities. We show that a generic feedback AGC mechanism, incorporating a nonlinear synaptic interaction into the feedback loop of a neural network, can enhance and emphasize important image attributes, such as curvature, size, depth, convexity/concavity and more, similar to its role in the adjustment of photoreceptors and retinal network sensitivity over the extremely high dynamic range of environmental light intensities, while enhancing the contrast. We further propose that visual illusions, well established by psychophysical experiments, are a by-product of the multidimensional AGC. This hypothesis is supported by simulations implementing AGC, which reproduce psychophysical data regarding size contrast effects known as the Ebbinghaus illusion, and depth contrast effects. Processing of curvature by an AGC network illustrates that it is an important mechanism of image structure pre-emphasis, which thereby enhances saliency. It is argued that the generic neural network of AGC constitutes a universal, parsimonious, unified mechanism of neurobiological automatic contrast sensitivity control. This mechanism/model can account for a wide range of physiological and psychophysical phenomena, such as visual illusions and contour completion, in cases of occlusion, by a basic neural network. Likewise, and as important, biologically motivated AGC provides attractive new means for the development of intelligent computer vision systems.

Effect of the initial synaptic state on the probability to induce long-term potentiation and depression

Long-term potentiation (LTP) and long-term depression (LTD) are the two major forms of long-lasting synaptic plasticity in the mammalian neurons, and are directly related to higher brain functions such as learning and memory. Experimentally, they are characterized by a change in the strength of a synaptic connection induced by repetitive and properly patterned stimulation protocols. Although many important details of the molecular events leading to LTP and LTD are known, experimenters often report problems in using standard induction protocols to obtain consistent results, especially for LTD in vivo. We hypothesize that a possible source of confusion in interpreting the results, from any given experiment on synaptic plasticity, can be the intrinsic limitation of the experimental techniques, which cannot take into account the actual state and peak conductance of the synapses before the conditioning protocol. In this article, we investigate the possibility that the same experimental protocol may result in different consequences (e.g., LTD instead of LTP), according to the initial conditions of the stimulated synapses, and can generate confusing results. Using biophysical models of synaptic plasticity and hippocampal CA1 pyramidal neurons, we study how, why, and to what extent the phenomena observed at the soma after induction of LTP/LTD reflects the actual (local) synaptic state. The model and the results suggest a physiologically plausible explanation for why LTD induction is experimentally difficult to obtain. They also suggest experimentally testable predictions on the stimulation protocols that may be more effective.

The cortical modulation of stimulus-specific adaptation in the auditory midbrain and thalamus: a potential neuronal correlate for predictive coding

To follow an ever-changing auditory scene, the auditory brain is continuously creating a representation of the past to form expectations about the future. Unexpected events will produce an error in the predictions that should “trigger” the network’s response. Indeed, neurons in the auditory midbrain, thalamus and cortex, respond to rarely occurring sounds while adapting to frequently repeated ones, i.e., they exhibit stimulus specific adaptation (SSA). SSA cannot be explained solely by intrinsic membrane properties, but likely involves the participation of the network. Thus, SSA is envisaged as a high order form of adaptation that requires the influence of cortical areas. However, present research supports the hypothesis that SSA, at least in its simplest form (i.e., to frequency deviants), can be transmitted in a bottom-up manner through the auditory pathway. Here, we briefly review the underlying neuroanatomy of the corticofugal projections before discussing state of the art studies which demonstrate that SSA present in the medial geniculate body (MGB) and inferior colliculus (IC) is not inherited from the cortex but can be modulated by the cortex via the corticofugal pathways. By modulating the gain of neurons in the thalamus and midbrain, the auditory cortex (AC) would refine SSA subcortically, preventing irrelevant information from reaching the cortex.

Plasticity in memristive devices for spiking neural networks

Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.

Circuit motifs for contrast-adaptive differentiation in early sensory systems: the role of presynaptic inhibition and short-term plasticity

In natural signals, such as the luminance value across of a visual scene, abrupt changes in intensity value are often more relevant to an organism than intensity values at other positions and times. Thus to reduce redundancy, sensory systems are specialized to detect the times and amplitudes of informative abrupt changes in the input stream rather than coding the intensity values at all times. In theory, a system that responds transiently to fast changes is called a differentiator. In principle, several different neural circuit mechanisms exist that are capable of responding transiently to abrupt input changes. However, it is unclear which circuit would be best suited for early sensory systems, where the dynamic range of the natural input signals can be very wide. We here compare the properties of different simple neural circuit motifs for implementing signal differentiation. We found that a circuit motif based on presynaptic inhibition (PI) is unique in a sense that the vesicle resources in the presynaptic site can be stably maintained over a wide range of stimulus intensities, making PI a biophysically plausible mechanism to implement a differentiator with a very wide dynamical range. Moreover, by additionally considering short-term plasticity (STP), differentiation becomes contrast adaptive in the PI-circuit but not in other potential neural circuit motifs. Numerical simulations show that the behavior of the adaptive PI-circuit is consistent with experimental observations suggesting that adaptive presynaptic inhibition might be a good candidate neural mechanism to achieve differentiation in early sensory systems.

A Framework for Quantitative Modeling of Neural Circuits Involved in Sleep-to-Wake Transition

Identifying the neuronal circuits and dynamics of sleep-to-wake transition is essential to understanding brain regulation of behavioral states, including sleep–wake cycles, arousal, and hyperarousal. Recent work by different laboratories has used optogenetics to determine the role of individual neuromodulators in state transitions. The optogenetically driven data do not yet provide a multi-dimensional schematic of the mechanisms underlying changes in vigilance states. This work presents a modeling framework to interpret, assist, and drive research on the sleep-regulatory network. We identify feedback, redundancy, and gating hierarchy as three fundamental aspects of this model. The presented model is expected to expand as additional data on the contribution of each transmitter to a vigilance state becomes available. Incorporation of conductance-based models of neuronal ensembles into this model and existing models of cortical excitability will provide more comprehensive insight into sleep dynamics as well as sleep and arousal-related disorders.

Statistical context shapes stimulus-specific adaptation in human auditory cortex

Stimulus-specific adaptation is the phenomenon whereby neural response magnitude decreases with repeated stimulation. Inconsistencies between recent nonhuman animal recordings and computational modeling suggest dynamic influences on stimulus-specific adaptation. The present human electroencephalography (EEG) study investigates the potential role of statistical context in dynamically modulating stimulus-specific adaptation by examining the auditory cortex-generated N1 and P2 components. As in previous studies of stimulus-specific adaptation, listeners were presented with oddball sequences in which the presentation of a repeated tone was infrequently interrupted by rare spectral changes taking on three different magnitudes. Critically, the statistical context varied with respect to the probability of small versus large spectral changes within oddball sequences (half of the time a small change was most probable; in the other half a large change was most probable). We observed larger N1 and P2 amplitudes (i.e., release from adaptation) for all spectral changes in the small-change compared with the large-change statistical context. The increase in response magnitude also held for responses to tones presented with high probability, indicating that statistical adaptation can overrule stimulus probability per se in its influence on neural responses. Computational modeling showed that the degree of coadaptation in auditory cortex changed depending on the statistical context, which in turn affected stimulus-specific adaptation. Thus the present data demonstrate that stimulus-specific adaptation in human auditory cortex critically depends on statistical context. Finally, the present results challenge the implicit assumption of stationarity of neural response magnitudes that governs the practice of isolating established deviant-detection responses such as the mismatch negativity.

Predicting changes in cortical electrophysiological function after in vitro traumatic brain injury

Finite element (FE) models of traumatic brain injury (TBI) are capable of predicting injury-induced brain tissue deformation. However, current FE models are not equipped to predict the biological consequences of tissue deformation, which requires the determination of tolerance criteria relating the effects of mechanical stimuli to biologically relevant functional responses. To address this deficiency, we present functional tolerance criteria for the cortex for alterations in neuronal network electrophysiological function in response to controlled mechanical stimuli. Organotypic cortical slice cultures were mechanically injured via equibiaxial stretch with a well-characterized in vitro model of TBI at tissue strains and strain rates relevant to TBI (up to Lagrangian strain of 0.59 and strain rates up to 29 [Formula: see text]. At 4-6 days post-injury, electrophysiological function was assessed simultaneously throughout the cortex using microelectrode arrays. Electrophysiological parameters associated with unstimulated spontaneous network activity (neural event rate, duration, and magnitude), stimulated evoked responses (the maximum response [Formula: see text], the stimulus current necessary for a half-maximal response [Formula: see text], and the electrophysiological parameter [Formula: see text] that is representative of firing uniformity), and evoked paired-pulse ratios at varying interstimulus intervals were quantified for each cortical slice culture. Nonlinear regression was performed between mechanical injury parameters as independent variables (tissue strain and strain rate) and each electrophysiological parameter as output. Parsimonious best-fit equations were determined from a large pool of candidate equations with tenfold cross-validation. Changes in electrophysiological parameters were dependent on strain and strain rate in a complex manner. Compared to the hippocampus, the cortex was less spontaneously active, less excitable, and less prone to significant changes in electrophysiological function in response to controlled deformation (strain or strain rate). Our study provides functional data that can be incorporated into FE models to improve their predictive capabilities of the in vivo consequences of TBI.

Simultaneous encoding of odors by channels with diverse sensitivity to inhibition

Odorant receptors in the periphery map precisely onto olfactory glomeruli ("coding channels") in the brain. However, the odor tuning of a glomerulus is not strongly correlated with its spatial position. This raises the question of whether lateral inhibition between glomeruli is specific or nonspecific. Here we show that, in the Drosophila brain, focal activation of even a single glomerulus recruits GABAergic interneurons in all glomeruli. Moreover, the relative level of interneuron activity in different glomeruli is largely odor invariant. Although interneurons are recruited nonspecifically, glomeruli differ dramatically in their sensitivity to interneuron activity, and this is explained by their varying sensitivity to GABA. Interestingly, a stimulus is typically encoded in parallel by channels having high and low sensitivity to inhibition. Because lateral inhibition confers both costs and benefits, the brain might rely preferentially on "high" and "low" channels in different behavioral contexts.

[EFFECT OF NEONATAL SEIZURES ON THE SYNAPTIC PLASTICITY OF RAT SOMATOSENSORY CORTEX]

Using an experimental model of neonatal recurrent seizures we investigated the influence of epileptic seizures in the various forms of synaptic plasticity in neurons of the somatosensory cortex. We found that early seizures do not affect the post-tetanic potentiation of the amplitude of the postsynaptic potentials and the depression of postsynaptic potentials during high-frequency stimulation. However they result in the chronic increase of the long-term potentiation of synaptic transmission. These changes of synaptic plasticity may affect the processing of the sensory information in patients with a history of recurrent seizures during early development.

Stochastic cortical neurodynamics underlying the memory and cognitive changes in aging

The relatively random spiking times of individual neurons provide a source of noise in the brain. We show how this noise interacting with altered depth in the basins of attraction of networks involved in short-term memory, attention, and episodic memory provide an approach to understanding some of the cognitive changes in normal aging. The effects of the neurobiological changes in aging that are considered include reduced synaptic modification and maintenance during learning produced in part through reduced acetylcholine in normal aging, reduced dopamine which reduces NMDA-receptor mediated effects, reduced noradrenaline which increases cAMP and thus shunts excitatory synaptic inputs, and the effects of a reduction in acetylcholine in increasing spike frequency adaptation. Using integrate-and-fire simulations of an attractor network implementing memory recall and short-term memory, it is shown that all these changes associated with aging reduce the firing rates of the excitatory neurons, which in turn reduce the depth of the basins of attraction, resulting in a much decreased probability in maintaining in short-term memory what has been recalled from the attractor network. This stochastic dynamics approach opens up new ways to understand and potentially treat the effects of normal aging on memory and cognitive functions.

Interplay between synaptic endocannabinoid signaling and metaplasticity in neuronal circuit function and dysfunction

Synaptic neuromodulation acts across different functional domains to regulate cognitive processing and behavior. Recent challenges are related to elucidating the molecular and cellular mechanisms through which neuromodulatory pathways act on multiple time scales to signal state-dependent contingencies at the synaptic level or to stabilise synaptic connections during behavior. Here, we present a framework with the synaptic neuromodulators endocannabinoids (eCBs) as key players in dynamic synaptic changes. Modulation of various molecular components of the eCB pathway yields interconnected functional activation states of eCB signaling (prior, tonic, and persistent), which may contribute to metaplastic control of synaptic and behavioral functions in health and disease. The emerging picture supports aberrant metaplasticity as a contributor to cognitive dysfunction associated with several pathological states in which eCB signaling, or other neuromodulatory pathways, are deregulated.

Stimulus-specific adaptation at the synapse level in vitro

Stimulus-specific adaptation (SSA) is observed in many brain regions in humans and animals. SSA of cortical neurons has been proposed to accumulate through relays in ascending pathways. Here, we examined SSA at the synapse level using whole-cell patch-clamp recordings of primary cultured cortical neurons of the rat. First, we found that cultured neurons had high firing capability with 100-Hz current injection. However, neuron firing started to adapt to repeated electrically activated synaptic inputs at 10 Hz. Next, to activate different dendritic inputs, electrical stimulations were spatially separated. Cultured neurons showed similar SSA properties in the oddball stimulation paradigm compared to those reported in vivo. Single neurons responded preferentially to a deviant stimulus over repeated, standard stimuli considering both synapse-driven spikes and excitatory postsynaptic currents (EPSCs). Compared with two closely placed stimulating electrodes that activated highly overlapping dendritic fields, two separately placed electrodes that activated less overlapping dendritic fields elicited greater SSA. Finally, we used glutamate puffing to directly activate postsynaptic glutamate receptors. Neurons showed SSA to two separately placed puffs repeated at 10 Hz. Compared with EPSCs, GABAa receptor-mediated inhibitory postsynaptic currents showed weaker SSA. Heterogeneity of the synaptic inputs was critical for producing SSA, with glutamate receptor desensitization participating in the process. Our findings suggest that postsynaptic fatigue contributes largely to SSA at low frequencies.