Mechanisms generating the time course of dual component excitatory synaptic currents recorded in hippocampal slices

Computational Modeling of Extrasynaptic NMDA Receptors: Insights into Dendritic Signal Amplification Mechanisms

Dendritic structures play a pivotal role in the computational processes occurring within neurons. Signal propagation along dendrites relies on both passive conduction and active processes related to voltage-dependent ion channels. Among these channels, extrasynaptic N-methyl-D-aspartate channels (exNMDA) emerge as a significant contributor. Prior studies have mainly concentrated on interactions between synapses and nearby exNMDA (100 nm-10 µm from synapse), activated by presynaptic membrane glutamate. This study concentrates on the correlation between synaptic inputs and distal exNMDA (>100 µm), organized in clusters that function as signal amplifiers. Employing a computational model of a dendrite, we elucidate the mechanism underlying signal amplification in exNMDA clusters. Our findings underscore the pivotal role of the optimal spatial positioning of the NMDA cluster in determining signal amplification efficiency. Additionally, we demonstrate that exNMDA subunits characterized by a large conduction decay constant. Specifically, NR2B subunits exhibit enhanced effectiveness in signal amplification compared to subunits with steeper conduction decay. This investigation extends our understanding of dendritic computational processes by emphasizing the significance of distant exNMDA clusters as potent signal amplifiers. The implications of our computational model shed light on the spatial considerations and subunit characteristics that govern the efficiency of signal amplification in dendritic structures, offering valuable insights for future studies in neurobiology and computational neuroscience.

A prefrontal network model operating near steady and oscillatory states links spike desynchronization and synaptic deficits in schizophrenia

Schizophrenia results in part from a failure of prefrontal networks but we lack full understanding of how disruptions at a synaptic level cause failures at the network level. This is a crucial gap in our understanding because it prevents us from discovering how genetic mutations and environmental risks that alter synaptic function cause prefrontal network to fail in schizophrenia. To address that question, we developed a recurrent spiking network model of prefrontal local circuits that can explain the link between NMDAR synaptic and 0-lag spike synchrony deficits we recently observed in a pharmacological monkey model of prefrontal network failure in schizophrenia. We analyze how the balance between AMPA and NMDA components of recurrent excitation and GABA inhibition in the network influence oscillatory spike synchrony to inform the biological data. We show that reducing recurrent NMDAR synaptic currents prevents the network from shifting from a steady to oscillatory state in response to extrinsic inputs such as might occur during behavior. These findings strongly parallel dynamic modulation of 0-lag spike synchrony we observed between neurons in monkey prefrontal cortex during behavior, as well as the suppression of this 0-lag spiking by administration of NMDAR antagonists. As such, our cortical network model provides a plausible mechanism explaining the link between NMDAR synaptic and 0-lag spike synchrony deficits observed in a pharmacological monkey model of prefrontal network failure in schizophrenia.

Short-term postsynaptic plasticity facilitates predictive tracking in continuous attractors

Introduction

The N-methyl-D-aspartate receptor (NMDAR) plays a critical role in synaptic transmission and is associated with various neurological and psychiatric disorders. Recently, a novel form of postsynaptic plasticity known as NMDAR-based short-term postsynaptic plasticity (STPP) has been identified. It has been suggested that long-lasting glutamate binding to NMDAR allows for the retention of input information in brain slices up to 500 ms, leading to response facilitation. However, the impact of STPP on the dynamics of neuronal populations remains unexplored.

Methods

In this study, we incorporated STPP into a continuous attractor neural network (CANN) model to investigate its effects on neural information encoding in populations of neurons. Unlike short-term facilitation, a form of presynaptic plasticity, the temporally enhanced synaptic efficacy resulting from STPP destabilizes the network state of the CANN by increasing its mobility.

Results

Our findings demonstrate that the inclusion of STPP in the CANN model enables the network state to predictively respond to a moving stimulus. This nontrivial dynamical effect facilitates the tracking of the anticipated stimulus, as the enhanced synaptic efficacy induced by STPP enhances the system's mobility.

Discussion

The discovered STPP-based mechanism for sensory prediction provides valuable insights into the potential development of brain-inspired computational algorithms for prediction. By elucidating the role of STPP in neural population dynamics, this study expands our understanding of the functional implications of NMDAR-related plasticity in information processing within the brain.

Conclusion

The incorporation of STPP into a CANN model highlights its influence on the mobility and predictive capabilities of neural networks. These findings contribute to our knowledge of STPP-based mechanisms and their potential applications in developing computational algorithms for sensory prediction.

Different resting membrane potentials in posterior parietal cortex and prefrontal cortex in the view of recurrent synaptic strengths and neural network dynamics

In this study, we introduce the importance of elevated membrane potentials (MPs) in the prefrontal cortex (PFC) compared to that in the posterior parietal cortex (PPC), based on new observations of different MP levels in these areas. Through experimental data and spiking neural network modeling, we investigated a possible mechanism of the elevated membrane potential in the PFC and how these physiological differences affect neural network dynamics and cognitive functions in the PPC and PFC. Our findings indicate that NMDA receptors may be a main contributor to the elevated MP in the PFC region and highlight the potential of using a modeling toolkit to investigate the means by which changes in synaptic properties can affect neural dynamics and potentiate desirable cognitive functions through population activities in the corresponding brain regions.

Neuronal glutamate transporters control reciprocal inhibition and gain modulation in D1 medium spiny neurons

Understanding the function of glutamate transporters has broad implications for explaining how neurons integrate information and relay it through complex neuronal circuits. Most of what is currently known about glutamate transporters, specifically their ability to maintain glutamate homeostasis and limit glutamate diffusion away from the synaptic cleft, is based on studies of glial glutamate transporters. By contrast, little is known about the functional implications of neuronal glutamate transporters. The neuronal glutamate transporter EAAC1 is widely expressed throughout the brain, particularly in the striatum, the primary input nucleus of the basal ganglia, a region implicated with movement execution and reward. Here, we show that EAAC1 limits synaptic excitation onto a population of striatal medium spiny neurons identified for their expression of D1 dopamine receptors (D1-MSNs). In these cells, EAAC1 also contributes to strengthen lateral inhibition from other D1-MSNs. Together, these effects contribute to reduce the gain of the input-output relationship and increase the offset at increasing levels of synaptic inhibition in D1-MSNs. By reducing the sensitivity and dynamic range of action potential firing in D1-MSNs, EAAC1 limits the propensity of mice to rapidly switch between behaviors associated with different reward probabilities. Together, these findings shed light on some important molecular and cellular mechanisms implicated with behavior flexibility in mice.

Quantifying the Landscape of Decision Making From Spiking Neural Networks

The decision making function is governed by the complex coupled neural circuit in the brain. The underlying energy landscape provides a global picture for the dynamics of the neural decision making system and has been described extensively in the literature, but often as illustrations. In this work, we explicitly quantified the landscape for perceptual decision making based on biophysically-realistic cortical network with spiking neurons to mimic a two-alternative visual motion discrimination task. Under certain parameter regions, the underlying landscape displays bistable or tristable attractor states, which quantify the transition dynamics between different decision states. We identified two intermediate states: the spontaneous state which increases the plasticity and robustness of changes of minds and the "double-up" state which facilitates the state transitions. The irreversibility of the bistable and tristable switches due to the probabilistic curl flux demonstrates the inherent non-equilibrium characteristics of the neural decision system. The results of global stability of decision-making quantified by barrier height inferred from landscape topography and mean first passage time are in line with experimental observations. These results advance our understanding of the stochastic and dynamical transition mechanism of decision-making function, and the landscape and kinetic path approach can be applied to other cognitive function related problems (such as working memory) in brain networks.

Climbing Fiber-Mediated Spillover Transmission to Interneurons Is Regulated by EAAT4

Neurotransmitter spillover is a form of communication not readily predicted by anatomic structure. In the cerebellum, glutamate spillover from climbing fibers recruits molecular layer interneurons in the absence of conventional synaptic connections. Spillover-mediated signaling is typically limited by transporters that bind and reuptake glutamate. Here, we show that patterned expression of the excitatory amino acid transporter 4 (EAAT4) in Purkinje cells regulates glutamate spillover to molecular layer interneurons. Using male and female Aldolase C-Venus knock-in mice to visualize zebrin microzones, we find larger climbing fiber-evoked spillover EPSCs in regions with low levels of EAAT4 compared with regions with high EAAT4. This difference is not explained by presynaptic glutamate release properties or postsynaptic receptor density but rather by differences in the glutamate concentration reaching receptors on interneurons. Inhibiting glutamate transport normalizes the differences between microzones, suggesting that heterogeneity in EAAT4 expression is a primary determinant of differential spillover. These results show that neuronal glutamate transporters limit extrasynaptic transmission in a non-cell-autonomous manner and provide new insight into the functional specialization of cerebellar microzones.SIGNIFICANCE STATEMENT Excitatory amino acid transporters (EAATs) help maintain the fidelity and independence of point-to-point synaptic transmission. Whereas glial transporters are critical to maintain low ambient levels of extracellular glutamate to prevent excitotoxicity, neuronal transporters have more subtle roles in shaping excitatory synaptic transmission. Here we show that the patterned expression of neuronal EAAT4 in cerebellar microzones controls glutamate spillover from cerebellar climbing fibers to nearby interneurons. These results contribute to fundamental understanding of neuronal transporter functions and specialization of cerebellar microzones.

Slow excitatory synaptic currents generated by AMPA receptors

Decades of literature indicate that the AMPA-type glutamate receptor is among the fastest acting of all neurotransmitter receptors. These receptors are located at excitatory synapses, and conventional wisdom says that they activate in hundreds of microseconds, deactivate in milliseconds due to their low affinity for glutamate and also desensitize profoundly. These properties circumscribe AMPA receptor activation in both space and time. However, accumulating evidence shows that AMPA receptors can also activate with slow, indefatigable responses. They do so through interactions with auxiliary subunits that are able promote a switch to a high open probability, high-conductance 'superactive' mode. In this review, we show that any assumption that this phenomenon is limited to heterologous expression is false and rather that slow AMPA currents have been widely and repeatedly observed throughout the nervous system. Hallmarks of the superactive mode are a lack of desensitization, resistance to competitive antagonists and a current decay that outlives free glutamate by hundreds of milliseconds. Because the switch to the superactive mode is triggered by activation, AMPA receptors can generate accumulating 'pedestal' currents in response to repetitive stimulation, constituting a postsynaptic mechanism for short-term potentiation in the range 5-100 Hz. Further, slow AMPA currents span 'cognitive' time intervals in the 100 ms range (theta rhythms), of particular interest for hippocampal function, where slow AMPA currents are widely expressed in a synapse-specific manner. Here, we outline the implications that slow AMPA receptors have for excitatory synaptic transmission and computation in the nervous system.

Distorting Face Representations in Newborn Brains

What role does experience play in the development of face recognition? A growing body of evidence indicates that newborn brains need slowly changing visual experiences to develop accurate visual recognition abilities. All of the work supporting this "slowness constraint" on visual development comes from studies testing basic-level object recognition. Here, we present the results of controlled-rearing experiments that provide evidence for a slowness constraint on the development of face recognition, a prototypical subordinate-level object recognition task. We found that (1) newborn chicks can rapidly develop view-invariant face recognition and (2) the development of this ability relies on experience with slowly moving faces. When chicks were reared with quickly moving faces, they built distorted face representations that largely lacked invariance to viewpoint changes, effectively "breaking" their face recognition abilities. These results provide causal evidence that slowly changing visual experiences play a critical role in the development of face recognition, akin to basic-level object recognition. Thus, face recognition is not a hardwired property of vision but is learned rapidly as the visual system adapts to the temporal structure of the animal's visual environment.

Slow AMPA receptors in hippocampal principal cells

Glutamate receptor ion channels, including α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, mediate fast excitatory neurotransmission in the CNS. Previous work suggested that AMPA receptors produce a synaptic current with a millisecond duration. However, we find that about two-thirds of principal cells in the hippocampal CA1 region also express AMPA receptors with reduced desensitization that can stay active for half a second after repetitive stimuli. These slow AMPA receptors are expressed at about half of the synapses, with a flat spatial distribution. The increased charge transfer from slow AMPA receptors allows short-term potentiation from a postsynaptic locus and reliable triggering of action potentials. Biophysical and pharmacological observations imply slow AMPA receptors incorporate auxiliary proteins, and their activation lengthens miniature synaptic currents. These data indicate that AMPA receptors are a major source of synaptic diversity. Synapses harboring slow AMPA receptors could have unique roles in hippocampal function.

Role of AMPA receptor desensitization in short term depression - lessons from retinogeniculate synapses

Repetitive synapse activity induces various forms of short-term plasticity. The role of presynaptic mechanisms such as residual Ca²⁺ and vesicle depletion in short-term facilitation and short-term depression is well established. On the other hand, the contribution of postsynaptic mechanisms such as receptor desensitization and saturation to short-term plasticity is less well known and often ignored. In this review, I will describe short-term plasticity in retinogeniculate synapses of relay neurons of the dorsal lateral geniculate nucleus (dLGN) to exemplify the synaptic properties that facilitate the contribution of AMPA receptor desensitization to short-term plasticity. These include high vesicle release probability, glutamate spillover and, importantly, slow recovery from desensitization of AMPA receptors. The latter is strongly regulated by the interaction of AMPA receptors with auxiliary proteins such as CKAMP44. Finally, I discuss the relevance of short-term plasticity in retinogeniculate synapses for the processing of visual information by LGN relay neurons.

Structure and function of a neocortical synapse

In 1986, electron microscopy was used to reconstruct by hand the entire nervous system of a roundworm, the nematode Caenorhabditis elegans¹. Since this landmark study, high-throughput electron-microscopic techniques have enabled reconstructions of much larger mammalian brain circuits at synaptic resolution^2,3. Nevertheless, it remains unknown how the structure of a synapse relates to its physiological transmission strength-a key limitation for inferring brain function from neuronal wiring diagrams. Here we combine slice electrophysiology of synaptically connected pyramidal neurons in the mouse somatosensory cortex with correlated light microscopy and high-resolution electron microscopy of all putative synaptic contacts between the recorded neurons. We find a linear relationship between synapse size and strength, providing the missing link in assigning physiological weights to synapses reconstructed from electron microscopy. Quantal analysis also reveals that synapses contain at least 2.7 neurotransmitter-release sites on average. This challenges existing release models and provides further evidence that neocortical synapses operate with multivesicular release^4-6, suggesting that they are more complex computational devices than thought, and therefore expanding the computational power of the canonical cortical microcircuitry.

Local Postsynaptic Signaling on Slow Time Scales in Reciprocal Olfactory Bulb Granule Cell Spines Matches Asynchronous Release

In the vertebrate olfactory bulb (OB), axonless granule cells (GC) mediate self- and lateral inhibitory interactions between mitral/tufted cells via reciprocal dendrodendritic synapses. Locally triggered release of GABA from the large reciprocal GC spines occurs on both fast and slow time scales, possibly enabling parallel processing during olfactory perception. Here we investigate local mechanisms for asynchronous spine output. To reveal the temporal and spatial characteristics of postsynaptic ion transients, we imaged spine and adjacent dendrite Ca2 +- and Na+-signals with minimal exogenous buffering by the respective fluorescent indicator dyes upon two-photon uncaging of DNI-glutamate in OB slices from juvenile rats. Both postsynaptic fluorescence signals decayed slowly, with average half durations in the spine head of t1 / 2_Δ[Ca2 +]i ∼500 ms and t1 / 2_Δ[Na+]i ∼1,000 ms. We also analyzed the kinetics of already existing data of postsynaptic spine Ca2 +-signals in response to glomerular stimulation in OB slices from adult mice, either WT or animals with partial GC glutamate receptor deletions (NMDAR: GluN1 subunit; AMPAR: GluA2 subunit). In a large subset of spines the fluorescence signal had a protracted rise time (average time to peak ∼400 ms, range 20 to >1,000 ms). This slow rise was independent of Ca2 + entry via NMDARs, since similarly slow signals occurred in ΔGluN1 GCs. Additional Ca2 + entry in ΔGluA2 GCs (with AMPARs rendered Ca2 +-permeable), however, resulted in larger ΔF/Fs that rose yet more slowly. Thus GC spines appear to dispose of several local mechanisms to promote asynchronous GABA release, which are reflected in the time course of mitral/tufted cell recurrent inhibition.

The Generation of Time in the Hippocampal Memory System

We propose that ramping time cells in the lateral entorhinal cortex can be produced by synaptic adaptation and demonstrate this in an integrate-and-fire attractor network model. We propose that competitive networks in the hippocampal system can convert these entorhinal ramping cells into hippocampal time cells and demonstrate this in a competitive network. We propose that this conversion is necessary to provide orthogonal hippocampal time representations to encode the temporal sequence of events in hippocampal episodic memory, and we support that with analytic arguments. We demonstrate that this processing can produce hippocampal neuronal ensembles that not only show replay of the sequence later on, but can also do this in reverse order in reverse replay. This research addresses a major issue in neuroscience: the mechanisms by which time is encoded in the brain and how the time representations are then useful in the hippocampal memory of events and their order.

Data-driven integration of hippocampal CA1 synaptic physiology in silico

The anatomy and physiology of monosynaptic connections in rodent hippocampal CA1 have been extensively studied in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data‐driven approach to integrate the current state‐of‐the‐art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo‐dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short‐term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource toward a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.

δ subunit-containing GABAA IPSCs are driven by both synaptic and diffusional GABA in mouse dentate granule neurons

KEY POINTS

Current views suggest γ2 subunit-containing GABA_A receptors mediate phasic IPSCs while extrasynaptic δ subunits mediate diffusional IPSCs and tonic current. We have re-examined the roles of the two receptor populations using mice with picrotoxin resistance engineered into receptors containing the δ subunit. Using pharmacological separation, we find that in general δ and γ IPSCs are modulated in parallel by manipulations of transmitter output and diffusion, with evidence favouring modestly more diffusional contribution to δ IPSCs. Our findings also reveal that spontaneous δ IPSCs are mainly driven by channel deactivation, rather than by diffusion of GABA. Understanding the functional contributions of the two receptor classes may help us understand the actions of drug therapies with selective effects on one population over the other.

ABSTRACT

GABA_A receptors mediate transmission throughout the central nervous system and typically contain a δ subunit (δ receptors) or a γ2 subunit (γ2 receptors). δ IPSCs decay slower than γ2 IPSCs, but the reasons are unclear. Transmitter diffusion, rebinding, or slow deactivation kinetics of channels are candidates. We used gene editing to confer picrotoxin resistance on δ receptors in mice, then pharmacologically isolated δ receptors in mouse dentate granule cells to explore IPSCs. γ2 and δ components of IPSCs were modulated similarly by presynaptic manipulations and manipulations of transmitter lifetime, suggesting that GABA release recruits δ receptors proportionally to γ2 receptors. δ IPSCs showed more sensitivity to altered transmitter release and to a rapidly dissociating antagonist, suggesting an additional spillover contribution. Reducing GABA diffusion with 5% dextran increased the peak amplitude and decreased the decay of evoked δ IPSCs but had no effect on δ or dual-component (mainly γ2-driven) spontaneous IPSCs, suggesting that GABA actions can be local for both receptor types. Rapid application of varied [GABA] onto nucleated patches from dentate granule cells demonstrated a deactivation rate of δ receptors similar to that of δ spontaneous IPSCs, consistent with the idea that deactivation and local GABA actions drive δ spontaneous IPSCs. Overall, our results indicate that δ IPSCs are activated by both synaptic and diffusional GABA. Our results are consistent with a functional relationship between δ and γ2 GABA_A receptors akin to that of slow NMDA and fast AMPA EPSCs at glutamate synapses.

Network Models Predict That Pyramidal Neuron Hyperexcitability and Synapse Loss in the dlPFC Lead to Age-Related Spatial Working Memory Impairment in Rhesus Monkeys

Behavioral studies have shown spatial working memory impairment with aging in several animal species, including humans. Persistent activity of layer 3 pyramidal dorsolateral prefrontal cortex (dlPFC) neurons during delay periods of working memory tasks is important for encoding memory of the stimulus. In vitro studies have shown that these neurons undergo significant age-related structural and functional changes, but the extent to which these changes affect neural mechanisms underlying spatial working memory is not understood fully. Here, we confirm previous studies showing impairment on the Delayed Recognition Span Task in the spatial condition (DRSTsp), and increased in vitro action potential firing rates (hyperexcitability), across the adult life span of the rhesus monkey. We use a bump attractor model to predict how empirically observed changes in the aging dlPFC affect performance on the Delayed Response Task (DRT), and introduce a model of memory retention in the DRSTsp. Persistent activity-and, in turn, cognitive performance-in both models was affected much more by hyperexcitability of pyramidal neurons than by a loss of synapses. Our DRT simulations predict that additional changes to the network, such as increased firing of inhibitory interneurons, are needed to account for lower firing rates during the DRT with aging reported in vivo. Synaptic facilitation was an essential feature of the DRSTsp model, but it did not compensate fully for the effects of the other age-related changes on DRT performance. Modeling pyramidal neuron hyperexcitability and synapse loss simultaneously led to a partial recovery of function in both tasks, with the simulated level of DRSTsp impairment similar to that observed in aging monkeys. This modeling work integrates empirical data across multiple scales, from synapse counts to cognitive testing, to further our understanding of aging in non-human primates.

Cholinergic Modulation Promotes Attentional Modulation in Primary Visual Cortex- A Modeling Study

Attention greatly influences sensory neural processing by enhancing firing rates of neurons that represent the attended stimuli and by modulating their tuning properties. The cholinergic system is believed to partly mediate the attention contingent improvement of cortical processing by influencing neuronal excitability, synaptic transmission and neural network characteristics. Here, we used a biophysically based model to investigate the mechanisms by which cholinergic system influences sensory information processing in the primary visual cortex (V1) layer 4C. The physiological properties and architectures of our model were inspired by experimental data and include feed-forward input from dorsal lateral geniculate nucleus that sets up orientation preference in V1 neural responses. When including a cholinergic drive, we found significant sharpening in orientation selectivity, desynchronization of LFP gamma power and spike-field coherence, decreased response variability and correlation reduction mostly by influencing intracortical interactions and by increasing inhibitory drive. Our results indicated that these effects emerged due to changes specific to the behavior of the inhibitory neurons. The behavior of our model closely resembles the effects of attention on neural activities in monkey V1. Our model suggests precise mechanisms through which cholinergic modulation may mediate the effects of attention in the visual cortex.

N-terminal alternative splicing of GluN1 regulates the maturation of excitatory synapses and seizure susceptibility

The majority of NMDA receptors (NMDARs) in the brain are composed of 2 GluN1 and 2 GluN2 subunits. The inclusion or exclusion of 1 N-terminal and 2 C-terminal domains of GluN1 results in 8 splicing variants that exhibit distinct temporal and spatial patterns of expression and functional properties. However, previous functional analyses of Grin1 variants have been done using heterologous expression and the in vivo function of Grin1 splicing is unknown. Here we show that N-terminal splicing of GluN1 has important functions in the maturation of excitatory synapses. The inclusion of exon 5 of Grin1 is up-regulated in several brain regions such as the thalamus and neocortex. We find that deletion of Grin1 exon 5 disrupts the developmental remodeling of NMDARs in thalamic neurons and the effect is distinct from that of Grin2a (GluN2A) deletion. Deletion of Grin2a or exon 5 of Grin1 alone partially attenuates the shortening of NMDAR-mediated excitatory postsynaptic currents (NMDAR-EPSCs) during early life, whereas deletion of both Grin2a and exon 5 of Grin1 completely abolishes the developmental change in NMDAR-EPSC decay time. Deletion of exon 5 of Grin1 leads to an overproduction of excitatory synapses in layer 5 pyramidal neurons in the cortex and increases seizure susceptibility in adult mice. Our findings demonstrate that N-terminal splicing of GluN1 has important functions in synaptic maturation and neuronal network excitability.

Single Synapse Indicators of Impaired Glutamate Clearance Derived from Fast iGlu u Imaging of Cortical Afferents in the Striatum of Normal and Huntington (Q175) Mice

Changes in the balance between glutamate (Glu) release and uptake may stimulate synaptic reorganization and even synapse loss. In the case of neurodegeneration, a mismatch between astroglial Glu uptake and presynaptic Glu release could be detected if both parameters were assessed independently and at a single-synapse level. This has now become possible due to a new imaging assay with the genetically encoded ultrafast Glu sensor iGlu _u We report findings from individual corticostriatal synapses in acute slices prepared from mice of either sex that were >1 year of age. Contrasting patterns of short-term plasticity and a size criterion identified two classes of terminals, presumably corresponding to the previously defined IT (intratelencephalic) and PT (pyramidal tract) synapses. The latter exhibited a higher degree of frequency potentiation/residual Glu accumulation and were selected for our first iGlu _u single-synapse study in Q175 mice, a model of Huntington's disease (HD). In HD mice, the decay time constant of the perisynaptic Glu concentration (TauD), as an indicator of uptake, and the peak iGlu _u amplitude, as an indicator of release, were prolonged and reduced, respectively. Treatment of WT preparations with the astrocytic Glu uptake blocker TFB-TBOA (100 nm) mimicked the TauD changes in homozygotes. Considering the largest TauD values encountered in WT, ∼40% of PT synapses tested in Q175 heterozygotes can be classified as dysfunctional. Moreover, HD but not WT synapses exhibited a positive correlation between TauD and the peak amplitude of iGlu _u Finally, EAAT2 (excitatory amino acid transport protein 2) immunoreactivity was reduced next to corticostriatal terminals. Thus, astrocytic Glu transport remains a promising target for therapeutic intervention.SIGNIFICANCE STATEMENT Alterations in astrocytic Glu uptake can play a role in synaptic plasticity and neurodegeneration. Until now, the sensitivity of synaptic responses to pharmacological transport block and the resulting activation of NMDA receptors were regarded as reliable evidence for a mismatch between synaptic uptake and release. But the latter parameters are interdependent. Using a new genetically encoded sensor to monitor extracellular glutamate concentration ([Glu]) at individual corticostriatal synapses, we can now quantify the time constant of perisynaptic [Glu] decay (as an indicator of uptake) and the maximal [Glu] elevation next to the active zone (as an indicator of Glu release). The results provide a positive answer to the hitherto unresolved question of whether neurodegeneration (e.g., Huntington's disease) associates with a glutamate uptake deficit at tripartite excitatory synapses.

Higher ambient synaptic glutamate at inhibitory versus excitatory neurons differentially impacts NMDA receptor activity

Selective disruption of synaptic drive to inhibitory neurons could contribute to the pathophysiology of various brain disorders. We have previously identified a GluN2A-selective positive allosteric modulator, GNE-8324, that selectively enhances N-methyl-d-aspartate receptor (NMDAR)-mediated synaptic responses in inhibitory but not excitatory neurons. Here, we demonstrate that differences in NMDAR subunit composition do not underlie this selective potentiation. Rather, a higher ambient glutamate level in the synaptic cleft of excitatory synapses on inhibitory neurons is a key factor. We show that increasing expression of glutamate transporter 1 (GLT-1) eliminates GNE-8324 potentiation in inhibitory neurons, while decreasing GLT-1 activity enables potentiation in excitatory neurons. Our results reveal an unsuspected difference between excitatory synapses onto different neuronal types, and a more prominent activation of synaptic NMDARs by ambient glutamate in inhibitory than excitatory neurons. This difference has implications for tonic NMDAR activity/signaling and the selective modulation of inhibitory neuron activity to treat brain disorders.

Inhibitory interneurons play important roles in brain circuits and in several neuropsychiatric disorders. Here, the authors show that excitatory synapses onto interneurons vs. excitatory neurons differ in their ambient synaptic glutamate level, a finding with important implications for selective pharmacological targeting of inhibitory neuron NMDA receptors.

Non-accidental properties, metric invariance, and encoding by neurons in a model of ventral stream visual object recognition, VisNet

When objects transform into different views, some properties are maintained, such as whether the edges are convex or concave, and these non-accidental properties are likely to be important in view-invariant object recognition. The metric properties, such as the degree of curvature, may change with different views, and are less likely to be useful in object recognition. It is shown that in a model of invariant visual object recognition in the ventral visual stream, VisNet, non-accidental properties are encoded much more than metric properties by neurons. Moreover, it is shown how with the temporal trace rule training in VisNet, non-accidental properties of objects become encoded by neurons, and how metric properties are treated invariantly. We also show how VisNet can generalize between different objects if they have the same non-accidental property, because the metric properties are likely to overlap. VisNet is a 4-layer unsupervised model of visual object recognition trained by competitive learning that utilizes a temporal trace learning rule to implement the learning of invariance using views that occur close together in time. A second crucial property of this model of object recognition is, when neurons in the level corresponding to the inferior temporal visual cortex respond selectively to objects, whether neurons in the intermediate layers can respond to combinations of features that may be parts of two or more objects. In an investigation using the four sides of a square presented in every possible combination, it was shown that even though different layer 4 neurons are tuned to encode each feature or feature combination orthogonally, neurons in the intermediate layers can respond to features or feature combinations present is several objects. This property is an important part of the way in which high capacity can be achieved in the four-layer ventral visual cortical pathway. These findings concerning non-accidental properties and the use of neurons in intermediate layers of the hierarchy help to emphasise fundamental underlying principles of the computations that may be implemented in the ventral cortical visual stream used in object recognition.

Activity-induced spontaneous spikes in GABAergic neurons suppress seizure discharges: an implication of computational modeling

Background

Epilepsy, a prevalent neurological disorder, appears self-termination. The endogenous mechanism for seizure self-termination remains to be addressed in order to develop new strategies for epilepsy treatment. We aim to examine the role of activity-induced spontaneous spikes at GABAergic neurons as an endogenous mechanism in the seizure self-termination.

Methods and Results

Neuronal spikes were induced by depolarization pulses at cortical GABAergic neurons from temporal lobe epilepsy patients and mice, in which some of these neurons fired activity-induced spontaneous spikes. Neural networks including excitatory and inhibitory neurons were computationally constructed, and their functional properties were based on our studies from whole-cell recordings. With the changes in the portion and excitability of inhibitory neurons that generated activity-induced spontaneous spike, the efficacies to suppress synchronous seizure activity were analyzed, such as its onset time, decay slope and spike frequency. The increases in the proportion and excitability of inhibitory neurons that generated activity-induced spontaneous spikes effectively suppressed seizure activity in neural networks. These factors synergistically strengthened the efficacy of seizure activity suppression.

Conclusion

Our study supports a notion that activity-induced spontaneous spikes in GABAergic neurons may be an endogenous mechanism for seizure self-termination. A potential therapeutic strategy for epilepsy is to upregulate the cortical inhibitory neurons that generate activity-induced spontaneous spikes.

Physiology of Astroglia

Astrocytes are neural cells of ectodermal, neuroepithelial origin that provide for homeostasis and defense of the central nervous system (CNS). Astrocytes are highly heterogeneous in morphological appearance; they express a multitude of receptors, channels, and membrane transporters. This complement underlies their remarkable adaptive plasticity that defines the functional maintenance of the CNS in development and aging. Astrocytes are tightly integrated into neural networks and act within the context of neural tissue; astrocytes control homeostasis of the CNS at all levels of organization from molecular to the whole organ.

Physiology of Astroglia

Astrocytes are neural cells of ectodermal, neuroepithelial origin that provide for homeostasis and defense of the central nervous system (CNS). Astrocytes are highly heterogeneous in morphological appearance; they express a multitude of receptors, channels, and membrane transporters. This complement underlies their remarkable adaptive plasticity that defines the functional maintenance of the CNS in development and aging. Astrocytes are tightly integrated into neural networks and act within the context of neural tissue; astrocytes control homeostasis of the CNS at all levels of organization from molecular to the whole organ.

Computations in the deep vs superficial layers of the cerebral cortex

A fundamental question is how the cerebral neocortex operates functionally, computationally. The cerebral neocortex with its superficial and deep layers and highly developed recurrent collateral systems that provide a basis for memory-related processing might perform somewhat different computations in the superficial and deep layers. Here we take into account the quantitative connectivity within and between laminae. Using integrate-and-fire neuronal network simulations that incorporate this connectivity, we first show that attractor networks implemented in the deep layers that are activated by the superficial layers could be partly independent in that the deep layers might have a different time course, which might because of adaptation be more transient and useful for outputs from the neocortex. In contrast the superficial layers could implement more prolonged firing, useful for slow learning and for short-term memory. Second, we show that a different type of computation could in principle be performed in the superficial and deep layers, by showing that the superficial layers could operate as a discrete attractor network useful for categorisation and feeding information forward up a cortical hierarchy, whereas the deep layers could operate as a continuous attractor network useful for providing a spatially and temporally smooth output to output systems in the brain. A key advance is that we draw attention to the functions of the recurrent collateral connections between cortical pyramidal cells, often omitted in canonical models of the neocortex, and address principles of operation of the neocortex by which the superficial and deep layers might be specialized for different types of attractor-related memory functions implemented by the recurrent collaterals.

Modeling the Time-Course of Responses for the Border Ownership Selectivity Based on the Integration of Feedforward Signals and Visual Cortical Interactions

Border ownership (BO) indicates which side of a contour owns a border, and it plays a fundamental role in figure-ground segregation. The majority of neurons in V2 and V4 areas of monkeys exhibit BO selectivity. A physiological work reported that the responses of BO-selective cells show a rapid transition when a presented square is flipped along its classical receptive field (CRF) so that the opposite BO is presented, whereas the transition is significantly slower when a square with a clear BO is replaced by an ambiguous edge, e.g., when the square is enlarged greatly. The rapid transition seemed to reflect the influence of feedforward processing on BO selectivity. Herein, we investigated the role of feedforward signals and cortical interactions for time-courses in BO-selective cells by modeling a visual cortical network comprising V1, V2, and posterior parietal (PP) modules. In our computational model, the recurrent pathways among these modules gradually established the visual progress and the BO assignments. Feedforward inputs mainly determined the activities of these modules. Surrounding suppression/facilitation of early-level areas modulates the activities of V2 cells to provide BO signals. Weak feedback signals from the PP module enhanced the contrast gain extracted in V1, which underlies the attentional modulation of BO signals. Model simulations exhibited time-courses depending on the BO ambiguity, which were caused by the integration delay of V1 and V2 cells and the local inhibition therein given the difference in input stimulus. However, our model did not fully explain the characteristics of crucially slow transition: the responses of BO-selective physiological cells indicated the persistent activation several times longer than that of our model after the replacement with the ambiguous edge. Furthermore, the time-course of BO-selective model cells replicated the attentional modulation of response time in human psychophysical experiments. These attentional modulations for time-courses were induced by selective enhancement of early-level features due to interactions between V1 and PP. Our proposed model suggests fundamental roles of surrounding suppression/facilitation based on feedforward inputs as well as the interactions between early and parietal visual areas with respect to the ambiguity dependence of the neural dynamics in intermediate-level vision.

The Features and Functions of Neuronal Assemblies: Possible Dependency on Mechanisms beyond Synaptic Transmission

"Neuronal assemblies" are defined here as coalitions within the brain of millions of neurons extending in space up to 1-2 mm, and lasting for hundreds of milliseconds: as such they could potentially link bottom-up, micro-scale with top-down, macro-scale events. The perspective first compares the features in vitro versus in vivo of this underappreciated "meso-scale" level of brain processing, secondly considers the various diverse functions in which assemblies may play a pivotal part, and thirdly analyses whether the surprisingly spatially extensive and prolonged temporal properties of assemblies can be described exclusively in terms of classic synaptic transmission or whether additional, different types of signaling systems are likely to operate. Based on our own voltage-sensitive dye imaging (VSDI) data acquired in vitro we show how restriction to only one signaling process, i.e., synaptic transmission, is unlikely to be adequate for modeling the full profile of assemblies. Based on observations from VSDI with its protracted spatio-temporal scales, we suggest that two other, distinct processes are likely to play a significant role in assembly dynamics: "volume" transmission (the passive diffusion of diverse bioactive transmitters, hormones, and modulators), as well as electrotonic spread via gap junctions. We hypothesize that a combination of all three processes has the greatest potential for deriving a realistic model of assemblies and hence elucidating the various complex brain functions that they may mediate.

Propagation of Collective Temporal Regularity in Noisy Hierarchical Networks

Neuronal communication between different brain areas is achieved in terms of spikes. Consequently, spike-time regularity is closely related to many cognitive tasks and timing precision of neural information processing. A recent experiment on primate parietal cortex reports that spike-time regularity increases consistently from primary sensory to higher cortical regions. This observation conflicts with the influential view that spikes in the neocortex are fundamentally irregular. To uncover the underlying network mechanism, we construct a multilayered feedforward neural information transmission pathway and investigate how spike-time regularity evolves across subsequent layers. Numerical results reveal that despite the obviously irregular spiking patterns in previous several layers, neurons in downstream layers can generate rather regular spikes, which depends on the network topology. In particular, we find that collective temporal regularity in deeper layers exhibits resonance-like behavior with respect to both synaptic connection probability and synaptic weight, i.e., the optimal topology parameter maximizes the spike-timing regularity. Furthermore, it is demonstrated that synaptic properties, including inhibition, synaptic transient dynamics, and plasticity, have significant impacts on spike-timing regularity propagation. The emergence of the increasingly regular spiking (RS) patterns in higher parietal regions can, thus, be viewed as a natural consequence of spiking activity propagation between different brain areas. Finally, we validate an important function served by increased RS: promoting reliable propagation of spike-rate signals across downstream layers.

Response repetition biases in human perceptual decisions are explained by activity decay in competitive attractor models

Animals and humans have a tendency to repeat recent choices, a phenomenon known as choice hysteresis. The mechanism for this choice bias remains unclear. Using an established, biophysically informed model of a competitive attractor network for decision making, we found that decaying tail activity from the previous trial caused choice hysteresis, especially during difficult trials, and accurately predicted human perceptual choices. In the model, choice variability could be directionally altered through amplification or dampening of post-trial activity decay through simulated depolarizing or hyperpolarizing network stimulation. An analogous intervention using transcranial direct current stimulation (tDCS) over left dorsolateral prefrontal cortex (dlPFC) yielded a close match between model predictions and experimental results: net soma depolarizing currents increased choice hysteresis, while hyperpolarizing currents suppressed it. Residual activity in competitive attractor networks within dlPFC may thus give rise to biases in perceptual choices, which can be directionally controlled through non-invasive brain stimulation.

The Edge of Stability: Response Times and Delta Oscillations in Balanced Networks

The standard architecture of neocortex is a network with excitation and inhibition in closely maintained balance. These networks respond fast and with high precision to their inputs and they allow selective amplification of patterned signals. The stability of such networks is known to depend on balancing the strengths of positive and negative feedback. We here show that a second condition is required for stability which depends on the relative strengths and time courses of fast (AMPA) and slow (NMDA) currents in the excitatory projections. This condition also determines the response time of the network. We show that networks which respond quickly to an input are necessarily close to an oscillatory instability which resonates in the delta range. This instability explains the existence of neocortical delta oscillations and the emergence of absence epilepsy. Although cortical delta oscillations are a network-level phenomenon, we show that in non-pathological networks, individual neurons receive sufficient information to keep the network in the fast-response regime without sliding into the instability.

Many networks in the brain are finely balanced, with equal contributions from excitation and inhibition. Deviations from this balance, if for instance the total amount of excitation exceeds that of inhibition, lead to potentially devastating instabilities. Unlike previous work we consider the interaction between fast and slow excitatory connections. We show that not only the amount of excitation needs to be controlled to achieve network stability but also the ratio of slow to fast excitation. Furthermore, optimally fast network performance requires that networks approach instability. However, networks very close to this instability develop oscillations in the delta range (1–4Hz) which potentially cause absence epilepsy. We show that a normal (non-pathological) network can auto-regulate its activity to avoid the instability.

VTA Projection Neurons Releasing GABA and Glutamate in the Dentate Gyrus

Both dopamine and nondopamine neurons from the ventral tegmental area (VTA) project to a variety of brain regions. Here we examine nondopaminergic neurons in the mouse VTA that send long-range projections to the hippocampus. Using a combination of retrograde tracers, optogenetic tools, and electrophysiological recordings, we show that VTA GABAergic axons make synaptic contacts in the granule cell layer of the dentate gyrus, where we can elicit small postsynaptic currents. Surprisingly, the currents displayed a partial sensitivity to both bicuculline and NBQX, suggesting that these mesohippocampal neurons corelease both GABA and glutamate. Finally, we show that this projection is functional in vivo and its stimulation reduces granule cell-firing rates under anesthesia. Altogether, the present results describe a novel connection between GABA and glutamate coreleasing of cells of the VTA and the dentate gyrus. This connection could be relevant for a variety of functions, including reward-related memory and neurogenesis.

Modulating synaptic NMDA receptors

Recent structural information on ligand-gated glutamate receptors and newly-discovered clinical uses for NMDA receptor antagonists has renewed interest in understanding the mechanisms of drug action at these receptors. Although the voltage-dependence and calcium permeability of NMDA receptors are well-studied, the mechanisms affecting the time course of synaptic NMDA receptor activation may be of more therapeutic value by serving as a rheostat for the total synaptic response. The NMDA receptor-mediated EPSC time course has been thought of as a fixed parameter based simply on receptor subunit composition as variably constrained by anatomical and developmental expression patterns, albeit subject to modification by kinetic behaviors such as modal gating. However, the EPSC time course also can be manipulated by endogenous and exogenous ligands. In this commentary we discuss insights into the in situ composition and kinetic behavior of synaptic NMDA receptors and propose new opportunities to target modulatory sites on NMDA receptors and to develop useful therapeutics. The emerging data on the atomic structure of NMDA receptors and knowledge of the kinetics of native receptors in neurons provide a roadmap in this regard. This article is part of the Special Issue entitled 'Ionotropic glutamate receptors'.

Spike synchrony generated by modulatory common input through NMDA-type synapses

Common excitatory input to neurons increases their firing rates and the strength of the spike correlation (synchrony) between them. Little is known, however, about the synchronizing effects of modulatory common input. Here, we show that modulatory common input with the slow synaptic kinetics of N-methyl-d-aspartate (NMDA) receptors enhances firing rates and also produces synchrony. Tight synchrony (correlations on the order of milliseconds) always increases with modulatory strength. Unexpectedly, the relationship between strength of modulation and strength of loose synchrony (tens of milliseconds) is not monotonic: The strongest loose synchrony is obtained for intermediate modulatory amplitudes. This finding explains recent neurophysiological results showing that in cortical areas V1 and V2, presumed modulatory top-down input due to contour grouping increases (loose and tight) synchrony but that additional modulatory input due to top-down attention does not change tight synchrony and actually decreases loose synchrony. These neurophysiological findings are understood from our model of integrate-and-fire neurons under the assumption that contour grouping as well as attention lead to additive modulatory common input through NMDA-type synapses. In contrast, circuits with common projections through model α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors did not exhibit the paradoxical decrease of synchrony with increased input. Our results suggest that NMDA receptors play a critical role in top-down response modulation in the visual cortex.

Inferring presynaptic population spiking from single-trial membrane potential recordings

BACKGROUND

The time-varying membrane potential of a cortical neuron contains important information about the network activity. Extracting this information requires separating excitatory and inhibitory synaptic inputs from single-trial membrane potential recordings without averaging across trials.

NEW METHOD

We propose a method to extract the time course of excitatory and inhibitory synaptic inputs to a neuron from a single-trial membrane potential recording. The method takes advantage of the differences in the time constants and the reversal potentials of the excitatory and inhibitory synaptic currents, which allows the untangling of the two conductance types.

RESULTS

We evaluate the applicability of the method on a leaky integrate-and-fire model neuron and find high quality of estimation of excitatory synaptic conductance changes and presynaptic population spikes. Application of the method to a real cortical neuron with known synaptic inputs in a brain slice returns high-quality estimation of the time course of the excitatory synaptic conductance. Application of the method to membrane potential recordings from a cortical pyramidal neuron of an intact brain reveals complex network activity.

COMPARISON WITH EXISTING METHODS

Existing methods are based on repeated trials and thus are limited to estimating the statistical features of synaptic conductance changes, or, when based on single trials, are limited to special cases, have low temporal resolution, or are impractically complicated.

CONCLUSIONS

We propose and test an efficient method for estimating the full time course of excitatory and inhibitory synaptic conductances from single-trial membrane potential recordings. The method is sufficiently simple to ensure widespread use in neuroscience.

Hunger States Control the Directions of Synaptic Plasticity via Switching Cell Type-Specific Subunits of NMDA Receptors

It remains largely unknown whether and how hunger states control activity-dependent synaptic plasticity, such as long-term potentiation (LTP) and long-term depression (LTD). We here report that both LTP and LTD of excitatory synaptic strength within the appetite control circuits residing in hypothalamic arcuate nucleus (ARC) behave in a manner of hunger states dependence and cell type specificity. For instance, we find that tetanic stimulation induces LTP at orexigenic agouti-related protein (AgRP) neurons in ad libitum fed mice, whereas it induces LTD in food-deprived mice. In an opposite direction, the same induction protocol induces LTD at anorexigenic pro-opiomelanocortin (POMC) neurons in fed mice but weak LTP in deprived mice. Mechanistically, we also find that food deprivation increases the expressions of NR2C/NR2D/NR3-containing NMDA receptors (NMDARs) at AgRP neurons that contribute to the inductions of LTD, whereas it decreases their expressions at POMC neurons. Collectively, our data reveal that hunger states control the directions of activity-dependent synaptic plasticity by switching NMDA receptor subpopulations in a cell type-specific manner, providing insights into NMDAR-mediated interactions between energy states and associative memory. Significance statement: Based on the experiments performed in this study, we demonstrate that activity-dependent synaptic plasticity is also under the control of energy states by regulating NMDAR subpopulations in a cell type-specific manner. We thus propose a reversible memory configuration constructed from energy states-dependent cell type-specific bidirectional conversions of LTP and LTD. Together with the distinct functional roles played by NMDAR signaling in the control of food intake and energy states, these findings reveal a new reciprocal interaction between energy states and associative memory, one that might serve as a target for therapeutic treatments of the energy-related memory disorders or vice versa.

Neuromechanistic Model of Auditory Bistability

Sequences of higher frequency A and lower frequency B tones repeating in an ABA- triplet pattern are widely used to study auditory streaming. One may experience either an integrated percept, a single ABA-ABA- stream, or a segregated percept, separate but simultaneous streams A-A-A-A- and -B---B--. During minutes-long presentations, subjects may report irregular alternations between these interpretations. We combine neuromechanistic modeling and psychoacoustic experiments to study these persistent alternations and to characterize the effects of manipulating stimulus parameters. Unlike many phenomenological models with abstract, percept-specific competition and fixed inputs, our network model comprises neuronal units with sensory feature dependent inputs that mimic the pulsatile-like A1 responses to tones in the ABA- triplets. It embodies a neuronal computation for percept competition thought to occur beyond primary auditory cortex (A1). Mutual inhibition, adaptation and noise are implemented. We include slow NDMA recurrent excitation for local temporal memory that enables linkage across sound gaps from one triplet to the next. Percepts in our model are identified in the firing patterns of the neuronal units. We predict with the model that manipulations of the frequency difference between tones A and B should affect the dominance durations of the stronger percept, the one dominant a larger fraction of time, more than those of the weaker percept—a property that has been previously established and generalized across several visual bistable paradigms. We confirm the qualitative prediction with our psychoacoustic experiments and use the behavioral data to further constrain and improve the model, achieving quantitative agreement between experimental and modeling results. Our work and model provide a platform that can be extended to consider other stimulus conditions, including the effects of context and volition.

Humans have an astonishing ability to separate out different sound sources in a busy room: think of how we can hear individual voices in a bustling coffee shop. Rather than voices, we use sound stimuli in the lab: repeating patterns of high and low tones. The tone sequences are ambiguous and can be interpreted in different ways—either grouped into a single stream, or separated out into different streams. When listening for a long time, one’s perception switches every few seconds, a phenomenon called auditory bistability. Based on knowledge of the organization of brain areas involved in separating out different sound sources and how neurons in these areas respond to the ambiguous sequences, we developed a computational model of auditory bistabilty. Our model is less abstract than existing models and shows how groups of neurons may compete in order to dictate what you perceive. We predict how the difference between the two tone sequences affects what you hear over time and we performed an experiment with human listeners to confirm our prediction. The model provides groundwork to further explore the way the brain deals with the busy and often ambiguous world of sound.

Glucose Transporter Type I Deficiency (G1D) at 25 (1990-2015): Presumptions, Facts, and the Lives of Persons With This Rare Disease

BACKGROUND

As is often the case for rare diseases, the number of published reviews and case reports of glucose transporter type I deficiency (G1D) approaches or exceeds that of original research. This can indicate medical interest, but also scientific stagnation.

METHODS

In assessing this state of affairs here, we focus not on what is peculiar or disparate about G1D, but on the assumptions that have reigned thus far undisputed, and critique them as a potential impediment to progress. To summarize the most common G1D phenotype, we trace the 25-year story of G1D in parallel with the natural history of one of two index patients, identified in 1990 by one of us (G.M.R.) and brought up to date by the other (J.M.P.) while later examining widely repeated but little-scrutinized statements. Among them are those that pertain to assumptions about brain fuels; energy failure; cerebrospinal glucose concentration; the purpose of ketogenic diet; the role of the defective blood-brain barrier; genotype-phenotype correlations; a bewildering array of phenotypes; ictogenesis, seizures, and the electroencephalograph; the use of mice to model the disorder; and what treatments may and may not be expected to accomplish.

RESULTS

We reach the forgone conclusion that the proper study of mankind-and of one of its ailments (G1D) -is man itself (rather than mice, isolated cells, or extrapolated inferences) and propose a framework for rigorous investigation that we hope will lead to a better understanding and to better treatments for this and for rare disorders in general.

CONCLUSIONS

These considerations, together with experience drawn from other disorders, lead, as a logical consequence, to the nullification of the view that therapeutic development (i.e., trials) for rare diseases could or should be accelerated without the most vigorous scientific scrutiny: trial and error constitute an inseparable couple, such that, at the present time, hastening the former is bound to precipitate the latter.

Invariant visual object recognition: biologically plausible approaches

Key properties of inferior temporal cortex neurons are described, and then, the biological plausibility of two leading approaches to invariant visual object recognition in the ventral visual system is assessed to investigate whether they account for these properties. Experiment 1 shows that VisNet performs object classification with random exemplars comparably to HMAX, except that the final layer C neurons of HMAX have a very non-sparse representation (unlike that in the brain) that provides little information in the single-neuron responses about the object class. Experiment 2 shows that VisNet forms invariant representations when trained with different views of each object, whereas HMAX performs poorly when assessed with a biologically plausible pattern association network, as HMAX has no mechanism to learn view invariance. Experiment 3 shows that VisNet neurons do not respond to scrambled images of faces, and thus encode shape information. HMAX neurons responded with similarly high rates to the unscrambled and scrambled faces, indicating that low-level features including texture may be relevant to HMAX performance. Experiment 4 shows that VisNet can learn to recognize objects even when the view provided by the object changes catastrophically as it transforms, whereas HMAX has no learning mechanism in its S-C hierarchy that provides for view-invariant learning. This highlights some requirements for the neurobiological mechanisms of high-level vision, and how some different approaches perform, in order to help understand the fundamental underlying principles of invariant visual object recognition in the ventral visual stream.

Dendritic sodium spikes are required for long-term potentiation at distal synapses on hippocampal pyramidal neurons

Dendritic integration of synaptic inputs mediates rapid neural computation as well as longer-lasting plasticity. Several channel types can mediate dendritically initiated spikes (dSpikes), which may impact information processing and storage across multiple timescales; however, the roles of different channels in the rapid vs long-term effects of dSpikes are unknown. We show here that dSpikes mediated by Nav channels (blocked by a low concentration of TTX) are required for long-term potentiation (LTP) in the distal apical dendrites of hippocampal pyramidal neurons. Furthermore, imaging, simulations, and buffering experiments all support a model whereby fast Nav channel-mediated dSpikes (Na-dSpikes) contribute to LTP induction by promoting large, transient, localized increases in intracellular calcium concentration near the calcium-conducting pores of NMDAR and L-type Cav channels. Thus, in addition to contributing to rapid neural processing, Na-dSpikes are likely to contribute to memory formation via their role in long-lasting synaptic plasticity.

Understanding the nonlinear physiological and behavioral effects of tDCS through computational neurostimulation

Despite the success of noninvasive brain stimulation (NIBS), the mechanism of action through which different stimulation techniques interact with information processing in targeted neural circuits largely remains unknown. Applying neurostimulation in silico to computational models with biophysical plausibility provides one route to interrogate the possible mechanisms through which stimulation interacts with neural circuits, and generate predictions about the resultant behavior. Here, we address the recent observation that the physiological and behavioral effects of transcranial direct current stimulation (tDCS) might be nonlinear with regard to stimulation intensity or duration. We simulate neurostimulation in an established, biophysically informed neural network attractor model that generates simple behavioral choices and thus allows for assessing the impact of stimulation on both neural dynamics and behavior. We demonstrate that nonlinear effects of stimulation intensity on the accuracy and decision time of the model can arise from a limit on the integration rate of the network, nonlinear effects of stimulation on neural firing rates before the onset of the stimulus, and the inhibitory effect of hyperpolarizing stimulation on pyramidal neurons. We thus present a detailed modeling treatment of nonlinear tDCS effects during a behavioral task, and provide detailed hypotheses about the neural causes that lead to observed nonlinear behavioral effects during stimulation. This framework can provide a blueprint for future work on the neural and behavioral consequences of NIBS in health and disease.

Spike synchrony reveals emergence of proto-objects in visual cortex

Neurons at early stages of the visual cortex signal elemental features, such as pieces of contour, but how these signals are organized into perceptual objects is unclear. Theories have proposed that spiking synchrony between these neurons encodes how features are grouped (binding-by-synchrony), but recent studies did not find the predicted increase in synchrony with binding. Here we propose that features are grouped to "proto-objects" by intrinsic feedback circuits that enhance the responses of the participating feature neurons. This hypothesis predicts synchrony exclusively between feature neurons that receive feedback from the same grouping circuit. We recorded from neurons in macaque visual cortex and used border-ownership selectivity, an intrinsic property of the neurons, to infer whether or not two neurons are part of the same grouping circuit. We found that binding produced synchrony between same-circuit neurons, but not between other pairs of neurons, as predicted by the grouping hypothesis. In a selective attention task, synchrony emerged with ignored as well as attended objects, and higher synchrony was associated with faster behavioral responses, as would be expected from early grouping mechanisms that provide the structure for object-based processing. Thus, synchrony could be produced by automatic activation of intrinsic grouping circuits. However, the binding-related elevation of synchrony was weak compared with its random fluctuations, arguing against synchrony as a code for binding. In contrast, feedback grouping circuits encode binding by modulating the response strength of related feature neurons. Thus, our results suggest a novel coding mechanism that might underlie the proto-objects of perception.

Networks for memory, perception, and decision-making, and beyond to how the syntax for language might be implemented in the brain

Neural principles that provide a foundation for memory, perception, and decision-making include place coding with sparse distributed representations, associative synaptic modification, and attractor networks in which the storage capacity is in the order of the number of associatively modifiable recurrent synapses on any one neuron. Based on those and further principles of cortical computation, hypotheses are explored in which syntax is encoded in the cortex using sparse distributed place coding. Each cortical module 2-3 mm in diameter is proposed to be formed of a local attractor neuronal network with a capacity in the order of 10,000 words (e.g. subjects, verbs or objects depending on the module). Such a system may form a deep language-of-thought layer. For the information to be communicated to other people, the modules in which the neurons are firing which encode the syntactic role, as well as which neurons are firing to specify the words, must be communicated. It is proposed that one solution to this (used in English) is temporal order encoding, for example subject-verb-object. It is shown with integrate-and-fire simulations that this order encoding could be implemented by weakly forward-coupled subject-verb-object modules. A related system can decode a temporal sequence. This approach based on known principles of cortical computation needs to be extended to investigate further whether it could form a biological foundation for the implementation of language in the brain. This article is part of a Special Issue entitled SI: Brain and Memory.

Inter-synaptic learning of combination rules in a cortical network model

Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons. Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network.

Finding and recognizing objects in natural scenes: complementary computations in the dorsal and ventral visual systems

Searching for and recognizing objects in complex natural scenes is implemented by multiple saccades until the eyes reach within the reduced receptive field sizes of inferior temporal cortex (IT) neurons. We analyze and model how the dorsal and ventral visual streams both contribute to this. Saliency detection in the dorsal visual system including area LIP is modeled by graph-based visual saliency, and allows the eyes to fixate potential objects within several degrees. Visual information at the fixated location subtending approximately 9° corresponding to the receptive fields of IT neurons is then passed through a four layer hierarchical model of the ventral cortical visual system, VisNet. We show that VisNet can be trained using a synaptic modification rule with a short-term memory trace of recent neuronal activity to capture both the required view and translation invariances to allow in the model approximately 90% correct object recognition for 4 objects shown in any view across a range of 135° anywhere in a scene. The model was able to generalize correctly within the four trained views and the 25 trained translations. This approach analyses the principles by which complementary computations in the dorsal and ventral visual cortical streams enable objects to be located and recognized in complex natural scenes.

Deformation-specific and deformation-invariant visual object recognition: pose vs. identity recognition of people and deforming objects

When we see a human sitting down, standing up, or walking, we can recognize one of these poses independently of the individual, or we can recognize the individual person, independently of the pose. The same issues arise for deforming objects. For example, if we see a flag deformed by the wind, either blowing out or hanging languidly, we can usually recognize the flag, independently of its deformation; or we can recognize the deformation independently of the identity of the flag. We hypothesize that these types of recognition can be implemented by the primate visual system using temporo-spatial continuity as objects transform as a learning principle. In particular, we hypothesize that pose or deformation can be learned under conditions in which large numbers of different people are successively seen in the same pose, or objects in the same deformation. We also hypothesize that person-specific representations that are independent of pose, and object-specific representations that are independent of deformation and view, could be built, when individual people or objects are observed successively transforming from one pose or deformation and view to another. These hypotheses were tested in a simulation of the ventral visual system, VisNet, that uses temporal continuity, implemented in a synaptic learning rule with a short-term memory trace of previous neuronal activity, to learn invariant representations. It was found that depending on the statistics of the visual input, either pose-specific or deformation-specific representations could be built that were invariant with respect to individual and view; or that identity-specific representations could be built that were invariant with respect to pose or deformation and view. We propose that this is how pose-specific and pose-invariant, and deformation-specific and deformation-invariant, perceptual representations are built in the brain.

Reliable neuronal systems: the importance of heterogeneity

For every engineer it goes without saying: in order to build a reliable system we need components that consistently behave precisely as they should. It is also well known that neurons, the building blocks of brains, do not satisfy this constraint. Even neurons of the same type come with huge variances in their properties and these properties also vary over time. Synapses, the connections between neurons, are highly unreliable in forwarding signals. In this paper we argue that both these fact add variance to neuronal processes, and that this variance is not a handicap of neural systems, but that instead predictable and reliable functional behavior of neural systems depends crucially on this variability. In particular, we show that higher variance allows a recurrently connected neural population to react more sensitively to incoming signals, and processes them faster and more energy efficient. This, for example, challenges the general assumption that the intrinsic variability of neurons in the brain is a defect that has to be overcome by synaptic plasticity in the process of learning.

Discharge-rate persistence of baseline activity during fixation reflects maintenance of memory-period activity in the macaque posterior parietal cortex

Recent evidence has demonstrated that spatiotemporal patterns of spontaneous activity reflect the patterns of activity evoked by sensory stimuli. However, few studies have examined whether response profiles of task-evoked activity, which is not related to external sensory stimuli but rather to internal processes, are also reflected in those of spontaneous activity. To address this, we recorded activity of neurons in the lateral intraparietal area (LIP) when monkeys performed reaction-time and delayed-response visual-search tasks. We particularly focused on the target location-dependent modulation of delay-period activity (delay-period modulation) in the delayed-response task, and the discharge-rate persistency in fixation-period activity (baseline-activity maintenance) in the reaction-time task. Baseline-activity maintenance was assessed by the correlation between the spike counts of 2 separate bins. We found that baseline-activity maintenance, calculated from bins separated by a long interval (200-500 ms), was correlated with delay-period modulation, whereas that calculated from bins separated by a short interval (~100 ms) was correlated with trial-to-trial fluctuations in baseline activity, suggesting a link between the capability to hold task-related information in delay-period activity and the degree of baseline-activity maintenance in a timescale-dependent manner.