Neuronal arithmetic

Neuronal Population Activity in Spinal Motor Circuits: Greater Than the Sum of Its Parts

The core elements of stereotypical movements such as locomotion, scratching and breathing are generated by networks in the lower brainstem and the spinal cord. Ensemble activities in spinal motor networks had until recently been merely a black box, but with the emergence of ultra-thin Silicon multi-electrode technology it was possible to reveal the spiking activity of larger parts of the network. A series of experiments revealed unexpected features of spinal networks, such as multiple spiking regimes and lognormal firing rate distributions. The lognormality renders the widespread idea of a typical firing rate ± standard deviation an ill-suited description, and therefore these findings define a new arithmetic of motor networks. Focusing on the population activity behind motor pattern generation this review summarizes this advance and discusses its implications.

Differential processing in modality-specific Mauthner cell dendrites

KEY POINTS

The present study examines dendritic integrative processes that occur in many central neurons but have been challenging to study in vivo in the vertebrate brain. The Mauthner cell of goldfish receives auditory and visual information via two separate dendrites, providing a privileged scenario for in vivo examination of dendritic integration. The results show differential attenuation properties in the Mauthner cell dendrites arising at least partly from differences in cable properties and the nonlinear behaviour of the respective dendritic membranes. In addition to distinct modality-dependent membrane specialization in neighbouring dendrites of the Mauthner cell, we report cross-modal dendritic interactions via backpropagating postsynaptic potentials. Broadly, the results of the present study provide an exceptional example for the processing power of single neurons.

ABSTRACT

Animals process multimodal information for adaptive behavioural decisions. In fish, evasion of a diving bird that breaks the water surface depends on integrating visual and auditory stimuli with very different characteristics. How do neurons process such differential sensory inputs at the dendritic level? For that, we studied the Mauthner cells (M-cells) in the goldfish startle circuit, which receive visual and auditory inputs via two separate dendrites, both accessible for in vivo recordings. We investigated whether electrophysiological membrane properties and dendrite morphology, studied in vivo, play a role in selective sensory processing in the M-cell. The results obtained show that anatomical and electrophysiological differences between the dendrites combine to produce stronger attenuation of visually evoked postsynaptic potentials (PSPs) than to auditory evoked PSPs. Interestingly, our recordings showed also cross-modal dendritic interaction because auditory evoked PSPs invade the ventral dendrite (VD), as well as the opposite where visual PSPs invade the lateral dendrite (LD). However, these interactions were asymmetrical, with auditory PSPs being more prominent in the VD than visual PSPs in the LD. Modelling experiments imply that this asymmetry is caused by active conductances expressed in the proximal segments of the VD. The results obtained in the present study suggest modality-dependent membrane specialization in M-cell dendrites suited for processing stimuli of different time domains and, more broadly, provide a compelling example of information processing in single neurons.

Is more always better? How different 'doses' of exercise after incomplete spinal cord injury affects the membrane properties of deep dorsal horn interneurons

Interneurons in the deep dorsal horn (DDH) of the spinal cord process somatosensory input, and form an important link between upper and lower motoneurons to subsequently shape motor output. Exercise training after SCI is known to improve functional motor recovery, but little is known about the mechanisms within spinal cord neurons that underlie these improvements. Here we investigate how the properties of DDH interneurons are affected by spinal cord injury (SCI) alone, and SCI in combination with different 'doses' of treadmill exercise training (3, 6, and 9wks). In an adult mouse hemisection model of SCI we used whole-cell patch-clamp electrophysiology to record intrinsic, AP firing and gain modulation properties from DDH interneurons in a horizontal spinal cord slice preparation. We find that neurons within two segments of the injury, both ipsi- and contralateral to the hemisection, are similarly affected by SCI and SCI plus exercise. The passive intrinsic membrane properties input resistance (R_in) and rheobase are sensitive to the effects of recovery time and exercise training after SCI thus altering DDH interneuron excitability. Conversely, select active membrane properties are largely unaffected by either SCI or exercise training. SCI itself causes a mismatch in the expression of voltage-gated subthreshold currents and AP discharge firing type. Over time after SCI, and especially with exercise training (9wks), this mismatched expression is exacerbated. Lastly, amplification properties (i.e. gain of frequency-current relationship) of DDH interneurons are altered by SCI alone and recover spontaneously with no clear effect of exercise training. These results suggest a larger 'dose' of exercise training (9wks) has a strong and selective effect on specific membrane properties, and on the output of interneurons in the vicinity of a SCI. These electrophysiological data provide new insights into the plasticity of DDH interneurons and the mechanisms by which exercise therapy after SCI can improve recovery.

"Silent" NMDA Synapses Enhance Motion Sensitivity in a Mature Retinal Circuit

Retinal direction-selective ganglion cells (DSGCs) have the remarkable ability to encode motion over a wide range of contrasts, relying on well-coordinated excitation and inhibition (E/I). E/I is orchestrated by a diverse set of glutamatergic bipolar cells that drive DSGCs directly, as well as indirectly through feedforward GABAergic/cholinergic signals mediated by starburst amacrine cells. Determining how direction-selective responses are generated across varied stimulus conditions requires understanding how glutamate, acetylcholine, and GABA signals are precisely coordinated. Here, we use a combination of paired patch-clamp recordings, serial EM, and large-scale multi-electrode array recordings to show that a single high-sensitivity source of glutamate is processed differentially by starbursts via AMPA receptors and DSGCs via NMDA receptors. We further demonstrate how this novel synaptic arrangement enables DSGCs to encode direction robustly near threshold contrasts. Together, these results reveal a space-efficient synaptic circuit model for direction computations, in which "silent" NMDA receptors play critical roles.

Cortical neurons and networks are dormant but fully responsive during isoelectric brain state

A continuous isoelectric electroencephalogram reflects an interruption of endogenously-generated activity in cortical networks and systematically results in a complete dissolution of conscious processes. This electro-cerebral inactivity occurs during various brain disorders, including hypothermia, drug intoxication, long-lasting anoxia and brain trauma. It can also be induced in a therapeutic context, following the administration of high doses of barbiturate-derived compounds, to interrupt a hyper-refractory status epilepticus. Although altered sensory responses can be occasionally observed on an isoelectric electroencephalogram, the electrical membrane properties and synaptic responses of individual neurons during this cerebral state remain largely unknown. The aim of the present study was to characterize the intracellular correlates of a barbiturate-induced isoelectric electroencephalogram and to analyse the sensory-evoked synaptic responses that can emerge from a brain deprived of spontaneous electrical activity. We first examined the sensory responsiveness from patients suffering from intractable status epilepticus and treated by administration of thiopental. Multimodal sensory responses could be evoked on the flat electroencephalogram, including visually-evoked potentials that were significantly amplified and delayed, with a high trial-to-trial reproducibility compared to awake healthy subjects. Using an analogous pharmacological procedure to induce prolonged electro-cerebral inactivity in the rat, we could describe its cortical and subcortical intracellular counterparts. Neocortical, hippocampal and thalamo-cortical neurons were all silent during the isoelectric state and displayed a flat membrane potential significantly hyperpolarized compared with spontaneously active control states. Nonetheless, all recorded neurons could fire action potentials in response to intracellularly injected depolarizing current pulses and their specific intrinsic electrophysiological features were preserved. Manipulations of the membrane potential and intracellular injection of chloride in neocortical neurons failed to reveal an augmented synaptic inhibition during the isoelectric condition. Consistent with the sensory responses recorded from comatose patients, large and highly reproducible somatosensory-evoked potentials could be generated on the inactive electrocorticogram in rats. Intracellular recordings revealed that the underlying neocortical pyramidal cells responded to sensory stimuli by complex synaptic potentials able to trigger action potentials. As in patients, sensory responses in the isoelectric state were delayed compared to control responses and exhibited an elevated reliability during repeated stimuli. Our findings demonstrate that during prolonged isoelectric brain state neurons and synaptic networks are dormant rather than excessively inhibited, conserving their intrinsic properties and their ability to integrate and propagate environmental stimuli.

GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits

Cortical networks are composed of glutamatergic excitatory projection neurons and local GABAergic inhibitory interneurons that gate signal flow and sculpt network dynamics. Although they represent a minority of the total neocortical neuronal population, GABAergic interneurons are highly heterogeneous, forming functional classes based on their morphological, electrophysiological, and molecular features, as well as connectivity and in vivo patterns of activity. Here we review our current understanding of neocortical interneuron diversity and the properties that distinguish cell types. We then discuss how the involvement of multiple cell types, each with a specific set of cellular properties, plays a crucial role in diversifying and increasing the computational power of a relatively small number of simple circuit motifs forming cortical networks. We illustrate how recent advances in the field have shed light onto the mechanisms by which GABAergic inhibition contributes to network operations.

Cell-type-specific inhibition of the dendritic plateau potential in striatal spiny projection neurons

Striatal spiny projection neurons (SPNs) receive convergent excitatory synaptic inputs from the cortex and thalamus. Activation of spatially clustered and temporally synchronized excitatory inputs at the distal dendrites could trigger plateau potentials in SPNs. Such supralinear synaptic integration is crucial for dendritic computation. However, how plateau potentials interact with subsequent excitatory and inhibitory synaptic inputs remains unknown. By combining computational simulation, two-photon imaging, optogenetics, and dual-color uncaging of glutamate and GABA, we demonstrate that plateau potentials can broaden the spatiotemporal window for integrating excitatory inputs and promote spiking. The temporal window of spiking can be delicately controlled by GABAergic inhibition in a cell-type-specific manner. This subtle inhibitory control of plateau potential depends on the location and kinetics of the GABAergic inputs and is achieved by the balance between relief and reestablishment of NMDA receptor Mg²⁺ block. These findings represent a mechanism for controlling spatiotemporal synaptic integration in SPNs.

Differential Dendritic Integration of Synaptic Potentials and Calcium in Cerebellar Interneurons

Dendritic voltage integration determines the transformation of synaptic inputs into output firing, while synaptic calcium integration drives plasticity mechanisms thought to underlie memory storage. Dendritic calcium integration has been shown to follow the same synaptic input-output relationship as dendritic voltage, but whether similar operations apply to neurons exhibiting sublinear voltage integration is unknown. We examined the properties and cellular mechanisms of these dendritic operations in cerebellar molecular layer interneurons using dendritic voltage and calcium imaging, in combination with synaptic stimulation or glutamate uncaging. We show that, while synaptic potentials summate sublinearly, concomitant dendritic calcium signals summate either linearly or supralinearly depending on the number of synapses activated. The supralinear dendritic calcium triggers a branch-specific, short-term suppression of neurotransmitter release that alters the pattern of synaptic activation. Thus, differential voltage and calcium integration permits dynamic regulation of neuronal input-output transformations without altering intrinsic nonlinear integration mechanisms.

Npas1+ Pallidal Neurons Target Striatal Projection Neurons

Compelling evidence demonstrates that the external globus pallidus (GPe) plays a key role in processing sensorimotor information. An anatomical projection from the GPe to the dorsal striatum has been described for decades. However, the cellular target and functional impact of this projection remain unknown. Using cell-specific transgenic mice, modern monosynaptic tracing techniques, and optogenetics-based mapping, we discovered that GPe neurons provide inhibitory inputs to direct and indirect pathway striatal projection neurons (SPNs). Our results indicate that the GPe input to SPNs arises primarily from Npas1-expressing neurons and is strengthened in a chronic Parkinson's disease (PD) model. Alterations of the GPe-SPN input in a PD model argue for the critical position of this connection in regulating basal ganglia motor output and PD symptomatology. Finally, chemogenetic activation of Npas1-expressing GPe neurons suppresses motor output, arguing that strengthening of the GPe-SPN connection is maladaptive and may underlie the hypokinetic symptoms in PD.

SIGNIFICANCE STATEMENT

An anatomical projection from the pallidum to the striatum has been described for decades, but little is known about its connectivity pattern. The authors dissect the presynaptic and postsynaptic neurons involved in this projection, and show its cell-specific remodeling and strengthening in parkinsonian mice. Chemogenetic activation of Npas1(+) pallidal neurons that give rise to the principal pallidostriatal projection increases the time that the mice spend motionless. This argues that maladaptive strengthening of this connection underlies the paucity of volitional movements, which is a hallmark of Parkinson's disease.

Developmental and genetic mechanisms of neural circuit evolution

Regardless of how a nervous system is genetically built, natural selection is acting on the functional outcome of its activity. To understand how nervous systems evolve, it is essential to analyze how their functional units - the neural circuits - change and adapt over time. A neural circuit can evolve in many different ways, and the underlying developmental and genetic mechanisms involve different sets of genes. Therefore, the comparison of gene expression can help reconstructing circuit evolution, as demonstrated by several examples in sensory systems. Functional constraints on neural circuit evolution suggest that in nervous systems developmental and genetic variants do not appear randomly, and that the evolution of neuroanatomy might be biased. Sensory systems, in particular, seem to evolve along trajectories that enhance their evolvability, ensuring adaptation to different environments.

Attentional modulation of neuronal variability in circuit models of cortex

The circuit mechanisms behind shared neural variability (noise correlation) and its dependence on neural state are poorly understood. Visual attention is well-suited to constrain cortical models of response variability because attention both increases firing rates and their stimulus sensitivity, as well as decreases noise correlations. We provide a novel analysis of population recordings in rhesus primate visual area V4 showing that a single biophysical mechanism may underlie these diverse neural correlates of attention. We explore model cortical networks where top-down mediated increases in excitability, distributed across excitatory and inhibitory targets, capture the key neuronal correlates of attention. Our models predict that top-down signals primarily affect inhibitory neurons, whereas excitatory neurons are more sensitive to stimulus specific bottom-up inputs. Accounting for trial variability in models of state dependent modulation of neuronal activity is a critical step in building a mechanistic theory of neuronal cognition.

DOI:http://dx.doi.org/10.7554/eLife.23978.001

The world around us is complex and our brains need to navigate this complexity. We must focus on relevant inputs from our senses – such as the bus we need to catch – while ignoring distractions – such as the eye-catching displays in the shop windows we pass on the same street. Selective attention is a tool that enables us to filter complex sensory scenes and focus on whatever is most important at the time. But how does selective attention work?

Our sense of vision results from the activity of cells in a region of the brain called visual cortex. Paying attention to an object affects the activity of visual cortex in two ways. First, it causes the average activity of the brain cells in the visual cortex that respond to that object to increase. Second, it reduces spontaneous moment-to-moment fluctuations in the activity of those brain cells, known as noise. Both of these effects make it easier for the brain to process the object in question.

Kanashiro et al. set out to build a mathematical model of visual cortex that captures these two components of selective attention. The cortex contains two types of brain cells: excitatory neurons, which activate other cells, and inhibitory neurons, which suppress other cells. Experiments suggest that excitatory neurons contribute to the flow of activity within the cortex, whereas inhibitory neurons help cancel out noise. The new mathematical model predicts that paying attention affects inhibitory neurons far more than excitatory ones. According to the model, selective attention works mainly by reducing the noise that would otherwise distort the activity of visual cortex.

The next step is to test this prediction directly. This will require measuring the activity of the inhibitory neurons in an animal performing a selective attention task. Such experiments, which should be achievable using existing technologies, will allow scientists to confirm or disprove the current model, and to dissect the mechanisms that underlie visual attention.

DOI:http://dx.doi.org/10.7554/eLife.23978.002

Neural Circuitry of Reward Prediction Error

Dopamine neurons facilitate learning by calculating reward prediction error, or the difference between expected and actual reward. Despite two decades of research, it remains unclear how dopamine neurons make this calculation. Here we review studies that tackle this problem from a diverse set of approaches, from anatomy to electrophysiology to computational modeling and behavior. Several patterns emerge from this synthesis: that dopamine neurons themselves calculate reward prediction error, rather than inherit it passively from upstream regions; that they combine multiple separate and redundant inputs, which are themselves interconnected in a dense recurrent network; and that despite the complexity of inputs, the output from dopamine neurons is remarkably homogeneous and robust. The more we study this simple arithmetic computation, the knottier it appears to be, suggesting a daunting (but stimulating) path ahead for neuroscience more generally.

Medial Ganglionic Eminence Progenitors Transplanted into Hippocampus Integrate in a Functional and Subtype-Appropriate Manner

Medial ganglionic eminence (MGE) transplantation rescues disease phenotypes in various preclinical models with interneuron deficiency or dysfunction, including epilepsy. While underlying mechanism(s) remains unclear to date, a simple explanation is that appropriate synaptic integration of MGE-derived interneurons elevates GABA-mediated inhibition and modifies the firing activity of excitatory neurons in the host brain. However, given the complexity of interneurons and potential for transplant-derived interneurons to integrate or alter the host network in unexpected ways, it remains unexplored whether synaptic connections formed by transplant-derived interneurons safely mirror those associated with endogenous interneurons. Here, we combined optogenetics, interneuron-specific Cre driver mouse lines, and electrophysiology to study synaptic integration of MGE progenitors. We demonstrated that MGE-derived interneurons, when transplanted into the hippocampus of neonatal mice, migrate in the host brain, differentiate to mature inhibitory interneurons, and form appropriate synaptic connections with native pyramidal neurons. Endogenous and transplant-derived MGE progenitors preferentially formed inhibitory synaptic connections onto pyramidal neurons but not endogenous interneurons. These findings demonstrate that transplanted MGE progenitors functionally integrate into the postnatal hippocampal network.

Pattern separation in the hippocampus through the eyes of computational modeling

Pattern separation is a mnemonic process that has been extensively studied over the years. It entails the ability -of primarily hippocampal circuits- to distinguish between highly similar inputs, via generating different neuronal activity (output) patterns. The dentate gyrus (DG) in particular has long been hypothesized to implement pattern separation by detecting and storing similar inputs as distinct representations. The ways in which these distinct representations can be generated have been explored in a number of theoretical and computational modeling studies. Here, we review two categories of pattern separation models: those that address the phenomenon in an abstract mathematical fashion and those that delve into the underlying biological mechanisms by taking into account the anatomy and/or physiology of hippocampal circuits. We summarize the strategies, findings and limitations of these modeling approaches in the light of new experimental findings and propose a unifying framework whereby different network, cellular and sub-cellular mechanisms converge to a common goal: controlling sparsity, the key determinant of pattern separation in the DG.

The GABAergic Hypothesis for Cognitive Disabilities in Down Syndrome

Down syndrome (DS) is a genetic disorder caused by the presence of a third copy of chromosome 21. DS affects multiple organs, but it invariably results in altered brain development and diverse degrees of intellectual disability. A large body of evidence has shown that synaptic deficits and memory impairment are largely determined by altered GABAergic signaling in trisomic mouse models of DS. These alterations arise during brain development while extending into adulthood, and include genesis of GABAergic neurons, variation of the inhibitory drive and modifications in the control of neural-network excitability. Accordingly, different pharmacological interventions targeting GABAergic signaling have proven promising preclinical approaches to rescue cognitive impairment in DS mouse models. In this review, we will discuss recent data regarding the complex scenario of GABAergic dysfunctions in the trisomic brain of DS mice and patients, and we will evaluate the state of current clinical research targeting GABAergic signaling in individuals with DS.

Autaptic Connections Shift Network Excitability and Bursting

We examine the role of structural autapses, when a neuron synapses onto itself, in driving network-wide bursting behavior. Using a simple spiking model of neuronal activity, we study how autaptic connections affect activity patterns, and evaluate if controllability significantly affects changes in bursting from autaptic connections. Adding more autaptic connections to excitatory neurons increased the number of spiking events and the number of network-wide bursts. We observed excitatory synapses contributed more to bursting behavior than inhibitory synapses. We evaluated if neurons with high average controllability, predicted to push the network into easily achievable states, affected bursting behavior differently than neurons with high modal controllability, thought to influence the network into difficult to reach states. Results show autaptic connections to excitatory neurons with high average controllability led to higher burst frequencies than adding the same number of self-looping connections to neurons with high modal controllability. The number of autapses required to induce bursting was lowered by adding autapses to high degree excitatory neurons. These results suggest a role of autaptic connections in controlling network-wide bursts in diverse cortical and subcortical regions of mammalian brain. Moreover, they open up new avenues for the study of dynamic neurophysiological correlates of structural controllability.

Inhibition in the auditory brainstem enhances signal representation and regulates gain in complex acoustic environments

Inhibition plays a crucial role in neural signal processing, shaping and limiting responses. In the auditory system, inhibition already modulates second order neurons in the cochlear nucleus, e.g. spherical bushy cells (SBCs). While the physiological basis of inhibition and excitation is well described, their functional interaction in signal processing remains elusive. Using a combination of in vivo loose-patch recordings, iontophoretic drug application, and detailed signal analysis in the Mongolian Gerbil, we demonstrate that inhibition is widely co-tuned with excitation, and leads only to minor sharpening of the spectral response properties. Combinations of complex stimuli and neuronal input-output analysis based on spectrotemporal receptive fields revealed inhibition to render the neuronal output temporally sparser and more reproducible than the input. Overall, inhibition plays a central role in improving the temporal response fidelity of SBCs across a wide range of input intensities and thereby provides the basis for high-fidelity signal processing.

Lognormal firing rate distribution reveals prominent fluctuation-driven regime in spinal motor networks

When spinal circuits generate rhythmic movements it is important that the neuronal activity remains within stable bounds to avoid saturation and to preserve responsiveness. Here, we simultaneously record from hundreds of neurons in lumbar spinal circuits of turtles and establish the neuronal fraction that operates within either a 'mean-driven' or a 'fluctuation-driven' regime. Fluctuation-driven neurons have a 'supralinear' input-output curve, which enhances sensitivity, whereas the mean-driven regime reduces sensitivity. We find a rich diversity of firing rates across the neuronal population as reflected in a lognormal distribution and demonstrate that half of the neurons spend at least 50 % of the time in the 'fluctuation-driven' regime regardless of behavior. Because of the disparity in input-output properties for these two regimes, this fraction may reflect a fine trade-off between stability and sensitivity in order to maintain flexibility across behaviors.

Plasticity-dependent, full detonation at hippocampal mossy fiber-CA3 pyramidal neuron synapses

Mossy fiber synapses on CA3 pyramidal cells are 'conditional detonators' that reliably discharge postsynaptic targets. The 'conditional' nature implies that burst activity in dentate gyrus granule cells is required for detonation. Whether single unitary excitatory postsynaptic potentials (EPSPs) trigger spikes in CA3 neurons remains unknown. Mossy fiber synapses exhibit both pronounced short-term facilitation and uniquely large post-tetanic potentiation (PTP). We tested whether PTP could convert mossy fiber synapses from subdetonator into detonator mode, using a recently developed method to selectively and noninvasively stimulate individual presynaptic terminals in rat brain slices. Unitary EPSPs failed to initiate a spike in CA3 neurons under control conditions, but reliably discharged them after induction of presynaptic short-term plasticity. Remarkably, PTP switched mossy fiber synapses into full detonators for tens of seconds. Plasticity-dependent detonation may be critical for efficient coding, storage, and recall of information in the granule cell-CA3 cell network.

Rapid Sensory Adaptation Redux: A Circuit Perspective

Adaptation is fundamental to life. All organisms adapt over timescales that span from evolution to generations and lifetimes to moment-by-moment interactions. The nervous system is particularly adept at rapidly adapting to change, and this in fact may be one of its fundamental principles of organization and function. Rapid forms of sensory adaptation have been well documented across all sensory modalities in a wide range of organisms, yet we do not have a comprehensive understanding of the adaptive cellular mechanisms that ultimately give rise to the corresponding percepts, due in part to the complexity of the circuitry. In this Perspective, we aim to build links between adaptation at multiple scales of neural circuitry by investigating the differential adaptation across brain regions and sub-regions and across specific cell types, for which the explosion of modern tools has just begun to enable. This investigation points to a set of challenges for the field to link functional observations to adaptive properties of the neural circuit that ultimately underlie percepts.

Artificial Synapses Based on in-Plane Gate Organic Electrochemical Transistors

Realization of biological synapses using electronic devices is regarded as the basic building blocks for neuromorphic engineering and artificial neural network. With the advantages of biocompatibility, low cost, flexibility, and compatible with printing and roll-to-roll processes, the artificial synapse based on organic transistor is of great interest. In this paper, the artificial synapse simulation by ion-gel gated organic field-effect transistors (FETs) with poly(3-hexylthiophene) (P3HT) active channel is demonstrated. Key features of the synaptic behaviors, such as paired-pulse facilitation (PPF), short-term plasticity (STP), self-tuning, the spike logic operation, spatiotemporal dentritic integration, and modulation are successfully mimicked. Furthermore, the interface doping processes of electrolyte ions between the active P3HT layer and ion gels is comprehensively studied for confirming the operating processes underlying the conductivity and excitatory postsynaptic current (EPSC) variations in the organic synaptic devices. This study represents an important step toward building future artificial neuromorphic systems with newly emerged ion gel gated organic synaptic devices.

The influence of increased membrane conductance on response properties of spinal motoneurons

During functional spinal neural network activity motoneurons receive massive synaptic excitation and inhibition, and their membrane conductance increases considerably - they are switched to a high-conductance state. High-conductance states can substantially alter response properties of motoneurons. In the present study we investigated how an increase in membrane conductance affects spike frequency adaptation, the gain (i.e., the slope of the frequency-current relationship) and the threshold for action potential generation. We used intracellular recordings from adult turtle motoneurons in spinal cord slices. Membrane conductance was increased pharmacologically by extracellular application of the GABAA receptor agonist muscimol. Our findings suggest that an increase in membrane conductance of about 40-50% increases the magnitude of spike frequency adaptation, but does not change the threshold for action potential generation. Increased conductance causes a subtractive rather than a divisive effect on the initial and the early frequency-current relationships and may have not only a subtractive but also a divisive effect on the steady-state frequency-current relationship.

Neural Mechanism to Simulate a Scale-Invariant Future

Predicting the timing and order of future events is an essential feature of cognition in higher life forms. We propose a neural mechanism to nondestructively translate the current state of spatiotemporal memory into the future, so as to construct an ordered set of future predictions almost instantaneously. We hypothesize that within each cycle of hippocampal theta oscillations, the memory state is swept through a range of translations to yield an ordered set of future predictions through modulations in synaptic connections. Theoretically, we operationalize critical neurobiological findings from hippocampal physiology in terms of neural network equations representing spatiotemporal memory. Combined with constraints based on physical principles requiring scale invariance and coherence in translation across memory nodes, the proposition results in Weber-Fechner spacing for the representation of both past (memory) and future (prediction) timelines. We show that the phenomenon of phase precession of neurons in the hippocampus and ventral striatum correspond to the cognitive act of future prediction.

Commissural Gain Control Enhances the Midbrain Representation of Sound Location

Accurate localization of sound sources is essential for survival behavior in many species. The inferior colliculi (ICs) are the first point in the auditory pathway where cues used to locate sounds, ie, interaural time differences (ITDs), interaural level differences (ILDs), and pinna spectral cues, are all represented in the same location. These cues are first extracted separately on each side of the midline in brainstem nuclei that project to the ICs. Because of this segregation, each IC predominantly represents stimuli in the contralateral hemifield. We tested the hypothesis that commissural connections between the ICs mediate gain control that enhances sound localization acuity. We recorded IC neurons sensitive to either ITDs or ILDs in anesthetized guinea pig, before, during, and following recovery from deactivation of the contralateral IC by cryoloop cooling or microdialysis of procaine. During deactivation, responses were rescaled by divisive gain change and additive shifts, which reduced the dynamic range of ITD and ILD response functions and the ability of neurons to signal changes in sound location. These data suggest that each IC exerts multiplicative gain control and subtractive shifts over the other IC that enhances the neural representation of sound location. Furthermore, this gain control operates in a similar manner on both ITD- and ILD-sensitive neurons, suggesting a shared mechanism operates across localization cues. Our findings reveal a novel dependence of sound localization on commissural processing.

SIGNIFICANCE STATEMENT

Sound localization, a fundamental process in hearing, is dependent on bilateral computations in the brainstem. How this information is transmitted from the brainstem to the auditory cortex, through several stages of processing, without loss of signal fidelity, is not clear. We show that the ability of neurons in the auditory midbrain to encode azimuthal sound location is dependent on gain control mediated by the commissure of the inferior colliculi. This finding demonstrates that commissural processing between homologous auditory nuclei, on either side of the midline, enhances the precision of sound localization.

Distributed and Mixed Information in Monosynaptic Inputs to Dopamine Neurons

Dopamine neurons encode the difference between actual and predicted reward, or reward prediction error (RPE). Although many models have been proposed to account for this computation, it has been difficult to test these models experimentally. Here we established an awake electrophysiological recording system, combined with rabies virus and optogenetic cell-type identification, to characterize the firing patterns of monosynaptic inputs to dopamine neurons while mice performed classical conditioning tasks. We found that each variable required to compute RPE, including actual and predicted reward, was distributed in input neurons in multiple brain areas. Further, many input neurons across brain areas signaled combinations of these variables. These results demonstrate that even simple arithmetic computations such as RPE are not localized in specific brain areas but, rather, distributed across multiple nodes in a brain-wide network. Our systematic method to examine both activity and connectivity revealed unexpected redundancy for a simple computation in the brain.

A Computational Framework for Realistic Retina Modeling

Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.

Is realistic neuronal modeling realistic?

Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models.

Integrative properties and transfer function of cortical neurons initiating absence seizures in a rat genetic model

KEY POINTS

Absence seizures are accompanied by spike-and-wave discharges in cortical electroencephalograms. These complex paroxysmal activities, affecting the thalamocortical networks, profoundly alter cognitive performances and preclude conscious perception. Here, using a well-recognized genetic model of absence epilepsy, we investigated in vivo how information processing was impaired in the ictogenic neurons, i.e. the population of cortical neurons responsible for seizure initiation. In between seizures, ictogenic neurons were more prone to generate bursting activity and their firing response to weak depolarizing events was considerably facilitated compared to control neurons. In the course of seizures, information processing became unstable in ictogenic cells, alternating between an increased and a decreased responsiveness to excitatory inputs, depending on the spike and wave patterns. The state-dependent modulation in the excitability of ictogenic neurons affects their inter-seizure transfer function and their time-to-time responsiveness to incoming inputs during absences.

ABSTRACT

Epileptic seizures result from aberrant cellular and/or synaptic properties that can alter the capacity of neurons to integrate and relay information. During absence seizures, spike-and-wave discharges (SWDs) interfere with incoming sensory inputs and preclude conscious experience. The Genetic Absence Epilepsy Rats from Strasbourg (GAERS), a well-established animal model of absence epilepsy, allows exploration of the cellular basis of this impaired information processing. Here, by combining in vivo electrocorticographic and intracellular recordings from GAERS and control animals, we investigated how the pro-ictogenic properties of seizure-initiating cortical neurons modify their integrative properties and input-output operation during inter-ictal periods and during the spike (S-) and wave (W-) cortical patterns alternating during seizures. In addition to a sustained depolarization and an excessive firing rate in between seizures, ictogenic neurons exhibited a pronounced hyperpolarization-activated depolarization compared to homotypic control neurons. Firing frequency versus injected current relations indicated an increased sensitivity of GAERS cells to weak excitatory inputs, without modifications in the trial-to-trial variability of current-induced firing. During SWDs, the W-component resulted in paradoxical effects in ictogenic neurons, associating an increased membrane input resistance with a reduction in the current-evoked firing responses. Conversely, the collapse of cell membrane resistance during the S-component was accompanied by an elevated current-evoked firing relative to W-sequences, which remained, however, lower compared to inter-ictal periods. These findings show a dynamic modulation of ictogenic neurons' intrinsic properties that may alter inter-seizure cortical function and participate in compromising information processing in cortical networks during absences.

The mechanics of state-dependent neural correlations

Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of population activity is the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs. Similar to the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the physiological mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high-dimensional neural data.

Flexible Metal Oxide/Graphene Oxide Hybrid Neuromorphic Transistors on Flexible Conducting Graphene Substrates

Flexible metal oxide/graphene oxide hybrid multi-gate neuromorphic transistors are fabricated on flexible conducting graphene substrates. Dendritic integrations in both spatial and temporal modes are emulated, and spatiotemporal correlated logics are obtained. A proof-of-principle visual system model for emulating Lobula Giant Motion Detector neuron is also investigated. The results are of great significance for flexible sensors and neuromorphic cognitive systems.

Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks

Utilizing the sparsity ubiquitous in real-world network connectivity, we develop a theoretical framework for efficiently reconstructing sparse feed-forward connections in a pulse-coupled nonlinear network through its output activities. Using only a small ensemble of random inputs, we solve this inverse problem through the compressive sensing theory based on a hidden linear structure intrinsic to the nonlinear network dynamics. The accuracy of the reconstruction is further verified by the fact that complex inputs can be well recovered using the reconstructed connectivity. We expect this Rapid Communication provides a new perspective for understanding the structure-function relationship as well as compressive sensing principle in nonlinear network dynamics.

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.

Only a Minority of the Inhibitory Inputs to Cerebellar Golgi Cells Originates from Local GABAergic Cells

Cerebellar Golgi cells (GoCs) efficiently control the spiking activity of granule cells through GABAA receptor-mediated tonic and phasic inhibition. Recent experiments provided compelling evidence for the extensive interconnection of GoCs through electrical synapses, but their chemical inhibitory synaptic inputs are debated. Here, we investigated the GABAergic synaptic inputs of GoCs using in vitro electrophysiology and quantitative light microscopy (LM) and electron microscopy (EM). We characterized GABAA receptor-mediated IPSCs in GoCs and Lugaro cells (LuCs), and found that IPSCs in GoCs have lower frequencies, smaller amplitudes, and much slower decay kinetics. Pharmacological and LM immunolocalization experiments revealed that GoCs express α3, whereas LuCs express α1 subunit-containing GABAA receptors. The selective expression and clustered distribution of the α3 subunit in GoCs allowed the quantitative analysis of GABAergic synapses on their dendrites in the molecular layer (ML). EM and LM experiments in rats, and wild-type and GlyT2-GFP transgenic mice revealed that only one third of axon terminals establishing GABAergic synapses on GoC dendrites contain GlyT2, ruling out LuCs, globular cells, and any noncortical glycinergic inputs as major inhibitory sources. We also show that axon terminals of stellate/basket cells very rarely innervate GlyT2-GFP-expressing GoCs, indicating that only a minority of the inhibitory inputs to GoCs in the ML originates from local interneurons, and the majority of their inhibitory inputs exclusively releases GABA.

Inhibition Controls Asynchronous States of Neuronal Networks

Computations in cortical circuits require action potentials from excitatory and inhibitory neurons. In this mini-review, I first provide a quick overview of findings that indicate that GABAergic neurons play a fundamental role in coordinating spikes and generating synchronized network activity. Next, I argue that these observations helped popularize the notion that network oscillations require a high degree of spike correlations among interneurons which, in turn, produce synchronous inhibition of the local microcircuit. The aim of this text is to discuss some recent experimental and computational findings that support a complementary view: one in which interneurons participate actively in producing asynchronous states in cortical networks. This requires a proper mixture of shared excitation and inhibition leading to asynchronous activity between neighboring cells. Such contribution from interneurons would be extremely important because it would tend to reduce the spike correlation between neighboring pyramidal cells, a drop in redundancy that could enhance the information-processing capacity of neural networks.

The external globus pallidus: progress and perspectives

The external globus pallidus (GPe) of the basal ganglia is in a unique and powerful position to influence processing of motor information by virtue of its widespread projections to all basal ganglia nuclei. Despite the clinical importance of the GPe in common motor disorders such as Parkinson's disease, there is only limited information about its cellular composition and organizational principles. In this review, recent advances in the understanding of the diversity in the molecular profile, anatomy, physiology and corresponding behaviour during movement of GPe neurons are described. Importantly, this study attempts to build consensus and highlight commonalities of the cellular classification based on existing but contentious literature. Additionally, an analysis of the literature concerning the intricate reciprocal loops formed between the GPe and major synaptic partners, including both the striatum and the subthalamic nucleus, is provided. In conclusion, the GPe has emerged as a crucial node in the basal ganglia macrocircuit. While subtleties in the cellular makeup and synaptic connection of the GPe create new challenges, modern research tools have shown promise in untangling such complexity, and will provide better understanding of the roles of the GPe in encoding movements and their associated pathologies.

Proton-Conducting Graphene Oxide-Coupled Neuron Transistors for Brain-Inspired Cognitive Systems

Proton-conducting graphene oxide electrolyte films with very high electric-double-layer capacitance are used as the gate dielectrics for oxide-based neuron transistor fabrication. Paired-pulse facilitation, dendritic integration, and orientation tuning are successfully emulated. Additionally, neuronal gain controls (arithmetic) are also experimentally demonstrated. The results provide a new-concept approach for building brain-inspired cognitive systems.

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo

The way neurons process information depends both on their intrinsic membrane properties and on the dynamics of the afferent synaptic network. In particular, endogenously-generated network activity, which strongly varies as a function of the state of vigilance, significantly modulates neuronal computation. To investigate how different spontaneous cerebral dynamics impact single neurons' integrative properties, we developed a new experimental strategy in the rat consisting in suppressing in vivo all cerebral activity by means of a systemic injection of a high dose of sodium pentobarbital. Cortical activities, continuously monitored by combined electrocorticogram (ECoG) and intracellular recordings are progressively slowed down, leading to a steady isoelectric profile. This extreme brain state, putting the rat into a deep comatose, was carefully monitored by measuring the physiological constants of the animal throughout the experiments. Intracellular recordings allowed us to characterize and compare the integrative properties of the same neuron embedded into physiologically relevant cortical dynamics, such as those encountered in the sleep-wake cycle, and when the brain was fully silent.

The role of inhibition in epileptic networks

Inhibition plays many roles in cortical circuits, including coordination of network activity in different brain rhythms and neuronal clusters, gating of activity, gain control, and dictating the manner in which activity flows through the network. This latter is particularly relevant to epileptic states, when extreme hypersynchronous discharges can spread across cortical territories. We review these different physiological and pathological roles and discuss how inhibition can be compromised and why this predisposes the network to seizures.

NMDA Receptors Multiplicatively Scale Visual Signals and Enhance Directional Motion Discrimination in Retinal Ganglion Cells

Postsynaptic responses in many CNS neurons are typically small and variable, often making it difficult to distinguish physiologically relevant signals from background noise. To extract salient information, neurons are thought to integrate multiple synaptic inputs and/or selectively amplify specific synaptic activation patterns. Here, we present evidence for a third strategy: directionally selective ganglion cells (DSGCs) in the mouse retina multiplicatively scale visual signals via a mechanism that requires both nonlinear NMDA receptor (NMDAR) conductances in DSGC dendrites and directionally tuned inhibition provided by the upstream retinal circuitry. Postsynaptic multiplication enables DSGCs to discriminate visual motion more accurately in noisy visual conditions without compromising directional tuning. These findings demonstrate a novel role for NMDARs in synaptic processing and provide new insights into how synaptic and network features interact to accomplish physiologically relevant neural computations.

A geometric method for computing ocular kinematics and classifying gaze events using monocular remote eye tracking in a robotic environment

BACKGROUND

Robotic and virtual-reality systems offer tremendous potential for improving assessment and rehabilitation of neurological disorders affecting the upper extremity. A key feature of these systems is that visual stimuli are often presented within the same workspace as the hands (i.e., peripersonal space). Integrating video-based remote eye tracking with robotic and virtual-reality systems can provide an additional tool for investigating how cognitive processes influence visuomotor learning and rehabilitation of the upper extremity. However, remote eye tracking systems typically compute ocular kinematics by assuming eye movements are made in a plane with constant depth (e.g. frontal plane). When visual stimuli are presented at variable depths (e.g. transverse plane), eye movements have a vergence component that may influence reliable detection of gaze events (fixations, smooth pursuits and saccades). To our knowledge, there are no available methods to classify gaze events in the transverse plane for monocular remote eye tracking systems. Here we present a geometrical method to compute ocular kinematics from a monocular remote eye tracking system when visual stimuli are presented in the transverse plane. We then use the obtained kinematics to compute velocity-based thresholds that allow us to accurately identify onsets and offsets of fixations, saccades and smooth pursuits. Finally, we validate our algorithm by comparing the gaze events computed by the algorithm with those obtained from the eye-tracking software and manual digitization.

RESULTS

Within the transverse plane, our algorithm reliably differentiates saccades from fixations (static visual stimuli) and smooth pursuits from saccades and fixations when visual stimuli are dynamic.

CONCLUSIONS

The proposed methods provide advancements for examining eye movements in robotic and virtual-reality systems. Our methods can also be used with other video-based or tablet-based systems in which eye movements are performed in a peripersonal plane with variable depth.

Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons

A reason why the thalamus is more than a passive gateway for sensory signals is that two-third of the synapses of thalamocortical neurons are directly or indirectly related to the activity of corticothalamic axons. While the responses of thalamocortical neurons evoked by sensory stimuli are well characterized, with ON- and OFF-center receptive field structures, the prevalence of synaptic noise resulting from neocortical feedback in intracellularly recorded thalamocortical neurons in vivo has attracted little attention. However, in vitro and modeling experiments point to its critical role for the integration of sensory signals. Here we combine our recent findings in a unified framework suggesting the hypothesis that corticothalamic synaptic activity is adapted to modulate the transfer efficiency of thalamocortical neurons during selective attention at three different levels: First, on ionic channels by interacting with intrinsic membrane properties, second at the neuron level by impacting on the input-output gain, and third even more effectively at the cell assembly level by boosting the information transfer of sensory features encoded in thalamic subnetworks. This top-down population control is achieved by tuning the correlations in subthreshold membrane potential fluctuations and is adapted to modulate the transfer of sensory features encoded by assemblies of thalamocortical relay neurons. We thus propose that cortically-controlled (de-)correlation of subthreshold noise is an efficient and swift dynamic mechanism for selective attention in the thalamus.

Inhibitory dysfunction in amyotrophic lateral sclerosis: future therapeutic opportunities

In amyotrophic lateral sclerosis, motor neuron hyperexcitability and inhibitory dysfunction is emerging as a potential causative link in the dysfunction and degeneration of the motoneuronal circuitry that characterizes the disease. Interneurons, as key regulators of excitability, may mediate much of this imbalance, yet we know little about the way in which inhibitory deficits perturb excitability. In this review, we explore inhibitory control of excitability and the potential contribution of altered inhibition to amyotrophic lateral sclerosis disease processes and vulnerabilities, identifying important windows of therapeutic opportunity and potential interventions, specifically targeting inhibitory control at key disease stages.

Ribbon Synapses and Visual Processing in the Retina

The first synapses transmitting visual information contain an unusual organelle, the ribbon, which is involved in the transport and priming of vesicles to be released at the active zone. The ribbon is one of many design features that allow efficient refilling of the active zone, which in turn enables graded changes in membrane potential to be transmitted using a continuous mode of neurotransmitter release. The ribbon also plays a key role in supplying vesicles for rapid and transient bursts of release that signal fast changes, such as the onset of light. We increasingly understand how the physiological properties of ribbon synapses determine basic transformations of the visual signal and, in particular, how the process of refilling the active zone regulates the gain and adaptive properties of the retinal circuit. The molecular basis of ribbon function is, however, far from clear.

Extracting Social Information from Chemosensory Cues: Consideration of Several Scenarios and Their Functional Implications

Across all sensory modalities, stimuli can vary along multiple dimensions. Efficient extraction of information requires sensitivity to those stimulus dimensions that provide behaviorally relevant information. To derive social information from chemosensory cues, sensory systems must embed information about the relationships between behaviorally relevant traits of individuals and the distributions of the chemical cues that are informative about these traits. In simple cases, the mere presence of one particular compound is sufficient to guide appropriate behavior. However, more generally, chemosensory information is conveyed via relative levels of multiple chemical cues, in non-trivial ways. The computations and networks needed to derive information from multi-molecule stimuli are distinct from those required by single molecule cues. Our current knowledge about how socially relevant information is encoded by chemical blends, and how it is extracted by chemosensory systems is very limited. This manuscript explores several scenarios and the neuronal computations required to identify them.