Neuronal arithmetic

A memory access gate controlled by dynamic contexts

Temporary difficulties in accessing the contents of memories are a common experience in everyday life, for example, when we try to recognize a known person in an unusual context. In addition, recent experiments seem to indicate that retrograde amnesia in the early stages of Alzheimer's disease is due to disorders in accessing memories that were installed normally. These facts suggest the existence of an intermediate step between the stimulus arrival and the associative recognition. In this work, a multimodular neurocomputational model is presented postulating the existence of a neural gate that controls the access of the stimulus with its context to the consolidated memory. If recognition is not achieved, a random search is initiated in a contextual network aroused by the initial context. The search continues until the appropriate context that allows for recognition is found or until the process is turned off because the initial stimulus is no longer maintained in the working memory. The model is based on vector patterns of neural activity and context-dependent matrix memories. Simple Markov chain simulations are presented to exemplify possible search scenarios in the contextual network. Finally, we discuss some of the characteristics of the model and the phenomenon under study.

Hue selectivity from recurrent circuitry in Drosophila

In the perception of color, wavelengths of light reflected off objects are transformed into the derived quantities of brightness, saturation and hue. Neurons responding selectively to hue have been reported in primate cortex, but it is unknown how their narrow tuning in color space is produced by upstream circuit mechanisms. We report the discovery of neurons in the Drosophila optic lobe with hue-selective properties, which enables circuit-level analysis of color processing. From our analysis of an electron microscopy volume of a whole Drosophila brain, we construct a connectomics-constrained circuit model that accounts for this hue selectivity. Our model predicts that recurrent connections in the circuit are critical for generating hue selectivity. Experiments using genetic manipulations to perturb recurrence in adult flies confirm this prediction. Our findings reveal a circuit basis for hue selectivity in color vision.

Muscarinic Modulation of Synaptic Transmission and Short-Term Plasticity in the Dorsal and Ventral Hippocampus

Muscarinic neurotransmission is fundamentally involved in supporting several brain functions by modulating flow of information in brain neural circuits including the hippocampus which displays a remarkable functional segregation along its longitudinal axis. However, how muscarinic neuromodulation contributes to the functional segregation along the hippocampus remains unclear. In this study we show that the nonselective muscarinic receptor agonist carbachol similarly suppresses basal synaptic transmission in the dorsal and ventral CA1 hippocampal field, in a concentration-depended manner. Furthermore, using a ten-pulse stimulation train of varying frequency we found that carbachol changes the frequency filtering properties more in ventral than dorsal hippocampus by facilitating synaptic inputs at a wide range of input frequencies in the ventral compared with dorsal hippocampus. Using the M2 receptor antagonist gallamine and the M4 receptor antagonist tropicamide, we found that M2 receptors are involved in controlling basal synaptic transmission and short-term synaptic plasticity (STSP) in the ventral but not the dorsal hippocampus, while M4 receptors participate in modulating basal synaptic transmission and STSP in both segments of the hippocampus. These results were corroborated by the higher protein expression levels of M2 receptors in the ventral compared with dorsal hippocampus. We conclude that muscarinic transmission modulates excitatory synaptic transmission and short-term synaptic plasticity along the entire rat hippocampus by acting through M4 receptors and recruiting M2 receptors only in the ventral hippocampus. Furthermore, M4 receptors appear to exert a permissive role on the actions of M2 receptors on STSP in the ventral hippocampus. This dorsoventral differentiation of muscarinic modulation is expected to have important implications in information processing along the endogenous hippocampal circuitry.

The neuron mixer and its impact on human brain dynamics

A signal mixer facilitates rich computation, which has been the building block of modern telecommunication. This frequency mixing produces new signals at the sum and difference frequencies of input signals, enabling powerful operations such as heterodyning and multiplexing. Here, we report that a neuron is a signal mixer. We found through ex vivo and in vivo whole-cell measurements that neurons mix exogenous (controlled) and endogenous (spontaneous) subthreshold membrane potential oscillations, producing new oscillation frequencies, and that neural mixing originates in voltage-gated ion channels. Furthermore, we demonstrate that mixing is evident in human brain activity and is associated with cognitive functions. We found that the human electroencephalogram displays distinct clusters of local and inter-region mixing and that conversion of the salient posterior alpha-beta oscillations into gamma-band oscillations regulates visual attention. Signal mixing may enable individual neurons to sculpt the spectrum of neural circuit oscillations and utilize them for computational operations.

Principles of synaptic encoding of brainstem circadian rhythms

Circadian regulation of autonomic tone and reflex pathways pairs physiological processes with the daily light cycle. However, the underlying mechanisms mediating these changes on autonomic neurocircuitry are only beginning to be understood. The brainstem nucleus of the solitary tract (NTS) and adjacent nuclei, including the area postrema and dorsal motor nucleus of the vagus, are key candidates for rhythmic control of some aspects of the autonomic nervous system. Recent findings have contributed to a working model of circadian regulation in the brainstem which manifests from the transcriptional, to synaptic, to circuit levels of organization. Vagal afferent neurons and the NTS possess rhythmic clock gene expression, rhythmic action potential firing, and our recent findings demonstrate rhythmic spontaneous glutamate release. In addition, postsynaptic conductances also vary across the day producing subtle changes in membrane depolarization which govern synaptic efficacy. Together these coordinated pre- and postsynaptic changes provide nuanced control of synaptic transmission across the day to tune the sensitivity of primary afferent input and likely govern reflex output. Further, given the important role for the brainstem in integrating cues such as feeding, cardiovascular function and temperature, it may also be an underappreciated locus in mediating the effects of such non-photic entraining cues. This short review focuses on the neurophysiological principles that govern NTS synaptic transmission and how circadian rhythms impacted them across the day.

A sparse code for natural sound context in auditory cortex

Accurate sound perception can require integrating information over hundreds of milliseconds or even seconds. Spectro-temporal models of sound coding by single neurons in auditory cortex indicate that the majority of sound-evoked activity can be attributed to stimuli with a few tens of milliseconds. It remains uncertain how the auditory system integrates information about sensory context on a longer timescale. Here we characterized long-lasting contextual effects in auditory cortex (AC) using a diverse set of natural sound stimuli. We measured context effects as the difference in a neuron's response to a single probe sound following two different context sounds. Many AC neurons showed context effects lasting longer than the temporal window of a traditional spectro-temporal receptive field. The duration and magnitude of context effects varied substantially across neurons and stimuli. This diversity of context effects formed a sparse code across the neural population that encoded a wider range of contexts than any constituent neuron. Encoding model analysis indicates that context effects can be explained by activity in the local neural population, suggesting that recurrent local circuits support a long-lasting representation of sensory context in auditory cortex.

Lateral inhibition in V1 controls neural & perceptual contrast sensitivity

Lateral inhibition is a central principle for sensory system function. It is thought to operate by the activation of inhibitory neurons that restrict the spatial spread of sensory excitation. Much work on the role of inhibition in sensory systems has focused on visual cortex; however, the neurons, computations, and mechanisms underlying cortical lateral inhibition remain debated, and its importance for visual perception remains unknown. Here, we tested how lateral inhibition from PV or SST neurons in mouse primary visual cortex (V1) modulates neural and perceptual sensitivity to stimulus contrast. Lateral inhibition from PV neurons reduced neural and perceptual sensitivity to visual contrast in a uniform subtractive manner, whereas lateral inhibition from SST neurons more effectively changed the slope (or gain) of neural and perceptual contrast sensitivity. A neural circuit model identified spatially extensive lateral projections from SST neurons as the key factor, and we confirmed this with direct subthreshold measurements of a larger spatial footprint for SST versus PV lateral inhibition. Together, these results define cell-type specific computational roles for lateral inhibition in V1, and establish their unique consequences on sensitivity to contrast, a fundamental aspect of the visual world.

GABAAR-mediated tonic inhibition differentially modulates intrinsic excitability of VIP- and SST- expressing interneurons in layers 2/3 of the somatosensory cortex

Extrasynaptic GABAA receptors (GABAARs) mediating tonic inhibition are thought to play an important role in the regulation of neuronal excitability. However, little is known about a cell type-specific tonic inhibition in molecularly distinctive types of GABAergic interneurons in the mammalian neocortex. Here, we used whole-cell patch-clamp techniques in brain slices prepared from transgenic mice expressing red fluorescent protein (TdTomato) in vasoactive intestinal polypeptide- or somatostatin- positive interneurons (VIP-INs and SST-INs, respectively) to investigate tonic and phasic GABAAR-mediated inhibition as well as effects of GABAA inhibition on intrinsic excitability of these interneurons in layers 2/3 (L2/3) of the somatosensory (barrel) cortex. We found that tonic inhibition was stronger in VIP-INs compared to SST-INs. Contrary to the literature data, tonic inhibition in SST-INs was comparable to pyramidal (Pyr) neurons. Next, tonic inhibition in both interneuron types was dependent on the activity of delta subunit-containing GABAARs. Finally, the GABAAR activity decreased intrinsic excitability of VIP-INs but not SST-INs. Altogether, our data indicate that GABAAR-mediated inhibition modulates neocortical interneurons in a type-specific manner. In contrast to L2/3 VIP-INs, intrinsic excitability of L2/3 SST-INs is immune to the GABAAR-mediated inhibition.

Solving classification tasks by a receptron based on nonlinear optical speckle fields

Among several approaches to tackle the problem of energy consumption in modern computing systems, two solutions are currently investigated: one consists of artificial neural networks (ANNs) based on photonic technologies, the other is a different paradigm compared to ANNs and it is based on random networks of non-linear nanoscale junctions resulting from the assembling of nanoparticles or nanowires as substrates for neuromorphic computing. These networks show the presence of emergent complexity and collective phenomena in analogy with biological neural networks characterized by self-organization, redundancy, and non-linearity. Starting from this background, we propose and formalize a generalization of the perceptron model to describe a classification device based on a network of interacting units where the input weights are non-linearly dependent. We show that this model, called "receptron", provides substantial advantages compared to the perceptron as, for example, the solution of non-linearly separable Boolean functions with a single device. The receptron model is used as a starting point for the implementation of an all-optical device that exploits the non-linearity of optical speckle fields produced by a solid scatterer. By encoding these speckle fields we generated a large variety of target Boolean functions. We demonstrate that by properly setting the model parameters, different classes of functions with different multiplicity can be solved efficiently. The optical implementation of the receptron scheme opens the way for the fabrication of a completely new class of optical devices for neuromorphic data processing based on a very simple hardware.

Role of interneuron subtypes in controlling trial-by-trial output variability in the neocortex

Trial-by-trial variability is a ubiquitous property of neuronal activity in vivo which shapes the stimulus response. Computational models have revealed how local network structure and feedforward inputs shape the trial-by-trial variability. However, the role of input statistics and different interneuron subtypes in this process is less understood. To address this, we investigate the dynamics of stimulus response in a cortical microcircuit model with one excitatory and three inhibitory interneuron populations (PV, SST, VIP). Our findings demonstrate that the balance of inputs to different neuron populations and input covariances are the primary determinants of output trial-by-trial variability. The effect of input covariances is contingent on the input balances. In general, the network exhibits smaller output trial-by-trial variability in a PV-dominated regime than in an SST-dominated regime. Importantly, our work reveals mechanisms by which output trial-by-trial variability can be controlled in a context, state, and task-dependent manner.

Modulation of SK Channels via Calcium Buffering Tunes Intrinsic Excitability of Parvalbumin Interneurons in Neuropathic Pain: A Computational and Experimental Investigation

Parvalbumin-expressing interneurons (PVINs) play a crucial role within the dorsal horn of the spinal cord by preventing touch inputs from activating pain circuits. In both male and female mice, nerve injury decreases PVINs' output via mechanisms that are not fully understood. In this study, we show that PVINs from nerve-injured male mice change their firing pattern from tonic to adaptive. To examine the ionic mechanisms responsible for this decreased output, we used a reparametrized Hodgkin-Huxley type model of PVINs, which predicted (1) the firing pattern transition is because of an increased contribution of small conductance calcium-activated potassium (SK) channels, enabled by (2) impairment in intracellular calcium buffering systems. Analyzing the dynamics of the Hodgkin-Huxley type model further demonstrated that a generalized Hopf bifurcation differentiates the two types of state transitions observed in the transient firing of PVINs. Importantly, this predicted mechanism holds true when we embed the PVIN model within the neuronal circuit model of the spinal dorsal horn. To experimentally validate this hypothesized mechanism, we used pharmacological modulators of SK channels and demonstrated that (1) tonic firing PVINs from naive male mice become adaptive when exposed to an SK channel activator, and (2) adapting PVINs from nerve-injured male mice return to tonic firing on SK channel blockade. Our work provides important insights into the cellular mechanism underlying the decreased output of PVINs in the spinal dorsal horn after nerve injury and highlights potential pharmacological targets for new and effective treatment approaches to neuropathic pain.SIGNIFICANCE STATEMENT Parvalbumin-expressing interneurons (PVINs) exert crucial inhibitory control over Aβ fiber-mediated nociceptive pathways at the spinal dorsal horn. The loss of their inhibitory tone leads to neuropathic symptoms, such as mechanical allodynia, via mechanisms that are not fully understood. This study identifies the reduced intrinsic excitability of PVINs as a potential cause for their decreased inhibitory output in nerve-injured condition. Combining computational and experimental approaches, we predict a calcium-dependent mechanism that modulates PVINs' electrical activity following nerve injury: a depletion of cytosolic calcium buffer allows for the rapid accumulation of intracellular calcium through the active membranes, which in turn potentiates SK channels and impedes spike generation. Our results therefore pinpoint SK channels as potential therapeutic targets for treating neuropathic symptoms.

Multiplicative processing in the modeling of cognitive activities in large neural networks

Explaining the foundation of cognitive abilities in the processing of information by neural systems has been in the beginnings of biophysics since McCulloch and Pitts pioneered work within the biophysics school of Chicago in the 1940s and the interdisciplinary cybernetists meetings in the 1950s, inseparable from the birth of computing and artificial intelligence. Since then, neural network models have traveled a long path, both in the biophysical and the computational disciplines. The biological, neurocomputational aspect reached its representational maturity with the Distributed Associative Memory models developed in the early 70 s. In this framework, the inclusion of signal-signal multiplication within neural network models was presented as a necessity to provide matrix associative memories with adaptive, context-sensitive associations, while greatly enhancing their computational capabilities. In this review, we show that several of the most successful neural network models use a form of multiplication of signals. We present several classical models that included such kind of multiplication and the computational reasons for the inclusion. We then turn to the different proposals about the possible biophysical implementation that underlies these computational capacities. We pinpoint the important ideas put forth by different theoretical models using a tensor product representation and show that these models endow memories with the context-dependent adaptive capabilities necessary to allow for evolutionary adaptation to changing and unpredictable environments. Finally, we show how the powerful abilities of contemporary computationally deep-learning models, inspired in neural networks, also depend on multiplications, and discuss some perspectives in view of the wide panorama unfolded. The computational relevance of multiplications calls for the development of new avenues of research that uncover the mechanisms our nervous system uses to achieve multiplication.

State-of-the-art image and video quality assessment with a metric based on an intrinsically non-linear neural summation model

The development of automatic methods for image and video quality assessment that correlate well with the perception of human observers is a very challenging open problem in vision science, with numerous practical applications in disciplines such as image processing and computer vision, as well as in the media industry. In the past two decades, the goal of image quality research has been to improve upon classical metrics by developing models that emulate some aspects of the visual system, and while the progress has been considerable, state-of-the-art quality assessment methods still share a number of shortcomings, like their performance dropping considerably when they are tested on a database that is quite different from the one used to train them, or their significant limitations in predicting observer scores for high framerate videos. In this work we propose a novel objective method for image and video quality assessment that is based on the recently introduced Intrinsically Non-linear Receptive Field (INRF) formulation, a neural summation model that has been shown to be better at predicting neural activity and visual perception phenomena than the classical linear receptive field. Here we start by optimizing, on a classic image quality database, the four parameters of a very simple INRF-based metric, and proceed to test this metric on three other databases, showing that its performance equals or surpasses that of the state-of-the-art methods, some of them having millions of parameters. Next, we extend to the temporal domain this INRF image quality metric, and test it on several popular video quality datasets; again, the results of our proposed INRF-based video quality metric are shown to be very competitive.

Cell-type-specific plasticity of inhibitory interneurons in the rehabilitation of auditory cortex after peripheral damage

Peripheral sensory organ damage leads to compensatory cortical plasticity that is associated with a remarkable recovery of cortical responses to sound. The precise mechanisms that explain how this plasticity is implemented and distributed over a diverse collection of excitatory and inhibitory cortical neurons remain unknown. After noise trauma and persistent peripheral deficits, we found recovered sound-evoked activity in mouse A1 excitatory principal neurons (PNs), parvalbumin- and vasoactive intestinal peptide-expressing neurons (PVs and VIPs), but reduced activity in somatostatin-expressing neurons (SOMs). This cell-type-specific recovery was also associated with cell-type-specific intrinsic plasticity. These findings, along with our computational modelling results, are consistent with the notion that PV plasticity contributes to PN stability, SOM plasticity allows for increased PN and PV activity, and VIP plasticity enables PN and PV recovery by inhibiting SOMs.

Hue selectivity from recurrent circuitry in Drosophila

A universal principle of sensory perception is the progressive transformation of sensory information from broad non-specific signals to stimulus-selective signals that form the basis of perception. To perceive color, our brains must transform the wavelengths of light reflected off objects into the derived quantities of brightness, saturation and hue. Neurons responding selectively to hue have been reported in primate cortex, but it is unknown how their narrow tuning in color space is produced by upstream circuit mechanisms. To enable circuit level analysis of color perception, we here report the discovery of neurons in the Drosophila optic lobe with hue selective properties. Using the connectivity graph of the fly brain, we construct a connectomics-constrained circuit model that accounts for this hue selectivity. Unexpectedly, our model predicts that recurrent connections in the circuit are critical for hue selectivity. Experiments using genetic manipulations to perturb recurrence in adult flies confirms this prediction. Our findings reveal the circuit basis for hue selectivity in color vision.

Somatic Integration of Incoherent Dendritic Inputs in the Gerbil Medial Superior Olive

The medial superior olive (MSO) is a binaural nucleus that is specialized in detecting the relative arrival times of sounds at both ears. Excitatory inputs to its neurons originating from either ear are segregated to different dendrites. To study the integration of synaptic inputs both within and between dendrites, we made juxtacellular and whole-cell recordings from the MSO in anesthetized female gerbils, while presenting a "double zwuis" stimulus, in which each ear received its own set of tones, which were chosen in a way that all second-order distortion products (DP2s) could be uniquely identified. MSO neurons phase-locked to multiple tones within the multitone stimulus, and vector strength, a measure for spike phase-locking, generally depended linearly on the size of the average subthreshold response to a tone. Subthreshold responses to tones in one ear depended little on the presence of sound in the other ear, suggesting that inputs from different ears sum linearly without a substantial role for somatic inhibition. The "double zwuis" stimulus also evoked response components in the MSO neuron that were phase-locked to DP2s. Bidendritic subthreshold DP2s were quite rare compared with bidendritic suprathreshold DP2s. We observed that in a small subset of cells, the ability to trigger spikes differed substantially between both ears, which might be explained by a dendritic axonal origin. Some neurons that were driven monaurally by only one of the two ears nevertheless showed decent binaural tuning. We conclude that MSO neurons are remarkably good in finding binaural coincidences even among uncorrelated inputs.SIGNIFICANCE STATEMENT Neurons in the medial superior olive are essential for precisely localizing low-frequency sounds in the horizontal plane. From their soma, only two dendrites emerge, which are innervated by inputs originating from different ears. Using a new sound stimulus, we studied the integration of inputs both within and between these dendrites in unprecedented detail. We found evidence that inputs from different dendrites add linearly at the soma, but that small increases in somatic potentials could lead to large increases in the probability of generating a spike. This basic scheme allowed the MSO neurons to detect the relative arrival time of inputs at both dendrites remarkably efficient, although the relative size of these inputs could differ considerably.

Parallel functional architectures within a single dendritic tree

The input-output transformation of individual neurons is a key building block of neural circuit dynamics. While previous models of this transformation vary widely in their complexity, they all describe the underlying functional architecture as unitary, such that each synaptic input makes a single contribution to the neuronal response. Here, we show that the input-output transformation of CA1 pyramidal cells is instead best captured by two distinct functional architectures operating in parallel. We used statistically principled methods to fit flexible, yet interpretable, models of the transformation of input spikes into the somatic "output" voltage and to automatically select among alternative functional architectures. With dendritic Na+ channels blocked, responses are accurately captured by a single static and global nonlinearity. In contrast, dendritic Na+-dependent integration requires a functional architecture with multiple dynamic nonlinearities and clustered connectivity. These two architectures incorporate distinct morphological and biophysical properties of the neuron and its synaptic organization.

How synaptic strength, short-term plasticity, and input synchrony contribute to neuronal spike output

Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few 'strong' synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including 'background' activity from the many 'weak' synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.

Sensory fusion in the hoverfly righting reflex

We study how falling hoverflies use sensory cues to trigger appropriate roll righting behavior. Before being released in a free fall, flies were placed upside-down with their legs contacting the substrate. The prior leg proprioceptive information about their initial orientation sufficed for the flies to right themselves properly. However, flies also use visual and antennal cues to recover faster and disambiguate sensory conflicts. Surprisingly, in one of the experimental conditions tested, hoverflies flew upside-down while still actively flapping their wings. In all the other conditions, flies were able to right themselves using two roll dynamics: fast ([Formula: see text]50ms) and slow ([Formula: see text]110ms) in the presence of consistent and conflicting cues, respectively. These findings suggest that a nonlinear sensory integration of the three types of sensory cues occurred. A ring attractor model was developed and discussed to account for this cue integration process.

Mal de Debarquement Syndrome explained by a vestibulo-cerebellar oscillator

Mal de Debarquement Syndrome (MdDS) is a puzzling central vestibular disorder characterized by a long-lasting perception of oscillatory postural instability that may occur after sea travels or flights. We have postulated that MdDS originates from the post-disembarking persistence of an adaptive internal oscillator consisting of a loop system, involving the right and left vestibular nuclei, and the Purkinje cells of the right and left flocculonodular cerebellar cortex, connected by GABAergic and glutamatergic fibers. We have formulated here a mathematical model of the vestibulo-cerebellar loop system and carried out a computational analysis based on a set of differential equations describing the interactions among the loop elements and containing Hill functions that model input-output firing rates relationships among neurons. The analysis indicates that the system acquires a spontaneous and permanent oscillatory behavior for a decrease of threshold and an increase of sensitivity in neuronal input-output responses. These results suggest a role for synaptic plasticity in MdDS pathophysiology, thus reinforcing our previous hypothesis that MdDS may be the result of excessive synaptic plasticity acting on the vestibulo-cerebellar network during its entraining to an oscillatory environment. Hence, our study points to neuroendocrine pathways that lead to increased synaptic response as possible new therapeutic targets for the clinical treatment of the disorder.

The connectome of an insect brain

Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.

Dual Light Temporal Coding Modes Enabled by Nanoparticle-Mediated Phototransistors via Gate Bias Modulation for Brain-Inspired Visual Perception

The core integration and cooperation of the retina, neurons, and synapses in the visual systems enable humans to effectively sense and process visual information with low power consumption. To mimic the human visual system, an artificial sensory nerve, along with optical sensing─a paired-pulse ratio (PPR) of the light pulse stimulated currents─and neural coding has been developed. For performing the artificial visual perception functions, we consistently reveal the positive and negative correlations between the PPR index and light pulse time interval by applying two consecutive light stimuli with gate voltages of -10 and 5 V, respectively, to a phototransistor. This phototransistor contains a heterostructured channel layer composed of zinc-oxide nanoparticles (ZnO NPs) interconnected with a solution-processed zinc-tin oxide (ZTO) film. The oxygen adsorption and desorption on the ZnO NP surface under light illumination are responsible for the positive-sloped PPR; the electron trapping effect at the ZnO NP/SiO2 interface is attributed to the negative-sloped PPR. The various accountable light power densities and number of surface trap states are considered to be directly realizing these spike-timing interval-dependent characteristics. The actual benefit of these characteristics is the dual temporal coding modes based on multiplicative operation using a ZTO/ZnO NP phototransistor realized via the active gate voltage modulation. The contrary tendency of the PPR index and temporal coding─a major biological neural coding─is well demonstrated by the potential of ZTO/ZnO NP phototransistors to be implemented in sensor networks for an artificial visual perception.

Fundamental Neurochemistry Review: GABAA receptor neurotransmission and epilepsy: Principles, disease mechanisms and pharmacotherapy

Epilepsy is a common neurological disorder associated with alterations of excitation-inhibition balance within brain neuronal networks. GABA_A receptor neurotransmission is the most prevalent form of inhibitory neurotransmission and is strongly implicated in both the pathophysiology and treatment of epilepsy, serving as a primary target for antiseizure medications for over a century. It is now established that GABA exerts a multifaceted influence through an array of GABA_A receptor subtypes that extends far beyond simply negating excitatory activity. As the role of GABA_A neurotransmission within inhibitory circuits is elaborated, this will enable the development of precision therapies that correct the network dysfunction underlying epileptic pathology.

How can artificial neural networks approximate the brain?

The article reviews the history development of artificial neural networks (ANNs), then compares the differences between ANNs and brain networks in their constituent unit, network architecture, and dynamic principle. The authors offer five points of suggestion for ANNs development and ten questions to be investigated further for the interdisciplinary field of brain simulation. Even though brain is a super-complex system with 1011 neurons, its intelligence does depend rather on the neuronal type and their energy supply mode than the number of neurons. It might be possible for ANN development to follow a new direction that is a combination of multiple modules with different architecture principle and multiple computation, rather than very large scale of neural networks with much more uniformed units and hidden layers.

Associative memory of structured knowledge

A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map each structure to a distributed neuronal activity pattern using a vector symbolic architecture scheme.We then use associative memory plasticity rules to store the binarized patterns as fixed points in a recurrent network. By a combination of signal-to-noise analysis and numerical simulations, we demonstrate that our model allows for efficient storage of these knowledge structures, such that the memorized structures as well as their individual building blocks (e.g., events and attributes) can be subsequently retrieved from partial retrieving cues. We show that long-term memory of structured knowledge relies on a new principle of computation beyond the memory basins. Finally, we show that our model can be extended to store sequences of memories as single attractors.

Joint coding of visual input and eye/head position in V1 of freely moving mice

Visual input during natural behavior is highly dependent on movements of the eyes and head, but how information about eye and head position is integrated with visual processing during free movement is unknown, as visual physiology is generally performed under head fixation. To address this, we performed single-unit electrophysiology in V1 of freely moving mice while simultaneously measuring the mouse's eye position, head orientation, and the visual scene from the mouse's perspective. From these measures, we mapped spatiotemporal receptive fields during free movement based on the gaze-corrected visual input. Furthermore, we found a significant fraction of neurons tuned for eye and head position, and these signals were integrated with visual responses through a multiplicative mechanism in the majority of modulated neurons. These results provide new insight into coding in the mouse V1 and, more generally, provide a paradigm for investigating visual physiology under natural conditions, including active sensing and ethological behavior.

Distinct neurexin-cerebellin complexes control AMPA- and NMDA-receptor responses in a circuit-dependent manner

At CA1→subiculum synapses, alternatively spliced neurexin-1 (Nrxn1SS4+) and neurexin-3 (Nrxn3SS4+) enhance NMDA-receptors and suppress AMPA-receptors, respectively, without affecting synapse formation. Nrxn1SS4+ and Nrxn3SS4+ act by binding to secreted cerebellin-2 (Cbln2) that in turn activates postsynaptic GluD1 receptors. Whether neurexin-Cbln2-GluD1 signaling has additional functions besides regulating NMDA- and AMPA-receptors, and whether such signaling performs similar roles at other synapses, however, remains unknown. Here, we demonstrate using constitutive Cbln2 deletions in mice that at CA1→subiculum synapses, Cbln2 performs no additional developmental roles besides regulating AMPA- and NMDA-receptors. Moreover, low-level expression of functionally redundant Cbln1 did not compensate for a possible synapse-formation function of Cbln2 at CA1→subiculum synapses. In exploring the generality of these findings, we examined the prefrontal cortex where Cbln2 was recently implicated in spinogenesis, and the cerebellum where Cbln1 is known to regulate parallel-fiber synapses. In the prefrontal cortex, Nrxn1SS4+-Cbln2 signaling selectively controlled NMDA-receptors without affecting spine or synapse numbers, whereas Nrxn3SS4+-Cbln2 signaling had no apparent role. In the cerebellum, conversely, Nrxn3SS4+-Cbln1 signaling regulated AMPA-receptors, whereas now Nrxn1SS4+-Cbln1 signaling had no manifest effect. Thus, Nrxn1SS4+- and Nrxn3SS4+-Cbln1/2 signaling complexes differentially control NMDA- and AMPA-receptors in different synapses in diverse neural circuits without regulating synapse or spine formation.

Computational models reveal how chloride dynamics determine the optimal distribution of inhibitory synapses to minimise dendritic excitability

Many neurons in the mammalian central nervous system have complex dendritic arborisations and active dendritic conductances that enable these cells to perform sophisticated computations. How dendritically targeted inhibition affects local dendritic excitability is not fully understood. Here we use computational models of branched dendrites to investigate where GABAergic synapses should be placed to minimise dendritic excitability over time. To do so, we formulate a metric we term the “Inhibitory Level” (IL), which quantifies the effectiveness of synaptic inhibition for reducing the depolarising effect of nearby excitatory input. GABAergic synaptic inhibition is dependent on the reversal potential for GABA_A receptors (EGABA), which is primarily set by the transmembrane chloride ion (Cl^-) concentration gradient. We, therefore, investigated how variable EGABA and dynamic chloride affects dendritic inhibition. We found that the inhibitory effectiveness of dendritic GABAergic synapses combines at an encircled branch junction. The extent of this inhibitory accumulation is dependent on the number of branches and location of synapses but is independent of EGABA. This inhibitory accumulation occurs even for very distally placed inhibitory synapses when they are hyperpolarising–but not when they are shunting. When accounting for Cl^- fluxes and dynamics in Cl^- concentration, we observed that Cl^- loading is detrimental to inhibitory effectiveness. This enabled us to determine the most inhibitory distribution of GABAergic synapses which is close to–but not at–a shared branch junction. This distribution balances a trade-off between a stronger combined inhibitory influence when synapses closely encircle a branch junction with the deleterious effects of increased Cl^- by loading that occurs when inhibitory synapses are co-located.

Dendritic branches allow for a rich repertoire of computational capabilities for neurons within the brain. Inhibitory synaptic inputs, which utilise the neurotransmitter GABA, refine and enhance dendritic computations. They are traditionally viewed with regards to their inhibitory effect on action potential generation at the neuronal cell body. Here, we studied the local effects of inhibitory synapses on excitability in dendrites. We also considered the dynamic nature of inhibition that deteriorates the longer it is active due to intracellular chloride ion loading. The central goal of our investigation was to find the best locations for multiple inhibitory synapses to maximise their combined inhibitory effectiveness on nearby excitation in the dendritic tree. We found that the optimal distribution is when inhibitory synapses closely encircle a branch junction, without being co-located at the junction itself. This maximises how their inhibitory influence combines whilst minimising the deleterious effects of chloride loading.

Cascaded normalizations for spatial integration in the primary visual cortex of primates

Spatial integration of visual information is an important function in the brain. However, neural computation for spatial integration in the visual cortex remains unclear. In this study, we recorded laminar responses in V1 of awake monkeys driven by visual stimuli with grating patches and annuli of different sizes. We find three important response properties related to spatial integration that are significantly different between input and output layers: neurons in output layers have stronger surround suppression, smaller receptive field (RF), and higher sensitivity to grating annuli partially covering their RFs. These interlaminar differences can be explained by a descriptive model composed of two global divisions (normalization) and a local subtraction. Our results suggest suppressions with cascaded normalizations (CNs) are essential for spatial integration and laminar processing in the visual cortex. Interestingly, the features of spatial integration in convolutional neural networks, especially in lower layers, are different from our findings in V1.

Neural Information Processing and Computations of Two-Input Synapses

Information processing in artificial neural networks is largely dependent on the nature of neuron models. While commonly used models are designed for linear integration of synaptic inputs, accumulating experimental evidence suggests that biological neurons are capable of nonlinear computations for many converging synaptic inputs via homo- and heterosynaptic mechanisms. This nonlinear neuronal computation may play an important role in complex information processing at the neural circuit level. Here we characterize the dynamics and coding properties of neuron models on synaptic transmissions delivered from two hidden states. The neuronal information processing is influenced by the cooperative and competitive interactions among synapses and the coherence of the hidden states. Furthermore, we demonstrate that neuronal information processing under two-input synaptic transmission can be mapped to linearly nonseparable XOR as well as basic AND/OR operations. In particular, the mixtures of linear and nonlinear neuron models outperform the fashion-MNIST test compared to the neural networks consisting of only one type. This study provides a computational framework for assessing information processing of neuron and synapse models that may be beneficial for the design of brain-inspired artificial intelligence algorithms and neuromorphic systems.

In vivo low-intensity magnetic pulses durably alter neocortical neuron excitability and spontaneous activity

KEY POINTS

Repetitive transcranial magnetic stimulation (rTMS) is a promising technique to alleviate neurological and psychiatric disorders caused by alterations in cortical activity. Our knowledge of the cellular mechanisms underlying rTMS-based therapies remains limited. We combined in vivo focal application of low-intensity rTMS (LI-rTMS) to the rat somatosensory cortex with intracellular recordings of subjacent pyramidal neurons to characterize the effects of weak magnetic fields at single cell level. Ten minutes of LI-rTMS delivered at 10 Hz reliably evoked action potentials in cortical neurons during the stimulation period, and induced durable attenuation of their intrinsic excitability, synaptic activity, and spontaneous firing. These results help us better understand the mechanisms of weak magnetic stimulation and should allow optimizing the effectiveness of stimulation protocols for clinical use.

ABSTRACT

Magnetic brain stimulation is a promising treatment for neurological and psychiatric disorders. However, a better understanding of its effects at the individual neuron level is essential to improve its clinical application. We combined focal low-intensity repetitive transcranial magnetic stimulation (LI-rTMS) to the rat somatosensory cortex with intracellular recordings of subjacent pyramidal neurons in vivo. Continuous 10 Hz LI-rTMS reliably evoked firing at ∼4-5 Hz during the stimulation period and induced durable attenuation of synaptic activity and spontaneous firing in cortical neurons, through membrane hyperpolarization and a reduced intrinsic excitability. However, inducing firing in individual neurons by repeated intracellular current injection did not reproduce LI-rTMS effects on neuronal properties. These data provide novel understanding of mechanisms underlying magnetic brain stimulation showing that, in addition to inducing biochemical plasticity, even weak magnetic fields can activate neurons and enduringly modulate their excitability. Abstract figure legend We examined by means of in vivo intracellular recordings in the rodent the effects of low-intensity (10 mT) repetitive transcranial magnetic stimulation (LI-rTMS) on the functional properties of primary somatosensory cortex pyramidal neurons. After a baseline period, during which cortical spontaneous activity and excitability were measured (Pre), LI-rTMS was applied at 10 Hz for 10 minutes. Despite their low intensity, magnetic pulses reliably evoked action potentials in cortical neurons. Ten minutes of LI-rTMS induced a progressive and long-lasting hyperpolarization of the neuronal membrane and a marked decrease in cell firing rate (Post). This was associated with an altered intrinsic neuronal excitability, characterized by reduced membrane input resistance and increased minimal current required to induce neuronal firing. A portion of this figure was created with biorender.com. This article is protected by copyright. All rights reserved.

Thalamocortical bistable switch as a theoretical model of fibromyalgia pathogenesis inferred from a literature survey

Fibromyalgia (FM) is an unsolved central pain processing disturbance. We aim to provide a unifying model for FM pathogenesis based on a loop network involving thalamocortical regions, i.e., the ventroposterior lateral thalamus (VPL), the somatosensory cortex (SC), and the thalamic reticular nucleus (TRN). The dynamics of the loop have been described by three differential equations having neuron mean firing rates as variables and containing Hill functions to model mutual interactions among the loop elements. A computational analysis conducted with MATLAB has shown a transition from monostability to bistability of the loop behavior for a weakening of GABAergic transmission between TRN and VPL. This involves the appearance of a high-firing-rate steady state, which becomes dominant and is assumed to represent pathogenic pain processing giving rise to chronic pain. Our model is consistent with a bulk of literature evidence, such as neuroimaging and pharmacological data collected on FM patients, and with correlations between FM and immunoendocrine conditions, such as stress, perimenopause, chronic inflammation, obesity, and chronic dizziness. The model suggests that critical targets for FM treatment are to be found among immunoendocrine pathways leading to GABA/glutamate imbalance having an impact on the thalamocortical system.

Dopamine Increases the Intrinsic Excitability of Parvalbumin-Expressing Fast-Spiking Cells in the Piriform Cortex

The piriform cortex (PCx) is essential for the adaptive processing of olfactory information. Neuromodulatory systems, including those utilizing serotonin, acetylcholine, noradrenaline, and dopamine, innervate and regulate neuronal activity in the PCx. Previous research has demonstrated the importance of acetylcholine, noradrenaline and serotonin in odor learning and memory. In contrast, the role of dopamine in the PCx remains under-explored. Here we examined how dopamine modulates the intrinsic electrical properties of identified classes of neurons in the PCx. We found that dopamine had no consistent effect on the intrinsic electrical properties of two types of glutamatergic neurons (semilunar and superficial pyramidal cells) or three types of GABAergic interneurons (horizontal, neurogliaform and somatastatin-expressing regular-spiking cells). However, dopamine had a striking effect on the intrinsic excitability of the parvalbumin-expressing fast-spiking (FS) class of GABAergic interneuron. Dopamine depolarized the resting potential, increased the input resistance and increased the firing frequency of FS cells. Co-application of dopamine with the D1-class dopamine receptor antagonist SCH 23390 blocked the effects of dopamine modulation on FS cells. Conversely, co-application of dopamine with the D2-class antagonist RS-(±)-sulpiride had no effect on dopamine modulation of these cells. Our results indicate that dopamine binds to D1-class dopamine receptors to increase the intrinsic excitability of FS cells. These findings suggest that dopamine has a highly targeted effect in the PCx and reveal how dopamine may modulate the balance between excitation and inhibition, with consequences for odor processing. In addition, our findings provide clues for understanding why neurodegenerative disorders that modify the dopamine system, such as Parkinson's disease, have a deleterious effect on the sense of smell, and may suggest novel diagnostics for the early detection of such disorders.

Neuronal energy use and brain evolution

Consider how advantageous it might be to have eyes on our hands, rather than on our faces: depth perception would be improved by the greater distance between the eyes, and it would be easy to look into relatively inaccessible spaces by appropriate movement of the hands. The absence of mammals that use this visual strategy draws attention to constraints on how evolution is able to 'design' the nervous system. Energy use in particular, in this case the large amount of energy that would be needed to send visual information along the ∼10⁶ optic nerve axons over the length of the arms to the brain (instead of along the much shorter optic nerve), imposes significant design constraints on the nervous system.

Parvalbumin interneuron dendrites enhance gamma oscillations

Dendrites are essential determinants of the input-output relationship of single neurons, but their role in network computations is not well understood. Here, we use a combination of dendritic patch-clamp recordings and in silico modeling to determine how dendrites of parvalbumin (PV)-expressing basket cells contribute to network oscillations in the gamma frequency band. Simultaneous soma-dendrite recordings from PV basket cells in the dentate gyrus reveal that the slope, or gain, of the dendritic input-output relationship is exceptionally low, thereby reducing the cell's sensitivity to changes in its input. By simulating gamma oscillations in detailed network models, we demonstrate that the low gain is key to increase spike synchrony in PV basket cell assemblies when cells are driven by spatially and temporally heterogeneous synaptic inputs. These results highlight the role of inhibitory neuron dendrites in synchronized network oscillations.

A complementary resistive switching neuron

The complementary resistive switching (CRS) memristor has originally been proposed for use as the storage element or artificial synapse in large-scale crossbar array with the capability of solving the sneak path problem, but its usage has mainly been hampered by the inherent destructiveness of the read operation (switching '1' state to 'ON' or '0' state). Taking a different perspective on this 'undesired' property, we here report on the inherent behavioral similarity between the CRS memristor and a leaky integrate-and-fire (LIF) neuron which is another basic neural computing element, in addition to synapse. In particular, the mechanism behind the undesired read destructiveness for storage element and artificial synapse can be exploited to naturally realize the LIF and the ensuing spontaneous repolarization processes, followed by a refractory period. By means of this biological similarity, we demonstrate a Pt/Ta₂O_5-x/TaO_y/Ta CRS memristor that can exhibit these neuronal behaviors and perform various fundamental neuronal operations, including additive/subtractive operations and coincidence detection. These results suggest that the CRS neuron, with its bio-interpretability, is a useful addition to the family of memristive neurons.

Polarization-perceptual anisotropic two-dimensional ReS2 neuro-transistor with reconfigurable neuromorphic vision

Polarization is a common and unique phenomenon in nature, which reveals more camouflage features of objects. However, current polarization-perceptual devices based on conventional physical architectures face enormous challenges for high-performance computation due to the traditional von Neumann bottleneck. In this work, a novel polarization-perceptual neuro-transistor with reconfigurable anisotropic vision is proposed based on a two-dimensional ReS₂ phototransistor. The device exhibits excellent photodetection ability and superior polarization sensitivity due to its direct band gap semiconductor property and strong anisotropic crystal structure, respectively. The fascinating polarization-sensitive neuromorphic behavior, such as polarization memory consolidation and reconfigurable visual imaging, are successfully realized. In particular, the regulated polarization responsivity and dichroic ratio are successfully emulated through our artificial compound eyes. More importantly, two intriguing polarization-perceptual applications for polarized navigation with reconfigurable adaptive learning abilities and three-dimensional visual polarization imaging are also experimentally demonstrated. The proposed device may provide a promising opportunity for future polarization perception systems in intelligent humanoid robots and autonomous vehicles.

A Photoelectric Spiking Neuron for Visual Depth Perception

The biological visual system encodes optical information into spikes and processes them by the neural network, which enables the perception with high throughput of visual processing with ultralow energy budget. This has inspired a wide spectrum of devices to imitate such neural process, while precise mimicking such procedure is still highly required. Here, a highly bio-realistic photoelectric spiking neuron for visual depth perception is presented. The firing spikes generated by the TaO_X memristive spiking encoders have a biologically similar frequency range of 1-200 Hz and sub-micro watts power. Such spiking encoder is integrated with a photodetector and a network of neuromorphic transistors, for information collection and recognition tasks, respectively. The distance-dependent response and eye fatigue of biological visual systems have been mimicked based on such photoelectric spiking neuron. The simulated depth perception shows a recognition improvement by adapting to sights at different distances. The results can advance the technologies in bioinspired or robotic systems that may be endowed with depth perception and power efficiency at the same time.

Synaptic counts approximate synaptic contact area in Drosophila

The pattern of synaptic connections among neurons defines the circuit structure, which constrains the computations that a circuit can perform. The strength of synaptic connections is costly to measure yet important for accurate circuit modeling. Synaptic surface area has been shown to correlate with synaptic strength, yet in the emerging field of connectomics, most studies rely instead on the counts of synaptic contacts between two neurons. Here we quantified the relationship between synaptic count and synaptic area as measured from volume electron microscopy of the larval Drosophila central nervous system. We found that the total synaptic surface area, summed across all synaptic contacts from one presynaptic neuron to a postsynaptic one, can be accurately predicted solely from the number of synaptic contacts, for a variety of neurotransmitters. Our findings support the use of synaptic counts for approximating synaptic strength when modeling neural circuits.

Multi-Band Bathymetry Mapping with Spiking Neuron Anomaly Detection

The developed method extracts bathymetry distributions from multiple satellite image bands. The automated remote sensing function is sparsely coded and combines spiking neural net anomaly filtration, spline, and multi-band fittings. Survey data were used to identify an activation threshold, decay rate, spline fittings, and multi-band weighting factors. Errors were computed for remotely sensed Landsat satellite images. Multi-band fittings achieved an average error of 25.3 cm. This proved sufficiently accurate to automatically extract shorelines to eliminate land areas in bathymetry mapping.

Functional Neuroanatomy of the Rat Nucleus Incertus–Medial Septum Tract: Implications for the Cell-Specific Control of the Septohippocampal Pathway

The medial septum (MS) is critically involved in theta rhythmogenesis and control of the hippocampal network, with which it is reciprocally connected. MS activity is influenced by brainstem structures, including the stress-sensitive, nucleus incertus (NI), the main source of the neuropeptide relaxin-3 (RLN3). In the current study, we conducted a comprehensive neurochemical and electrophysiological characterization of NI neurons innervating the MS in the rat, by employing classical and viral-based neural tract-tracing and electrophysiological approaches, and multiplex fluorescent in situ hybridization. We confirmed earlier reports that the MS is innervated by RLN3 NI neurons and documented putative glutamatergic (vGlut2 mRNA-expressing) neurons as a relevant NI neuronal population within the NI–MS tract. Moreover, we observed that NI neurons innervating MS can display a dual phenotype for GABAergic and glutamatergic neurotransmission, and that 40% of MS-projecting NI neurons express the corticotropin-releasing hormone-1 receptor. We demonstrated that an identified cholecystokinin (CCK)-positive NI neuronal population is part of the NI–MS tract, and that RLN3 and CCK NI neurons belong to a neuronal pool expressing the calcium-binding proteins, calbindin and calretinin. Finally, our electrophysiological studies revealed that MS is innervated by A-type potassium current-expressing, type I NI neurons, and that type I and II NI neurons differ markedly in their neurophysiological properties. Together these findings indicate that the MS is controlled by a discrete NI neuronal network with specific electrophysiological and neurochemical features; and these data are of particular importance for understanding neuronal mechanisms underlying the control of the septohippocampal system and related behaviors.

Activity-dependent modulation of NMDA receptors by endogenous zinc shapes dendritic function in cortical neurons

NMDA receptors (NMDARs) have been proposed to control single-neuron computations in vivo. However, whether specific mechanisms regulate the function of such receptors and modulate input-output transformations performed by cortical neurons under in vivo-like conditions is understudied. Here, we report that in layer 2/3 pyramidal neurons (L2/3 PNs), repeated synaptic stimulation results in an activity-dependent decrease in NMDAR function by vesicular zinc. Such a mechanism shifts the threshold for dendritic non-linearities and strongly reduces LTP. Modulation of NMDARs is cell and pathway specific, being present selectively in L2/3-L2/3 connections but absent in inputs originating from L4 neurons. Numerical simulations highlight that activity-dependent modulation of NMDARs influences dendritic computations, endowing L2/3 PN dendrites with the ability to sustain non-linear integrations constant across different regimes of synaptic activity like those found in vivo. Our results unveil vesicular zinc as an important endogenous modulator of dendritic function in cortical PNs.

•

Vesicular zinc release downregulates function of synaptic NMDARs in cortical neurons

•

Zinc modulation of NMDARs is activity dependent, pathway and cell specific

•

Endogenous zinc controls dendritic non-linearities and synaptic plasticity in L2/3 PNs

•

Modulation of NMDARs normalizes dendritic function during ongoing synaptic activity

Synaptic connectivity to L2/3 of primary visual cortex measured by two-photon optogenetic stimulation

Understanding cortical microcircuits requires thorough measurement of physiological properties of synaptic connections formed within and between diverse subclasses of neurons. Towards this goal, we combined spatially precise optogenetic stimulation with multicellular recording to deeply characterize intralaminar and translaminar monosynaptic connections to supragranular (L2/3) neurons in the mouse visual cortex. The reliability and specificity of multiphoton optogenetic stimulation were measured across multiple Cre lines, and measurements of connectivity were verified by comparison to paired recordings and targeted patching of optically identified presynaptic cells. With a focus on translaminar pathways, excitatory and inhibitory synaptic connections from genetically defined presynaptic populations were characterized by their relative abundance, spatial profiles, strength, and short-term dynamics. Consistent with the canonical cortical microcircuit, layer 4 excitatory neurons and interneurons within L2/3 represented the most common sources of input to L2/3 pyramidal cells. More surprisingly, we also observed strong excitatory connections from layer 5 intratelencephalic neurons and potent translaminar inhibition from multiple interneuron subclasses. The hybrid approach revealed convergence to and divergence from excitatory and inhibitory neurons within and across cortical layers. Divergent excitatory connections often spanned hundreds of microns of horizontal space. In contrast, divergent inhibitory connections were more frequently measured from postsynaptic targets near each other.

Neocortex saves energy by reducing coding precision during food scarcity

Information processing is energetically expensive. In the mammalian brain, it is unclear how information coding and energy use are regulated during food scarcity. Using whole-cell recordings and two-photon imaging in layer 2/3 mouse visual cortex, we found that food restriction reduced AMPA receptor conductance, reducing synaptic ATP use by 29%. Neuronal excitability was nonetheless preserved by a compensatory increase in input resistance and a depolarized resting potential. Consequently, neurons spiked at similar rates as controls but spent less ATP on underlying excitatory currents. This energy-saving strategy had a cost because it amplified the variability of visually-evoked subthreshold responses, leading to a 32% broadening of orientation tuning and impaired fine visual discrimination. This reduction in coding precision was associated with reduced levels of the fat mass-regulated hormone leptin and was restored by exogenous leptin supplementation. Our findings reveal that metabolic state dynamically regulates the energy spent on coding precision in neocortex.

Context-dependent relationships between locus coeruleus firing patterns and coordinated neural activity in the anterior cingulate cortex

Ascending neuromodulatory projections from the locus coeruleus (LC) affect cortical neural networks via the release of norepinephrine (NE). However, the exact nature of these neuromodulatory effects on neural activity patterns in vivo is not well understood. Here, we show that in awake monkeys, LC activation is associated with changes in coordinated activity patterns in the anterior cingulate cortex (ACC). These relationships, which are largely independent of changes in firing rates of individual ACC neurons, depend on the type of LC activation: ACC pairwise correlations tend to be reduced when ongoing (baseline) LC activity increases but enhanced when external events evoke transient LC responses. Both relationships covary with pupil changes that reflect LC activation and arousal. These results suggest that modulations of information processing that reflect changes in coordinated activity patterns in cortical networks can result partly from ongoing, context-dependent, arousal-related changes in activation of the LC-NE system.

Corticothalamic Projections Gate Alpha Rhythms in the Pulvinar

Two types of corticothalamic (CT) terminals reach the pulvinar nucleus of the thalamus, and their distribution varies according to the hierarchical level of the cortical area they originate from. While type 2 terminals are more abundant at lower hierarchical levels, terminals from higher cortical areas mostly exhibit type 1 axons. Such terminals also evoke different excitatory postsynaptic potential dynamic profiles, presenting facilitation for type 1 and depression for type 2. As the pulvinar is involved in the oscillatory regulation between intercortical areas, fundamental questions about the role of these different terminal types in the neuronal communication throughout the cortical hierarchy are yielded. Our theoretical results support that the co-action of the two types of terminals produces different oscillatory rhythms in pulvinar neurons. More precisely, terminal types 1 and 2 produce alpha-band oscillations at a specific range of connectivity weights. Such oscillatory activity is generated by an unstable transition of the balanced state network’s properties that it is found between the quiescent state and the stable asynchronous spike response state. While CT projections from areas 17 and 21a are arranged in the model as the empirical proportion of terminal types 1 and 2, the actions of these two cortical connections are antagonistic. As area 17 generates low-band oscillatory activity, cortical area 21a shifts pulvinar responses to stable asynchronous spiking activity and vice versa when area 17 produces an asynchronous state. To further investigate such oscillatory effects through corticothalamo-cortical projections, the transthalamic pathway, we created a cortical feedforward network of two cortical areas, 17 and 21a, with CT connections to a pulvinar-like network with two cortico-recipient compartments. With this model, the transthalamic pathway propagates alpha waves from the pulvinar to area 21a. This oscillatory transfer ceases when reciprocal connections from area 21a reach the pulvinar, closing the CT loop. Taken together, results of our model suggest that the pulvinar shows a bi-stable spiking activity, oscillatory or regular asynchronous spiking, whose responses are gated by the different activation of cortico-pulvinar projections from lower to higher-order areas such as areas 17 and 21a.

A data-informed mean-field approach to mapping of cortical parameter landscapes

Constraining the many biological parameters that govern cortical dynamics is computationally and conceptually difficult because of the curse of dimensionality. This paper addresses these challenges by proposing (1) a novel data-informed mean-field (MF) approach to efficiently map the parameter space of network models; and (2) an organizing principle for studying parameter space that enables the extraction biologically meaningful relations from this high-dimensional data. We illustrate these ideas using a large-scale network model of the Macaque primary visual cortex. Of the 10-20 model parameters, we identify 7 that are especially poorly constrained, and use the MF algorithm in (1) to discover the firing rate contours in this 7D parameter cube. Defining a "biologically plausible" region to consist of parameters that exhibit spontaneous Excitatory and Inhibitory firing rates compatible with experimental values, we find that this region is a slightly thickened codimension-1 submanifold. An implication of this finding is that while plausible regimes depend sensitively on parameters, they are also robust and flexible provided one compensates appropriately when parameters are varied. Our organizing principle for conceptualizing parameter dependence is to focus on certain 2D parameter planes that govern lateral inhibition: Intersecting these planes with the biologically plausible region leads to very simple geometric structures which, when suitably scaled, have a universal character independent of where the intersections are taken. In addition to elucidating the geometry of the plausible region, this invariance suggests useful approximate scaling relations. Our study offers, for the first time, a complete characterization of the set of all biologically plausible parameters for a detailed cortical model, which has been out of reach due to the high dimensionality of parameter space.

Attention improves information flow between neuronal populations without changing the communication subspace

Visual attention allows observers to change the influence of different parts of a visual scene on their behavior, suggesting that information can be flexibly shared between visual cortex and neurons involved in decision making. We investigated the neural substrate of flexible information routing by analyzing the activity of populations of visual neurons in the medial temporal area (MT) and oculo-motor neurons in the superior colliculus (SC) while rhesus monkeys switched spatial attention. We demonstrated that attention increases the efficacy of visuomotor communication: trial-to-trial variability in SC population activity could be better predicted by the activity of the MT population (and vice versa) when attention was directed toward their joint receptive fields. Surprisingly, this improvement in prediction was not explained by changes in the dimensionality of the shared subspace or in the magnitude of local or shared pairwise noise correlations. These results lay a foundation for future theoretical and experimental studies into how visual attention can flexibly change information flow between sensory and decision neurons.

Interpreting the Entire Connectivity of Individual Neurons in Micropatterned Neural Culture With an Integrated Connectome Analyzer of a Neuronal Network (iCANN)

The function of a neural circuit can be determined by the following: (1) characteristics of individual neurons composing the circuit, (2) their distinct connection structure, and (3) their neural circuit activity. However, prior research on correlations between these three factors revealed many limitations. In particular, profiling and modeling of the connectivity of complex neural circuits at the cellular level are highly challenging. To reduce the burden of the analysis, we suggest a new approach with simplification of the neural connection in an array of honeycomb patterns on 2D, using a microcontact printing technique. Through a series of guided neuronal growths in defined honeycomb patterns, a simplified neuronal circuit was achieved. Our approach allowed us to obtain the whole network connectivity at cellular resolution using a combination of stochastic multicolor labeling via viral transfection. Therefore, we were able to identify several types of hub neurons with distinct connectivity features. We also compared the structural differences between different circuits using three-node motif analysis. This new model system, iCANN, is the first experimental model of neural computation at the cellular level, providing neuronal circuit structures for the study of the relationship between anatomical structure and function of the neuronal network.