Differences in nonlinearities determine retinal cell types.

Classifying neurons in different types is still an open challenge. In the retina, recent works have taken advantage of the ability to record from a large number of cells to classify ganglion cells into different types based on functional information. Although the first attempts in this direction used the receptive field properties of each cell to classify them, more recent approaches have proposed to cluster ganglion cells directly based on their response to stimuli. These two approaches have not been compared directly. Here, we recorded the responses of a large number of ganglion cells and compared two methods for classifying them into functional groups, one based on the receptive field properties, and the other one using directly their responses to stimuli with various temporal frequencies. We show that the response-based approach allows separation of more types than the receptive field-based method, leading to a better classification. This better granularity is due to the fact that the response-based method takes into account not only the linear part of ganglion cell function but also some of the nonlinearities. A careful characterization of nonlinear processing is thus key to allowing functional classification of sensory neurons.NEW & NOTEWORTHY In the retina, ganglion cells can be classified based on their response to visual stimuli. Although some methods are based on the modeling of receptive fields, others rely on responses to characteristic stimuli. We compared these two classes of methods and show that the latter provides a higher discrimination performance. We also show that this gain arises from the ability to account for the nonlinear behavior of neurons.

Increasing excitation versus decreasing inhibition in auditory cortex: consequences on the discrimination performance between communication sounds.

KEY POINTS: Enhancing cortical excitability can be achieved by either reducing intracortical inhibition or by enhancing intracortical excitation. Here we compare the consequences of reducing intracortical inhibition and of enhancing intracortical excitation on the processing of communication sounds in the primary auditory cortex. Local application of gabazine and of AMPA enlarged the spectrotemporal receptive fields and increased the responses to communication to the same extent. The Mutual Information (an index of the cortical neurons' ability to discriminate between natural sounds) was increased in both cases, as were the noise and signal correlations. Spike-timing reliability was only increased after gabazine application and post-excitation suppression was affected in the opposite way: it was increased when reducing the intracortical inhibition but was eliminated by enhancing the excitation. A computational model suggests that these results can be explained by an additive effect vs. a multiplicative effect ABSTRACT: The level of excitability of cortical circuits is often viewed as one of the critical factors controlling perceptive performance. In theory, enhancing cortical excitability can be achieved either by reducing inhibitory currents or by increasing excitatory currents. Here, we evaluated whether reducing inhibitory currents or increasing excitatory currents in auditory cortex similarly affects the neurons' ability to discriminate between communication sounds. We attenuated the inhibitory currents by application of gabazine (GBZ), and increased the excitatory currents by applying AMPA in the auditory cortex while testing frequency receptive fields and responses to communication sounds. GBZ and AMPA enlarged the receptive fields and increased the responses to communication sounds to the same extent. The spike-timing reliability of neuronal responses was largely increased when attenuating the intracortical inhibition but not after increasing the excitation. The discriminative abilities of cortical cells increased in both cases but this increase was more pronounced after attenuating the inhibition. The shape of the response to communication sounds was modified in the opposite direction: reducing inhibition increased post-excitation suppression whereas this suppression tended to disappear when increasing the excitation. A computational model indicates that the additive effect promoted by AMPA vs. the multiplicative effect of GBZ on neuronal responses, together with the dynamics of spontaneous cortical activity, can explain these differences. Thus, although apparently equivalent for increasing cortical excitability, acting on inhibition vs. on excitation impacts differently the cortical ability to discriminate natural stimuli, and only modulating inhibition changed efficiently the cortical representation of communication sounds.

Fast Learning with Weak Synaptic Plasticity.

New sensory stimuli can be learned with a single or a few presentations. Similarly, the responses of cortical neurons to a stimulus have been shown to increase reliably after just a few repetitions. Long-term memory is thought to be mediated by synaptic plasticity, but in vitro experiments in cortical cells typically show very small changes in synaptic strength after a pair of presynaptic and postsynaptic spikes. Thus, it is traditionally thought that fast learning requires stronger synaptic changes, possibly because of neuromodulation. Here we show theoretically that weak synaptic plasticity can, in fact, support fast learning, because of the large number of synapses N onto a cortical neuron. In the fluctuation-driven regime characteristic of cortical neurons in vivo, the size of membrane potential fluctuations grows only as √N, whereas a single output spike leads to potentiation of a number of synapses proportional to N. Therefore, the relative effect of a single spike on synaptic potentiation grows as √N. This leverage effect requires precise spike timing. Thus, the large number of synapses onto cortical neurons allows fast learning with very small synaptic changes. Significance statement: Long-term memory is thought to rely on the strengthening of coactive synapses. This physiological mechanism is generally considered to be very gradual, and yet new sensory stimuli can be learned with just a few presentations. Here we show theoretically that this apparent paradox can be solved when there is a tight balance between excitatory and inhibitory input. In this case, small synaptic modifications applied to the many synapses onto a given neuron disrupt that balance and produce a large effect even for modifications induced by a single stimulus. This effect makes fast learning possible with small synaptic changes and reconciles physiological and behavioral observations.

Slow feature analysis with spiking neurons and its application to audio stimuli.

Extracting invariant features in an unsupervised manner is crucial to perform complex computation such as object recognition, analyzing music or understanding speech. While various algorithms have been proposed to perform such a task, Slow Feature Analysis (SFA) uses time as a means of detecting those invariants, extracting the slowly time-varying components in the input signals. In this work, we address the question of how such an algorithm can be implemented by neurons, and apply it in the context of audio stimuli. We propose a projected gradient implementation of SFA that can be adapted to a Hebbian like learning rule dealing with biologically plausible neuron models. Furthermore, we show that a Spike-Timing Dependent Plasticity learning rule, shaped as a smoothed second derivative, implements SFA for spiking neurons. The theory is supported by numerical simulations, and to illustrate a simple use of SFA, we have applied it to auditory signals. We show that a single SFA neuron can learn to extract the tempo in sound recordings.

Impact of sub and supra-threshold adaptation currents in networks of spiking neurons.

Neuronal adaptation is the intrinsic capacity of the brain to change, by various mechanisms, its dynamical responses as a function of the context. Such a phenomena, widely observed in vivo and in vitro, is known to be crucial in homeostatic regulation of the activity and gain control. The effects of adaptation have already been studied at the single-cell level, resulting from either voltage or calcium gated channels both activated by the spiking activity and modulating the dynamical responses of the neurons. In this study, by disentangling those effects into a linear (sub-threshold) and a non-linear (supra-threshold) part, we focus on the the functional role of those two distinct components of adaptation onto the neuronal activity at various scales, starting from single-cell responses up to recurrent networks dynamics, and under stationary or non-stationary stimulations. The effects of slow currents on collective dynamics, like modulation of population oscillation and reliability of spike patterns, is quantified for various types of adaptation in sparse recurrent networks.

Supervised learning with decision margins in pools of spiking neurons.

Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

The Convallis rule for unsupervised learning in cortical networks.

The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the "Convallis rule", mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex.

A Modular Implementation to Handle and Benchmark Drift Correction for High-Density Extracellular Recordings.

High-density neural devices are now offering the possibility to record from neuronal populations in vivo at unprecedented scale. However, the mechanical drifts often observed in these recordings are currently a major issue for "spike sorting," an essential analysis step to identify the activity of single neurons from extracellular signals. Although several strategies have been proposed to compensate for such drifts, the lack of proper benchmarks makes it hard to assess the quality and effectiveness of motion correction. In this paper, we present a benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in the literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying a motion correction algorithm as a preprocessing step in the spike sorting pipeline. We show how important it is to properly estimate the positions of the neurons from extracellular traces in order to correctly estimate the probe motion, compare several interpolation procedures, and highlight what are the current limits for motion correction approaches.

Conspiracy beliefs and perceptual inference in times of political uncertainty.

Sociopolitical crises causing uncertainty have accumulated in recent years, providing fertile ground for the emergence of conspiracy ideations. Computational models constitute valuable tools for understanding the mechanisms at play in the formation and rigidification of these unshakeable beliefs. Here, the Circular Inference model was used to capture associations between changes in perceptual inference and the dynamics of conspiracy ideations in times of uncertainty. A bistable perception task and conspiracy belief assessment focused on major sociopolitical events were administered to large populations from three polarized countries. We show that when uncertainty peaks, an overweighting of sensory information is associated with conspiracy ideations. Progressively, this exploration strategy gives way to an exploitation strategy in which increased adherence to conspiracy theories is associated with the amplification of prior information. Overall, the Circular Inference model sheds new light on the possible mechanisms underlying the progressive strengthening of conspiracy theories when individuals face highly uncertain situations.

Stable learning in stochastic network states.

The mammalian cerebral cortex is characterized in vivo by irregular spontaneous activity, but how this ongoing dynamics affects signal processing and learning remains unknown. The associative plasticity rules demonstrated in vitro, mostly in silent networks, are based on the detection of correlations between presynaptic and postsynaptic activity and hence are sensitive to spontaneous activity and spurious correlations. Therefore, they cannot operate in realistic network states. Here, we present a new class of spike-timing-dependent plasticity learning rules with local floating plasticity thresholds, the slow dynamics of which account for metaplasticity. This novel algorithm is shown to both correctly predict homeostasis in synaptic weights and solve the problem of asymptotic stable learning in noisy states. It is shown to naturally encompass many other known types of learning rule, unifying them into a single coherent framework. The mixed presynaptic and postsynaptic dependency of the floating plasticity threshold is justified by a cascade of known molecular pathways, which leads to experimentally testable predictions.

Population rate dynamics and multineuron firing patterns in sensory cortex.

Cortical circuits encode sensory stimuli through the firing of neuronal ensembles, and also produce spontaneous population patterns in the absence of sensory drive. This population activity is often characterized experimentally by the distribution of multineuron "words" (binary firing vectors), and a match between spontaneous and evoked word distributions has been suggested to reflect learning of a probabilistic model of the sensory world. We analyzed multineuron word distributions in sensory cortex of anesthetized rats and cats, and found that they are dominated by fluctuations in population firing rate rather than precise interactions between individual units. Furthermore, cortical word distributions change when brain state shifts, and similar behavior is seen in simulated networks with fixed, random connectivity. Our results suggest that similarity or dissimilarity in multineuron word distributions could primarily reflect similarity or dissimilarity in population firing rate dynamics, and not necessarily the precise interactions between neurons that would indicate learning of sensory features.

fMRI-based neurofeedback strategies and the way forward to treating phasic psychiatric symptoms.

Auditory verbal hallucinations (AVH) are the perfect illustration of phasic symptoms in psychiatric disorders. For some patients and in some situations, AVH cannot be relieved by standard therapeutic approaches. More advanced treatments are needed, among which neurofeedback, and more specifically fMRI-based neurofeedback, has been considered. This paper discusses the different possibilities to approach neurofeedback in the specific context of phasic symptoms, by highlighting the strengths and weaknesses of the available neurofeedback options. It concludes with the added value of the recently introduced information-based neurofeedback. Although requiring an online fMRI signal classifier, which can be quite complex to implement, this neurofeedback strategy opens a door toward an alternative treatment option for complex phasic symptomatology.

Optogenetic targeting of AII amacrine cells restores retinal computations performed by the inner retina.

Most inherited retinal dystrophies display progressive photoreceptor cell degeneration leading to severe visual impairment. Optogenetic reactivation of inner retinal neurons is a promising avenue to restore vision in retinas having lost their photoreceptors. Expression of optogenetic proteins in surviving ganglion cells, the retinal output, allows them to take on the lost photoreceptive function. Nonetheless, this creates an exclusively ON retina by expression of depolarizing optogenetic proteins in all classes of ganglion cells, whereas a normal retina extracts several features from the visual scene, with different ganglion cells detecting light increase (ON) and light decrease (OFF). Refinement of this therapeutic strategy should thus aim at restoring these computations. Here we used a vector that targets gene expression to a specific interneuron of the retina called the AII amacrine cell. AII amacrine cells simultaneously activate the ON pathway and inhibit the OFF pathway. We show that the optogenetic stimulation of AII amacrine cells allows restoration of both ON and OFF responses in the retina, but also mediates other types of retinal processing such as sustained and transient responses. Targeting amacrine cells with optogenetics is thus a promising avenue to restore better retinal function and visual perception in patients suffering from retinal degeneration.

Precision of neuronal localization in 2D cell cultures by using high-performance electropolymerized microelectrode arrays correlated with optical imaging.

Recently, the development of electronic devices to extracellularly record the simultaneous electrical activities of numerous neurons has been blooming, opening new possibilities to interface and decode neuronal activity. In this work, we tested how the use of EDOT electropolymerization to tune post-fabrication materials could optimize the cell/electrode interface of such devices. Our results showed an improved signal-to-noise ratio, better biocompatibility, and a higher number of neurons detected in comparison with gold electrodes. Then, using such enhanced recordings with 2D neuronal cultures combined with fluorescent optical imaging, we checked the extent to which the positions of the recorded neurons could be estimated solely via their extracellular signatures. Our results showed that assuming neurons behave as monopoles, positions could be estimated with a precision of approximately tens of micrometers.

Electropolymerization processing of side-chain engineered EDOT for high performance microelectrode arrays.

Microelectrode Arrays (MEAs) are popular tools for in vitro extracellular recording. They are often optimized by surface engineering to improve affinity with neurons and guarantee higher recording quality and stability. Recently, PEDOT:PSS has been used to coat microelectrodes due to its good biocompatibility and low impedance, which enhances neural coupling. Herein, we investigate on electro-co-polymerization of EDOT with its triglymated derivative to control valence between monomer units and hydrophilic functions on a conducting polymer. Molecular packing, cation complexation, dopant stoichiometry are governed by the glycolation degree of the electro-active coating of the microelectrodes. Optimal monomer ratio allows fine-tuning the material hydrophilicity and biocompatibility without compromising the electrochemical impedance of microelectrodes nor their stability while interfaced with a neural cell culture. After incubation, sensing readout on the modified electrodes shows higher performances with respect to unmodified electropolymerized PEDOT, with higher signal-to-noise ratio (SNR) and higher spike counts on the same neural culture. Reported SNR values are superior to that of state-of-the-art PEDOT microelectrodes and close to that of state-of-the-art 3D microelectrodes, with a reduced fabrication complexity. Thanks to this versatile technique and its impact on the surface chemistry of the microelectrode, we show that electro-co-polymerization trades with many-compound properties to easily gather them into single macromolecular structures. Applied on sensor arrays, it holds great potential for the customization of neurosensors to adapt to environmental boundaries and to optimize extracted sensing features.

How Do Spike Collisions Affect Spike Sorting Performance?

Recently, a new generation of devices have been developed to record neural activity simultaneously from hundreds of electrodes with a very high spatial density, both for in vitro and in vivo applications. While these advances enable to record from many more cells, they also challenge the already complicated process of spike sorting (i.e., extracting isolated single-neuron activity from extracellular signals). In this work, we used synthetic ground-truth recordings with controlled levels of correlations among neurons to quantitatively benchmark the performance of state-of-the-art spike sorters focusing specifically on spike collisions. Our results show that while modern template-matching-based algorithms are more accurate than density-based approaches, all methods, to some extent, failed to detect synchronous spike events of neurons with similar extracellular signals. Interestingly, the performance of the sorters is not largely affected by the spiking activity in the recordings, with respect to average firing rates and spike-train correlation levels. Since the performances of all modern spike sorting algorithms can be affected as function of the activity of the recorded neurons, scientific claims on correlations and synchrony should be carefully assessed based on the analysis provided in this paper.

Decoding Activity in Broca's Area Predicts the Occurrence of Auditory Hallucinations Across Subjects.

BACKGROUND: Functional magnetic resonance imaging (fMRI) capture aims at detecting auditory-verbal hallucinations (AVHs) from continuously recorded brain activity. Establishing efficient capture methods with low computational cost that easily generalize between patients remains a key objective in precision psychiatry. To address this issue, we developed a novel automatized fMRI-capture procedure for AVHs in patients with schizophrenia (SCZ). METHODS: We used a previously validated but labor-intensive personalized fMRI-capture method to train a linear classifier using machine learning techniques. We benchmarked the performances of this classifier on 2320 AVH periods versus resting-state periods obtained from SCZ patients with frequent symptoms (n = 23). We characterized patterns of blood oxygen level-dependent activity that were predictive of AVH both within and between subjects. Generalizability was assessed with a second independent sample gathering 2000 AVH labels (n = 34 patients with SCZ), while specificity was tested with a nonclinical control sample performing an auditory imagery task (840 labels, n = 20). RESULTS: Our between-subject classifier achieved high decoding accuracy (area under the curve = 0.85) and discriminated AVH from rest and verbal imagery. Optimizing the parameters on the first schizophrenia dataset and testing its performance on the second dataset led to an out-of-sample area under the curve of 0.85 (0.88 for the converse test). We showed that AVH detection critically depends on local blood oxygen level-dependent activity patterns within Broca's area. CONCLUSIONS: Our results demonstrate that it is possible to reliably detect AVH states from fMRI blood oxygen level-dependent signals in patients with SCZ using a multivariate decoder without performing complex preprocessing steps. These findings constitute a crucial step toward brain-based treatments for severe drug-resistant hallucinations.

Human periventricular nodular heterotopia shows several interictal epileptic patterns and hyperexcitability of neuronal firing.

Periventricular nodular heterotopia (PNH) is a malformation of cortical development that frequently causes drug-resistant epilepsy. The epileptogenicity of ectopic neurons in PNH as well as their role in generating interictal and ictal activity is still a matter of debate. We report the first in vivo microelectrode recording of heterotopic neurons in humans. Highly consistent interictal patterns (IPs) were identified within the nodules: (1) Periodic Discharges PLUS Fast activity (PD+F), (2) Sporadic discharges PLUS Fast activity (SD+F), and (3) epileptic spikes (ES). Neuronal firing rates were significantly modulated during all IPs, suggesting that multiple IPs were generated by the same local neuronal populations. Furthermore, firing rates closely followed IP morphologies. Among the different IPs, the SD+F pattern was found only in the three nodules that were actively involved in seizure generation but was never observed in the nodule that did not take part in ictal discharges. On the contrary, PD+F and ES were identified in all nodules. Units that were modulated during the IPs were also found to participate in seizures, increasing their firing rate at seizure onset and maintaining an elevated rate during the seizures. Together, nodules in PNH are highly epileptogenic and show several IPs that provide promising pathognomonic signatures of PNH. Furthermore, our results show that PNH nodules may well initiate seizures.

Intrusive experiences in posttraumatic stress disorder: Treatment response induces changes in the directed functional connectivity of the anterior insula.

BACKGROUND: One of the core features of posttraumatic stress disorder (PTSD) is re-experiencing trauma. The anterior insula (AI) has been proposed to play a crucial role in these intrusive experiences. However, the dynamic function of the AI in re-experiencing trauma and its putative modulation by effective therapy need to be specified. METHODS: Thirty PTSD patients were enrolled and exposed to traumatic memory reactivation therapy. Resting-state functional magnetic resonance imaging (fMRI) scans were acquired before and after treatment. To explore AI-directed influences over the rest of the brain, we referred to a mixed model using pre-/posttreatment Granger causality analysis seeded on the AI as a within-subject factor and treatment response as a between-subject factor. To further identify correlates of re-experiencing trauma, we investigated how intrusive severity affected (i) causality maps and (ii) the spatial stability of other intrinsic brain networks. RESULTS: We observed changes in AI-directed functional connectivity patterns in PTSD patients. Many within- and between-network causal paths were found to be less influenced by the AI after effective therapy. Insular influences were found to be positively correlated with re-experiencing symptoms, while they were linked with a stronger default mode network (DMN) and more unstable central executive network (CEN) connectivity. CONCLUSION: We showed that directed changes in AI signaling to the DMN and CEN at rest may underlie the degree of re-experiencing symptoms in PTSD. A positive response to treatment further induced changes in network-to-network anticorrelated patterns. Such findings may guide targeted neuromodulation strategies in PTSD patients not suitably improved by conventional treatment.

Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons.

The relationship between the dynamics of neural networks and their patterns of connectivity is far from clear, despite its importance for understanding functional properties. Here, we have studied sparsely-connected networks of conductance-based integrate-and-fire (IF) neurons with balanced excitatory and inhibitory connections and with finite axonal propagation speed. We focused on the genesis of states with highly irregular spiking activity and synchronous firing patterns at low rates, called slow Synchronous Irregular (SI) states. In such balanced networks, we examined the "macroscopic" properties of the spiking activity, such as ensemble correlations and mean firing rates, for different intracortical connectivity profiles ranging from randomly connected networks to networks with Gaussian-distributed local connectivity. We systematically computed the distance-dependent correlations at the extracellular (spiking) and intracellular (membrane potential) levels between randomly assigned pairs of neurons. The main finding is that such properties, when they are averaged at a macroscopic scale, are invariant with respect to the different connectivity patterns, provided the excitatory-inhibitory balance is the same. In particular, the same correlation structure holds for different connectivity profiles. In addition, we examined the response of such networks to external input, and found that the correlation landscape can be modulated by the mean level of synchrony imposed by the external drive. This modulation was found again to be independent of the external connectivity profile. We conclude that first and second-order "mean-field" statistics of such networks do not depend on the details of the connectivity at a microscopic scale. This study is an encouraging step toward a mean-field description of topological neuronal networks.