Pedro V. Carelli

Anticipated synchronization in a biologically plausible model of neuronal motifs

Two identical autonomous dynamical systems coupled in a master-slave configuration can exhibit anticipated synchronization (AS) if the slave also receives a delayed negative self-feedback. Recently, AS was shown to occur in systems of simplified neuron models, requiring the coupling of the neuronal membrane potential with its delayed value. However, this coupling has no obvious biological correlate. Here we propose a canonical neuronal microcircuit with standard chemical synapses, where the delayed inhibition is provided by an interneuron. In this biologically plausible scenario, a smooth transition from delayed synchronization (DS) to AS typically occurs when the inhibitory synaptic conductance is increased. The phenomenon is shown to be robust when model parameters are varied within a physiological range. Since the DS-AS transition amounts to an inversion in the timing of the pre- and post-synaptic spikes, our results could have a bearing on spike-timing-dependent plasticity models.

Modeling positive Granger causality and negative phase lag between cortical areas

Different measures of directional influence have been employed to infer effective connectivity in the brain. When the connectivity between two regions is such that one of them (the sender) strongly influences the other (the receiver), a positive phase lag is often expected. The assumption is that the time difference implicit in the relative phase reflects the transmission time of neuronal activity. However, Brovelli et al. (2004) observed that, in monkeys engaged in processing a cognitive task, a dominant directional influence from one area of sensorimotor cortex to another may be accompanied by either a negative or a positive time delay. Here we present a model of two brain regions, coupled with a well-defined directional influence, that displays similar features to those observed in the experimental data. This model is inspired by the theoretical framework of Anticipated Synchronization developed in the field of dynamical systems. Anticipated Synchronization is a form of synchronization that occurs when a unidirectional influence is transmitted from a sender to a receiver, but the receiver leads the sender in time. This counterintuitive synchronization regime can be a stable solution of two dynamical systems coupled in a master-slave (sender-receiver) configuration when the slave receives a negative delayed self-feedback. Despite efforts to understand the dynamics of Anticipated Synchronization, experimental evidence for it in the brain has been lacking. By reproducing experimental delay times and coherence spectra, our results provide a theoretical basis for the underlying mechanisms of the observed dynamics, and suggest that the primate cortex could operate in a regime of Anticipated Synchronization as part of normal neurocognitive function.

Single Synapse Information Coding in Intraburst Spike Patterns of Central Pattern Generator Motor Neurons

Burst firing is ubiquitous in nervous systems and has been intensively studied in central pattern generators (CPGs). Previous works have described subtle intraburst spike patterns (IBSPs) that, despite being traditionally neglected for their lack of relation to CPG motor function, were shown to be cell-type specific and sensitive to CPG connectivity. Here we address this matter by investigating how a bursting motor neuron expresses information about other neurons in the network. We performed experiments on the crustacean stomatogastric pyloric CPG, both in control conditions and interacting in real-time with computer model neurons. The sensitivity of postsynaptic to presynaptic IBSPs was inferred by computing their average mutual information along each neuron burst. We found that details of input patterns are nonlinearly and inhomogeneously coded through a single synapse into the fine IBSPs structure of the postsynaptic neuron following burst. In this way, motor neurons are able to use different time scales to convey two types of information simultaneously: muscle contraction (related to bursting rhythm) and the behavior of other CPG neurons (at a much shorter timescale by using IBSPs as information carriers). Moreover, the analysis revealed that the coding mechanism described takes part in a previously unsuspected information pathway from a CPG motor neuron to a nerve that projects to sensory brain areas, thus providing evidence of the general physiological role of information coding through IBSPs in the regulation of neuronal firing patterns in remote circuits by the CNS.

Self-Organized Near-Zero-Lag Synchronization Induced by Spike-Timing Dependent Plasticity in Cortical Populations.

Several cognitive tasks related to learning and memory exhibit synchronization of macroscopic cortical areas together with synaptic plasticity at neuronal level. Therefore, there is a growing effort among computational neuroscientists to understand the underlying mechanisms relating synchrony and plasticity in the brain. Here we numerically study the interplay between spike-timing dependent plasticity (STDP) and anticipated synchronization (AS). AS emerges when a dominant flux of information from one area to another is accompanied by a negative time lag (or phase). This means that the receiver region pulses before the sender does. In this paper we study the interplay between different synchronization regimes and STDP at the level of three-neuron microcircuits as well as cortical populations. We show that STDP can promote auto-organized zero-lag synchronization in unidirectionally coupled neuronal populations. We also find synchronization regimes with negative phase difference (AS) that are stable against plasticity. Finally, we show that the interplay between negative phase difference and STDP provides limited synaptic weight distribution without the need of imposing artificial boundaries.

Multiscale Functional Imaging in V1 and Cortical Correlates of Apparent Motion

In vivo intracellular electrophysiology offers the unique possibility of listening to the “synaptic rumor” of the cortical network captured by the recording electrode in a single V1 cell. The analysis of synaptic echoes evoked during sensory processing is used to reconstruct the distribution of input sources in visual space and time. It allows us to infer, in the cortical space, the dynamics of the effective input network afferent to the recorded cell. We have applied this method to demonstrate the propagation of visually evoked activity through lateral (and possibly feedback) connectivity in the primary cortex of higher mammals. This approach, based on functional synaptic imaging, is compared here with a real-time functional network imaging technique, based on the use of voltage-sensitive fluorescent dyes. The former method gives access to microscopic convergence processes during synaptic integration in a single neuron, while the latter describes the macroscopic divergence process at the neuronal map level. The joint application of the two techniques, which address two different scales of integration, is used to elucidate the cortical origin of low-level (non-attentive) binding processes participating in the emergence of illusory motion percepts predicted by the psychological Gestalt theory.

Synaptic Correlates of Low-Level Perception in V1.

The computational role of primary visual cortex (V1) in low-level perception remains largely debated. A dominant view assumes the prevalence of higher cortical areas and top-down processes in binding information across the visual field. Here, we investigated the role of long-distance intracortical connections in form and motion processing by measuring, with intracellular recordings, their synaptic impact on neurons in area 17 (V1) of the anesthetized cat. By systematically mapping synaptic responses to stimuli presented in the nonspiking surround of V1 receptive fields, we provide the first quantitative characterization of the lateral functional connectivity kernel of V1 neurons. Our results revealed at the population level two structural-functional biases in the synaptic integration and dynamic association properties of V1 neurons. First, subthreshold responses to oriented stimuli flashed in isolation in the nonspiking surround exhibited a geometric organization around the preferred orientation axis mirroring the psychophysical “association field” for collinear contour perception. Second, apparent motion stimuli, for which horizontal and feedforward synaptic inputs summed in-phase, evoked dominantly facilitatory nonlinear interactions, specifically during centripetal collinear activation along the preferred orientation axis, at saccadic-like speeds. This spatiotemporal integration property, which could constitute the neural correlate of a human perceptual bias in speed detection, suggests that local (orientation) and global (motion) information is already linked within V1. We propose the existence of a “dynamic association field” in V1 neurons, whose spatial extent and anisotropy are transiently updated and reshaped as a function of changes in the retinal flow statistics imposed during natural oculomotor exploration. SIGNIFICANCE STATEMENT The computational role of primary visual cortex in low-level perception remains debated. The expression of this “pop-out” perception is often assumed to require attention-related processes, such as top-down feedback from higher cortical areas. Using intracellular techniques in the anesthetized cat and novel analysis methods, we reveal unexpected structural-functional biases in the synaptic integration and dynamic association properties of V1 neurons. These structural-functional biases provide a substrate, within V1, for contour detection and, more unexpectedly, global motion flow sensitivity at saccadic speed, even in the absence of attentional processes. We argue for the concept of a “dynamic association field” in V1 neurons, whose spatial extent and anisotropy changes with retinal flow statistics, and more generally for a renewed focus on intracortical computation.

A modeling approach on why simple central pattern generators are built of irregular neurons.

The crustacean pyloric Central Pattern Generator (CPG) is a nervous circuit that endogenously provides periodic motor patterns. Even after about 40 years of intensive studies, the rhythm genesis is still not rigorously understood in this CPG, mainly because it is made of neurons with irregular intrinsic activity. Using mathematical models we addressed the question of using a network of irregularly behaving elements to generate periodic oscillations, and we show some advantages of using non-periodic neurons with intrinsic behavior in the transition from bursting to tonic spiking (as found in biological pyloric CPGs) as building components. We studied two- and three-neuron model CPGs built either with Hindmarsh-Rose or with conductance-based Hodgkin-Huxley-like model neurons. By changing a model’s parameter we could span the neuron’s intrinsic dynamical behavior from slow periodic bursting to fast tonic spiking, passing through a transition where irregular bursting was observed. Two-neuron CPG, half center oscillator (HCO), was obtained for each intrinsic behavior of the neurons by coupling them with mutual symmetric synaptic inhibition. Most of these HCOs presented regular antiphasic bursting activity and the changes of the bursting frequencies was studied as a function of the inhibitory synaptic strength. Among all HCOs, those made of intrinsic irregular neurons presented a wider burst frequency range while keeping a reliable regular oscillatory (bursting) behavior. HCOs of periodic neurons tended to be either hard to change their behavior with synaptic strength variations (slow periodic burster neurons) or unable to perform a physiologically meaningful rhythm (fast tonic spiking neurons). Moreover, 3-neuron CPGs with connectivity and output similar to those of the pyloric CPG presented the same results.

Anticipated and zero-lag synchronization in motifs of delay-coupled systems

Anticipated and zero-lag synchronization have been observed in different scientific fields. In the brain, they might play a fundamental role in information processing, temporal coding and spatial attention. Recent numerical work on anticipated and zero-lag synchronization studied the role of delays. However, an analytical understanding of the conditions for these phenomena remains elusive. In this paper, we study both phenomena in systems with small delays. By performing a phase reduction and studying phase locked solutions, we uncover the functional relation between the delay, excitation and inhibition for the onset of anticipated synchronization in a sender-receiver-interneuron motif. In the case of zero-lag synchronization in a chain motif, we determine the stability conditions. These analytical solutions provide an excellent prediction of the phase-locked regimes of Hodgkin-Huxley models and Roessler oscillators.

Inhibitory loop robustly induces anticipated synchronization in neuronal microcircuits

We investigate the synchronization properties between two excitatory coupled neurons in the presence of an inhibitory loop mediated by an interneuron. Dynamic inhibition together with noise independently applied to each neuron provide phase diversity in the dynamics of the neuronal motif. We show that the interplay between the coupling strengths and the external noise controls the phase relations between the neurons in a counterintuitive way. For a master-slave configuration (unidirectional coupling) we find that the slave can anticipate the master, on average, if the slave is subject to the inhibitory feedback. In this nonusual regime, called anticipated synchronization (AS), the phase of the postsynaptic neuron is advanced with respect to that of the presynaptic neuron. We also show that the AS regime survives even in the presence of unbalanced bidirectional excitatory coupling. Moreover, for the symmetric mutually coupled situation, the neuron that is subject to the inhibitory loop leads in phase.

Anticipated synchronization in neuronal circuits unveiled by a phase-response-curve analysis

Anticipated synchronization (AS) is a counterintuitive behavior that has been observed in several systems. When AS occurs in a sender-receiver configuration, the latter can predict the future dynamics of the former for certain parameter values. In particular, in neuroscience AS was proposed to explain the apparent discrepancy between information flow and time lag in the cortical activity recorded in monkeys. Despite its success, a clear understanding of the mechanisms yielding AS in neuronal circuits is still missing. Here we use the well-known phase-response-curve (PRC) approach to study the prototypical sender-receiver-interneuron neuronal motif. Our aim is to better understand how the transitions between delayed to anticipated synchronization and anticipated synchronization to phase-drift regimes occur. We construct a map based on the PRC method to predict the phase-locking regimes and their stability. We find that a PRC function of two variables, accounting simultaneously for the inputs from sender and interneuron into the receiver, is essential to reproduce the numerical results obtained using a Hodgkin-Huxley model for the neurons. On the contrary, the typical approximation that considers a sum of two independent single-variable PRCs fails for intermediate to high values of the inhibitory coupling strength of the interneuron. In particular, it loses the delayed-synchronization to anticipated-synchronization transition.