Visual stimuli modulate precise synchronous firing within the thalamus

Two-Photon Voltage Imaging of Spontaneous Activity from Multiple Neurons Reveals Network Activity in Brain Tissue

Recording the electrical activity of multiple neurons simultaneously would greatly facilitate studies on the function of neuronal circuits. The combination of the fast scanning by random-access multiphoton microscopy (RAMP) and the latest two-photon-compatible high-performance fluorescent genetically encoded voltage indicators (GEVIs) has enabled action potential detection in deep layers in in vivo brain. However, neuron connectivity analysis on optically recorded action potentials from multiple neurons in brain tissue has yet to be achieved. With high expression of a two-photon-compatible GEVI, ASAP3, via in utero electroporation and RAMP, we achieved voltage recording of spontaneous activities from multiple neurons in brain slice. We provide evidence for the developmental changes in intralaminar horizontal connections in somatosensory cortex layer 2/3 with a greater sensitivity than calcium imaging. This method thus enables investigation of neuronal network connectivity at the cellular resolution in brain tissue.

Intracellular, In Vivo, Dynamics of Thalamocortical Synapses in Visual Cortex

Seminal studies of the thalamocortical circuit in the visual system of the cat have been central to our understanding of sensory encoding. However, thalamocortical synaptic properties remain poorly understood. We used paired recordings, in the lateral geniculate nucleus (LGN) and primary visual cortex (V1), to provide the first in vivo characterization of sensory-driven thalamocortical potentials in V1. The amplitudes of EPSPs we characterized were smaller than those previously reported in vitro Consistent with prior findings, connected LGN-V1 pairs were only found when their receptive fields (RFs) overlapped, and the probability of connection increased steeply with degree of RF overlap and response similarity. However, surprisingly, we found no relationship between EPSP amplitudes and the similarity of RFs or responses, suggesting different connectivity models for intracortical and thalamocortical circuits. Putative excitatory regular-spiking (RS) and inhibitory fast-spiking (FS) V1 cells had similar EPSP characteristics, showing that in the visual system, feedforward excitation and inhibition are driven with equal strength by the thalamus. Similar to observations in the somatosensory cortex, FS V1 cells received less specific input from LGN. Finally, orientation tuning in V1 was not inherited from single presynaptic LGN cells, suggesting that it must emerge exclusively from the combined input of all presynaptic LGN cells. Our results help to decipher early visual encoding circuits and have immediate utility in providing physiological constraints to computational models of the visual system.SIGNIFICANCE STATEMENT To understand how the brain encodes the visual environment, we must understand the transfer of visual signals between various regions of the brain. Therefore, understanding synaptic dynamics is critical to our understanding of sensory encoding. This study provides the first characterization of visually evoked synaptic potentials between the visual thalamus and visual cortex in an intact animal. To record these potentials, we simultaneously recorded the extracellular potential of presynaptic thalamic cells and the intracellular potential of postsynaptic cortical cells in input layers of primary visual cortex. Our characterization of synaptic potentials in vivo disagreed with prior findings in vitro This study will increase our understanding of thalamocortical circuits and will improve computational models of visual encoding.

Intrinsic excitability state of local neuronal population modulates signal propagation in feed-forward neural networks

Reliable signal propagation across distributed brain areas is an essential requirement for cognitive function, and it has been investigated extensively in computational studies where feed-forward network (FFN) is taken as a generic model. But it is still unclear how distinct local network states, which are intrinsically generated by synaptic interactions within each layer, would affect the ability of FFN to transmit information. Here we investigate the impact of such network states on propagating transient synchrony (synfire) and firing rate by a combination of numerical simulations and analytical approach. Specifically, local network dynamics is attributed to the competition between excitatory and inhibitory neurons within each layer. Our results show that concomitant with different local network states, the performance of signal propagation differs dramatically. For both synfire propagation and firing rate propagation, there exists an optimal local excitability state, respectively, that optimizes the performance of signal propagation. Furthermore, we find that long-range connections strongly change the dependence of spiking activity propagation on local network state and propose that these two factors work jointly to determine information transmission across distributed networks. Finally, a simple mean field approach that bridges response properties of long-range connectivity and local subnetworks is utilized to reveal the underlying mechanism.

A novel, jitter-based method for detecting and measuring spike synchrony and quantifying temporal firing precision

BACKGROUND

Precise spike synchrony, at the millisecond or even sub-millisecond time scale, has been reported in different brain areas, but its neurobiological meaning and its underlying mechanisms remain unknown or controversial. Studying these questions is complicated by the lack of a validated, well-normalized and robust index for quantifying synchrony. Previously used measures of synchrony are often improperly normalized and thereby are not comparable between different experimental conditions, are sensitive to variations in firing rate or to the firing rate differential between the two neurons, and/or rely on untenable assumptions of firing rate stationarity and Poisson statistics. I describe here a novel measure, the Jitter-Based Synchrony Index (JBSI), that overcomes these issues.

RESULTS AND DISCUSSION

The JBSI method is based on the introduction of virtual spike jitter. While previous implementations of the jitter method used it only to detect synchrony, the JBSI method also quantifies synchrony. Previous implementations of the jitter method used computationally intensive Monte Carlo simulations to generate surrogate spike trains, whereas the JBSI is computed analytically. The JBSI method does not assume any specific firing model, and does not require that the spike trains be locked to a repeating external stimulus. The JBSI can assume values from 1 (maximal possible synchrony) to -1 (minimal possible synchrony) and is therefore properly normalized. Using simulated Poisson spike trains with introduced controlled spike coincidences, I demonstrate that the JBSI is a linear measure of the spike coincidence rate, is independent of the mean firing frequency or the firing frequency differential between the two neurons, and is not sensitive to co-modulations in the firing rates of the two neurons. In contrast, several commonly used synchrony indices fail under one or more of these scenarios. I also demonstrate how the JBSI can be used to estimate the spike timing precision in the system.

CONCLUSIONS

The JBSI is a conceptually simple and computationally efficient method that can be used to compute the statistical significance of firing synchrony, to quantify synchrony as a well-normalized index, and to estimate the degree of temporal precision in the system.

Gating of signal propagation in spiking neural networks by balanced and correlated excitation and inhibition

Both ongoing and natural stimulus driven neuronal activity are dominated by transients. Selective gating of these transients is mandatory for proper brain function and may, in fact, form the basis of millisecond-fast decision making and action selection. Here we propose that neuronal networks may exploit timing differences between correlated excitation and inhibition (temporal gating) to control the propagation of spiking activity transients. When combined with excitation-inhibition balance, temporal gating constitutes a powerful mechanism to control the propagation of mixtures of transient and tonic neural activity components.

Modulation of temporal precision in thalamic population responses to natural visual stimuli

Natural visual stimuli have highly structured spatial and temporal properties which influence the way visual information is encoded in the visual pathway. In response to natural scene stimuli, neurons in the lateral geniculate nucleus (LGN) are temporally precise - on a time scale of 10-25 ms - both within single cells and across cells within a population. This time scale, established by non stimulus-driven elements of neuronal firing, is significantly shorter than that of natural scenes, yet is critical for the neural representation of the spatial and temporal structure of the scene. Here, a generalized linear model (GLM) that combines stimulus-driven elements with spike-history dependence associated with intrinsic cellular dynamics is shown to predict the fine timing precision of LGN responses to natural scene stimuli, the corresponding correlation structure across nearby neurons in the population, and the continuous modulation of spike timing precision and latency across neurons. A single model captured the experimentally observed neural response, across different levels of contrasts and different classes of visual stimuli, through interactions between the stimulus correlation structure and the nonlinearity in spike generation and spike history dependence. Given the sensitivity of the thalamocortical synapse to closely timed spikes and the importance of fine timing precision for the faithful representation of natural scenes, the modulation of thalamic population timing over these time scales is likely important for cortical representations of the dynamic natural visual environment.

My recollections of Hubel and Wiesel and a brief review of functional circuitry in the visual pathway

The first paper of Hubel and Wiesel in The Journal of Physiology in 1959 marked the beginning of an exciting chapter in the history of visual neuroscience. Through a collaboration that lasted 25 years, Hubel and Wiesel described the main response properties of visual cortical neurons, the functional architecture of visual cortex and the role of visual experience in shaping cortical architecture. The work of Hubel and Wiesel transformed the field not only through scientific discovery but also by touching the life and scientific careers of many students. Here, I describe my personal experience as a postdoctoral student with Torsten Wiesel and how this experience influenced my own work.

Functional consequences of neuronal divergence within the retinogeniculate pathway

The neuronal connections from the retina to the dorsal lateral geniculate nucleus (dLGN) are characterized by a high specificity. Each retinal ganglion cell diverges to connect to a small group of geniculate cells and each geniculate cell receives input from a small number of retinal ganglion cells. Consistent with the high specificity of the connections, geniculate cells sharing input from the same retinal afferent are thought to have very similar receptive fields. However, the magnitude of the receptive-field mismatches, which has not been systematically measured across the different cell types in dLGN, seems to be in contradiction with the functional anatomy of the Y visual pathway: Y retinal afferents in the cat diverge into two geniculate layers (A and C) that have Y geniculate cells (Y(A) and Y(C)) with different receptive-field sizes, response latencies, nonlinearity of spatial summation, and contrast sensitivity. To better understand the functional consequences of retinogeniculate divergence, we recorded from pairs of geniculate cells that shared input from a common retinal afferent across layers and within the same layer in dLGN. We found that nearly all cell pairs that shared retinal input across layers had Y-type receptive fields of the same sign (i.e., both on-center) that overlapped by >70%, but frequently differed in size and response latency. The receptive-field mismatches were relatively small in value (receptive-field size ratio <5; difference in peak response <5 ms), but were robustly correlated with the strength of the synchronous firing generated by the shared retinal connections (R(2) = 0.75). On average, the percentage of geniculate spikes that could be attributed to shared retinal inputs was about 10% for all cell-pair combinations studied. These results are used to provide new estimates of retinogeniculate divergence for different cell classes.