Uncovering the organization of neural circuits with Generalized Phase Locking Analysis.

Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic "field" signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.

Coupling of hippocampal theta and ripples with pontogeniculooccipital waves.

The hippocampus has a major role in encoding and consolidating long-term memories, and undergoes plastic changes during sleep1. These changes require precise homeostatic control by subcortical neuromodulatory structures2. The underlying mechanisms of this phenomenon, however, remain unknown. Here, using multi-structure recordings in macaque monkeys, we show that the brainstem transiently modulates hippocampal network events through phasic pontine waves known as pontogeniculooccipital waves (PGO waves). Two physiologically distinct types of PGO wave appear to occur sequentially, selectively influencing high-frequency ripples and low-frequency theta events, respectively. The two types of PGO wave are associated with opposite hippocampal spike-field coupling, prompting periods of high neural synchrony of neural populations during periods of ripple and theta instances. The coupling between PGO waves and ripples, classically associated with distinct sleep stages, supports the notion that a global coordination mechanism of hippocampal sleep dynamics by cholinergic pontine transients may promote systems and synaptic memory consolidation as well as synaptic homeostasis.

Dissecting the Synapse- and Frequency-Dependent Network Mechanisms of In Vivo Hippocampal Sharp Wave-Ripples.

Hippocampal ripple oscillations likely support reactivation of memory traces that manifest themselves as temporally organized spiking of sparse neuronal ensembles. However, the network mechanisms concurring to achieve this function are largely unknown. We designed a multi-compartmental model of the CA3-CA1 subfields to generate biophysically realistic ripple dynamics from the cellular level to local field potentials. Simulations broadly parallel in vivo observations and support that ripples emerge from CA1 pyramidal spiking paced by recurrent inhibition. In addition to ripple oscillations, key coordination mechanisms involve concomitant aspects of network activity. Recurrent synaptic interactions in CA1 exhibit slow-gamma band coherence with CA3 input, thus offering a way to coordinate CA1 activities with CA3 inducers. Moreover, CA1 feedback inhibition controls the content of spontaneous replay during CA1 ripples, forming new mnemonic representations through plasticity. These insights are consistent with slow-gamma interactions and interneuronal circuit plasticity observed in vivo, suggesting a multifaceted ripple-related replay phenomenon.

Diversity of sharp-wave-ripple LFP signatures reveals differentiated brain-wide dynamical events.

Sharp-wave-ripple (SPW-R) complexes are believed to mediate memory reactivation, transfer, and consolidation. However, their underlying neuronal dynamics at multiple scales remains poorly understood. Using concurrent hippocampal local field potential (LFP) recordings and functional MRI (fMRI), we study local changes in neuronal activity during SPW-R episodes and their brain-wide correlates. Analysis of the temporal alignment between SPW and ripple components reveals well-differentiated SPW-R subtypes in the CA1 LFP. SPW-R-triggered fMRI maps show that ripples aligned to the positive peak of their SPWs have enhanced neocortical metabolic up-regulation. In contrast, ripples occurring at the trough of their SPWs relate to weaker neocortical up-regulation and absent subcortical down-regulation, indicating differentiated involvement of neuromodulatory pathways in the ripple phenomenon mediated by long-range interactions. To our knowledge, this study provides the first evidence for the existence of SPW-R subtypes with differentiated CA1 activity and metabolic correlates in related brain areas, possibly serving different memory functions.

A saliency-based bottom-up visual attention model for dynamic scenes analysis.

This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work adds motion saliency calculations to a neural network model with realistic temporal dynamics [(e.g., building motion salience on top of De Brecht and Saiki Neural Networks 19:1467-1474, (2006)]. The resulting network elicits strong transient responses to moving objects and reaches stability within a biologically plausible time interval. The responses are statistically different comparing between earlier and later motion neural activity; and between moving and non-moving objects. We demonstrate the network on a number of synthetic and real dynamical movie examples. We show that the model captures the motion saliency asymmetry phenomenon. In addition, the motion salience computation enables sudden-onset moving objects that are less salient in the static scene to rise above others. Finally, we include strong consideration for the neural latencies, the Lyapunov stability, and the neural properties being reproduced by the model.

Color coding in the cortex: a modified approach to bottom-up visual attention.

Itti and Koch's (Vision Research 40:1489-1506, 2000) saliency-based visual attention model is a broadly accepted model that describes how attention processes are deployed in the visual cortex in a pure bottom-up strategy. This work complements their model by modifying the color feature calculation. Evidence suggests that S-cone responses are elicited in the same spatial distribution and have the same sign as responses to M-cone stimuli; these cells are tentatively referred to as red-cyan. For other cells, the S-cone input seems to be aligned with the L-cone input; these cells might be green-magenta cells. To model red-cyan and green-magenta double-opponent cells, we implement a center-surround difference approach of the aforementioned model. The resulting color maps elicited enhanced responses to color salient stimuli when compared to the classic ones at high statistical significance levels. We also show that the modified model improves the prediction of locations attended by human viewers.

Heart rate variability dynamics for the prognosis of cardiovascular risk.

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis.