Faculty Bibliography

We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.

Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.

Electrophysiological oscillations in the brain have been shown to occur as multi-cycle events, with onset and offset dependent on behavioral and cognitive state. To provide a baseline for state-related and task-related events, we quantified oscillation features in resting-state recordings. We developed an open-source wavelet-based tool to detect and characterize such oscillation events (OEvents) and exemplify the use of this tool in both simulations and two invasively-recorded electrophysiology datasets: one from human, and one from non-human primate auditory system. After removing incidentally occurring event related potentials, we used OEvents to quantify oscillation features. We identified about 2 million oscillation events, classified within traditional frequency bands: delta, theta, alpha, beta, low gamma, gamma, and high gamma. Oscillation events of 1-44 cycles could be identified in at least one frequency band 90% of the time in human and non-human primate recordings. Individual oscillation events were characterized by non-constant frequency and amplitude. This result necessarily contrasts with prior studies which assumed frequency constancy, but is consistent with evidence from event-associated oscillations. We measured oscillation event duration, frequency span, and waveform shape. Oscillations tended to exhibit multiple cycles per event, verifiable by comparing filtered to unfiltered waveforms. In addition to the clear intra-event rhythmicity, there was also evidence of inter-event rhythmicity within bands, demonstrated by finding that coefficient of variation of interval distributions and Fano Factor measures differed significantly from a Poisson distribution assumption. Overall, our study provides an easy-to-use tool to study oscillation events at the single-trial level or in ongoing recordings, and demonstrates that rhythmic, multi-cycle oscillation events dominate auditory cortical dynamics.Significance StatementTo provide a baseline for auditory system cortical dynamics, we quantified neuronal oscillation event features in resting-state recordings of the auditory system. We found that even at rest, event-like oscillations are the dominant operational mode of the auditory cortex in both humans and non-human primates. Our results highlight the importance of the auditory system's rhythmic neuronal fluctuations in setting the context on top of which auditory processing necessary for behavior and cognition occurs. In addition, we demonstrate the importance of studying basic features of oscillation events in ongoing and single-trial recordings to understand their role in cognition and the mechanisms generating them.

New treatment development for psychiatric disorders depends critically upon the development of physiological measures that can accurately translate between preclinical animal models and clinical human studies. Such measures can be used both as stratification biomarkers to define pathophysiologically homogeneous patient populations and as target engagement biomarkers to verify similarity of effects across preclinical and clinical intervention. Traditional "time-domain" event-related potentials (ERP) have been used translationally to date but are limited by the significant differences in timing and distribution across rodent, monkey and human studies. By contrast, neuro-oscillatory responses, analyzed within the "time-frequency" domain, are relatively preserved across species permitting more precise translational comparisons. Moreover, neuro-oscillatory responses are increasingly being mapped to local circuit mechanisms and may be useful for investigating effects of both pharmacological and neuromodulatory interventions on excitatory/inhibitory balance. The present paper provides a roadmap for development of neuro-oscillatory responses as translational biomarkers in neuropsychiatric treatment development.

Previous studies indicate that motor sampling patterns modulate neuronal excitability in sensory brain regions by entraining brain rhythms, a process termed motor-initiated entrainment. In addition, rhythms of the external environment are also capable of entraining brain rhythms. Our first goal was to investigate the properties of motor-initiated entrainment in the auditory system using a prominent visual motor sampling pattern in primates, saccades. Second, we wanted to determine whether/how motor-initiated entrainment interacts with visual environmental entrainment. We examined laminar profiles of neuronal ensemble activity in primary auditory cortex and found that whereas motor-initiated entrainment has a suppressive effect, visual environmental entrainment has an enhancive effect. We also found that these processes are temporally coupled, and their temporal relationship ensures that their effect on excitability is complementary rather than interfering. Altogether, our results demonstrate that motor and sensory systems continuously interact in orchestrating the brain's context for the optimal sampling of our multisensory environment.

BACKGROUND:Mismatch negativity (MMN) is an extensively validated biomarker of cognitive function across both normative and clinical populations and has previously been localized to supratemporal auditory cortex. MMN is thought to represent a comparison of the features of the present stimulus versus a mnemonic template formed by the prior stimuli. METHODS:We used concurrent thalamic and primary auditory cortical (A1) laminar recordings in 7 macaques to evaluate the relative contributions of core (lemniscal) and matrix (nonlemniscal) thalamic afferents to MMN generation. RESULTS:We demonstrated that deviance-related activity is observed mainly in matrix regions of auditory thalamus, MMN generators are most prominent in layer 1 of cortex as opposed to sensory responses that activate layer 4 first and sequentially all cortical layers, and MMN is elicited independent of the frequency tuning of A1 neuronal ensembles. Consistent with prior reports, MMN-related thalamocortical activity was strongly inhibited by ketamine. CONCLUSIONS:Taken together, our results demonstrate distinct matrix versus core thalamocortical circuitry underlying the generation of a higher-order brain response (MMN) versus sensory responses.

Even the simplest cognitive processes involve interactions between cortical regions. To study these processes, we usually rely on averaging across several repetitions of a task or across long segments of data to reach a statistically valid conclusion. Neuronal oscillations reflect synchronized excitability fluctuations in ensembles of neurons and can be observed in electrophysiological recordings in the presence or absence of an external stimulus. Oscillatory brain activity has been viewed as sustained increase in power at specific frequency bands. However, this perspective has been challenged in recent years by the notion that oscillations may occur as transient burst-like events that occur in individual trials and may only appear as sustained activity when multiple trials are averaged together. In this review, we examine the idea that oscillatory activity can manifest as a transient burst as well as a sustained increase in power. We discuss the technical challenges involved in the detection and characterization of transient events at the single trial level, the mechanisms that might generate them and the features that can be extracted from these events to study single-trial dynamics of neuronal ensemble activity.

Rhythms are a fundamental and defining feature of neuronal activity in animals including humans. This rhythmic brain activity interacts in complex ways with rhythms in the internal and external environment through the phenomenon of 'neuronal entrainment', which is attracting increasing attention due to its suggested role in a multitude of sensory and cognitive processes. Some senses, such as touch and vision, sample the environment rhythmically, while others, like audition, are faced with mostly rhythmic inputs. Entrainment couples rhythmic brain activity to external and internal rhythmic events, serving fine-grained routing and modulation of external and internal signals across multiple spatial and temporal hierarchies. This interaction between a brain and its environment can be experimentally investigated and even modified by rhythmic sensory stimuli or invasive and non-invasive neuromodulation techniques. We provide a comprehensive overview of the topic and propose a theoretical framework of how neuronal entrainment dynamically structures information from incoming neuronal, bodily and environmental sources. We discuss the different types of neuronal entrainment, the conceptual advances in the field, and converging evidence for general principles.

Visual physiology is traditionally investigated by presenting stimuli with gaze held constant. However, during active viewing of a scene, information is actively acquired using systematic patterns of fixations and saccades. Prior studies suggest that during such active viewing, both nonretinal, saccade-related signals and "extra-classical" receptive field inputs modulate visual processing. This study used a set of active viewing tasks that allowed us to compare visual responses with and without direct foveal input, thus isolating the contextual eye movement-related influences. Studying nonhuman primates, we find strong contextual modulation in primary visual cortex (V1): excitability and response amplification immediately after fixation onset, transiting to suppression leading up to the next saccade. Time-frequency decomposition suggests that this amplification and suppression cycle stems from a phase reset of ongoing neuronal oscillatory activity. The impact of saccade-related contextual modulation on stimulus processing makes active visual sensing fundamentally different from the more passive processes investigated in traditional paradigms.

Prior studies have shown that repetitive presentation of acoustic stimuli results in an alignment of ongoing neuronal oscillations to the sequence rhythm via oscillatory entrainment by external cues. Our study aimed to explore the neural correlates of the perceptual parsing and grouping of complex repeating auditory patterns that occur based solely on statistical regularities, or context. Human psychophysical studies suggest that the recognition of novel auditory patterns amid a continuous auditory stimulus sequence occurs automatically halfway through the first repetition. We hypothesized that once repeating patterns were detected by the brain, internal rhythms would become entrained, demarcating the temporal structure of these repetitions despite lacking external cues defining pattern on- or offsets. To examine the neural correlates of pattern perception, neuroelectric activity of primary auditory cortex (A1) and thalamic nuclei was recorded while nonhuman primates passively listened to streams of rapidly presented pure tones and bandpass noise bursts. At arbitrary intervals, random acoustic patterns composed of 11 stimuli were repeated five times without any perturbance of the constant stimulus flow. We found significant delta entrainment by these patterns in the A1, medial geniculate body, and medial pulvinar. In A1 and pulvinar, we observed a statistically significant, pattern structure-aligned modulation of neuronal firing that occurred earliest in the pulvinar, supporting the idea that grouping and detecting complex auditory patterns is a top-down, context-driven process. Besides electrophysiological measures, a pattern-related modulation of pupil diameter verified that, like humans, nonhuman primates consciously detect complex repetitive patterns that lack physical boundaries.