Biology subjects

Lakatos, P.

Publications and source records attributed to Lakatos, P..

7 recordsLinked to original sources

bioRxiv2022

Saccadic modulation of neural excitability in auditory areas of the neocortex

SummaryIn natural "active" vision, humans and other primates use eye movements (saccades) to sample bits of information from visual scenes. In this process, nonretinal signals linked to saccades shift visual cortical neurons to a high excitability state as each saccade ends. The extent of this saccadic modulation outside of the visual system is unknown. Here, we show that during natural viewing, saccades modulate excitability in numerous auditory cortical areas, with a pattern complementary to that seen in visual areas. Bi-directional functional connectivity patterns suggest that these effects may arise from regions involved in saccade generation. By using saccadic signals to yoke excitability states in auditory areas to those in visual areas, the brain can improve information processing in complex natural settings.

neuroscience↗

bioRxiv2022

Saccade-related modulation of oscillatory activity in primary auditory cortex

The auditory and visual sensory systems are both used by the brain to obtain and organize information from our external environment, yet there are fundamental differences between these two systems. Visual information is acquired using systematic patterns of fixations and saccades, which are controlled by internal motor commands. Sensory input occurs in volleys that are tied to the timing of saccades. In contrast, the auditory system does not use such an overt motor sampling routine so the relationship between sensory input timing and motor activity is less clear. Previous studies of primary visual cortex (V1) in nonhuman primates (NHP) have shown that there is a cyclical modulation of excitability tied to the eye movement cycle and suggests that this excitability modulation stems from the phase reset of neuronal oscillations. We hypothesized that if saccades provide a supramodal temporal context for environmental information then we should also see saccade-related modulation of oscillatory activity in primary auditory cortex (A1) as NHPs shift their gaze around their surroundings. We used linear array multielectrodes to record cortical laminar neuroelectric activity profiles while subjects sat in a dark or dimly lit and silent chamber. Analysis of oscillatory activity in A1 suggests that saccades lead to a phase reset of neuronal oscillations in A1. Saccade-related phase reset of delta oscillations were observed across all layers while theta effects occurred primarily in extragranular layers. Although less frequent, alpha oscillations also showed saccade-related phase reset within the extragranular layers. Our results confirm that saccades provide a supramodal temporal context for the influx of sensory information into A1 and highlight the importance of considering the effects of eye position on auditory processing. Significance StatementUsing laminar multielectrodes, the current study examined saccade-related neuronal activity during resting state while NHPs sat in a dark or dimly lit room. Our results confirm that saccade-related modulation of delta band oscillatory activity occurs across all layers of A1. Interestingly, our data also show a saccade-related phase reset of theta and alpha bands that preferentially occurs in extragranular layers. These results confirm that saccades provide a supramodal temporal context for the influx of environmental information into A1 and emphasizes the importance of considering eye position when examining auditory processing.

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bioRxiv2022

Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics

We developed a biophysically-detailed model of the macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB) and thalamic reticular nuclei (TRN), using the NEURON simulator and NetPyNE multiscale modeling tool. We simulated A1 as a cortical column with a depth of 2000 m and 200 m diameter, containing over 12k neurons and 30M synapses. Neuron densities, laminar locations, classes, morphology and biophysics, and connectivity at the long-range, local and dendritic scale were derived from published experimental data. The A1 model included 6 cortical layers and multiple populations of neurons consisting of 4 excitatory and 4 inhibitory types, and was reciprocally connected to the thalamus (MGB and TRN), mimicking anatomical connectivity. MGB included core and matrix thalamocortical neurons with layer-specific projection patterns to A1, and thalamic interneurons projecting locally. Auditory stimulus-related inputs to the MGB were simulated using phenomenological models of the cochlear/auditory nerve and the inferior colliculus. The model generated cell type and layer-specific firing rates consistent with experimentally observed ranges, and accurately simulated the corresponding local field potentials (LFPs), current source density (CSD), and electroencephalogram (EEG) signals. Laminar CSD patterns during spontaneous activity, and in response to speech input, were similar to those recorded experimentally. Physiological oscillations emerged spontaneously across frequency bands without external rhythmic inputs and were comparable to those recorded in vivo. We used the model to unravel the contributions from distinct cell type and layer-specific neuronal populations to oscillation events detected in CSD, and explored how these relate to the population firing patterns. Overall, the computational model provides a quantitative theoretical framework to integrate and interpret a wide range of experimental data in auditory circuits. It also constitutes a powerful tool to evaluate hypotheses and make predictions about the cellular and network mechanisms underlying common experimental measurements, including MUA, LFP and EEG signals.

neuroscience↗

bioRxiv2021

Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning

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.

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bioRxiv2020

The role of motor and environmental visual rhythms in structuring auditory cortical excitability

One of the ways we perceive our external world is through the process of active sensing in which biological sensors (e.g. fingers and eyes) sample the environment utilizing mostly rhythmic motor routines. Previous studies indicate that these motor sampling patterns modulate neuronal excitability in sensory brain regions by entraining brain rhythms, a process termed motor-initiated entrainment. Additionally, rhythms of the external environment, that are independent of internal motor commands, are also capable of entraining rhythmic brain activity. The goal of our study was twofold. First, we aimed to investigate the properties of motor-initiated entrainment in the auditory system using the most prominent motor sampling pattern in primates, eye movements. Second, we wanted to determine whether/how motor-initiated entrainment by eye movements interacts with visual environmental entrainment. By examining laminar profiles of neuronal ensemble activity in the primary auditory cortex of non-human primates, we found that while motor-initiated entrainment has a suppressive, visual environmental entrainment has an enhancive effect. We also found that the two processes are temporally coupled during free viewing, and their temporal relationship ensures that their effect on neuronal ensemble excitability is complementary rather than interfering. Taken together, our results provide strong evidence that motor and sensory systems continuously interact in orchestrating the brains rhythmic context for the optimal sampling of our multisensory environment.

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bioRxiv2020

Taxonomy of neural oscillation events in primate auditory cortex

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 used two invasively-recorded electrophysiology datasets: one from human, and one from non-human primate auditory system. After removing incidentally occuring event related potentials, we used a wavelet transform based method to quantify oscillation features. We identified about 2 million oscillation events, classified within traditional frequency bands: delta, theta, alpha, beta, low gamma, gamma, high gamma. Oscillation events of 1-44 cycles were present in at least one frequency band in 90% of the time in human and non-human primate recordings. Individual oscillation events were characterized by non-constant frequency and amplitude. This result naturally contrasts with prior studies which assumed such 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 demonstrates that rhythmic, multi-cycle oscillation events dominate auditory cortical dynamics.

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bioRxiv2019

Flexible reset and entrainment of delta oscillations in primate primary auditory cortex: modeling and experiment

Salient auditory stimuli typically exhibit rhythmic temporal patterns. A growing body of evidence suggests that, in primary auditory cortex (A1), attention is associated with entrainment of delta rhythms (1 - 4 Hz) by these auditory stimuli. It is thought that this entrainment involves phase reset of ongoing spontaneous oscillations in A1 by thalamus matrix afferents, but precise mechanisms are unknown. Furthermore, naturalistic stimuli can vary widely in terms of their rhythmicity: some cycles can be longer than others and frequency can drift over time. It is not clear how the auditory system accommodates this natural variability. We show that in rhesus macaque monkey A1 in vivo, bottom-up gamma (40 Hz) click trains influence ongoing spontaneous delta rhythms by inducing an initial delta-timescale transient response, followed by entrainment to gamma and suppression of delta. We then construct a computational model to reproduce this effect, showing that transient thalamus matrix activation can reset A1 delta oscillations by directly activating deep (layer 5) IB cells, promoting bursting, and beginning a new delta cycle. In contrast, long duration gamma-rhythmic input stimuli induce a steady-state containing entrainment of superficial RS and FS cells at gamma, and suppression of delta oscillations. This suppression is achieved in the model by two complementary pathways. First, long-duration thalamus matrix input causes IB cells to switch from bursting to sparse firing, which disrupts the IB bursts associated with delta. Second, thalamus core input activates deep FS cells (by way of layer 4), which fire at gamma frequency and actively inhibit the delta oscillator. Together, these two fundamental operations of reset and suppression can respectively advance and delay the phase of the delta oscillator, allowing it to follow rhythms exhibiting the type of variability found in the natural environment. We discuss these findings in relation to functional implications for speech processing.\n\nAuthor summaryNeurons organize their firing into synchronous, rhythmic patterns. These neural oscillations have been shown to entrain to rhythmic stimuli in the external world, such as patterns of speech or patterns of movement. By entraining to a particular input stimulus, these oscillations are thought to help us attend to that stimulus and to exclude others. To understand how this synchronization emerges, we constructed a physiologically detailed mathematical model of the primary auditory cortex. By fitting this model to a variety of experimental data, we suggest fundamental mechanisms by which neurons of the auditory cortex can synchronize their activity to rhythmic external stimuli. This result will be useful for understanding the mechanism and limitations of oscillatory entrainment, which are thought to underlie the processing of naturalistic auditory inputs like speech or music. Furthermore, this model, though simplified, was shown to generalize and reproduce a wide range of experimental results, and can thus be used as a starting point for building more complex models of auditory cortex.

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