Earl K. Miller
Profile Url: earl-k--miller
Researcher at MIT
Neural oscillations are evident across cortex but their spatial structure is not well- explored. Are oscillations stationary or do they form traveling waves, i.e., spatially organized patterns whose peaks and troughs move sequentially across cortex? Here, we show that oscillations in the prefrontal cortex (PFC) organized as traveling waves in the theta (4-8Hz), alpha (8-12Hz), and beta (12-30Hz) bands. Some traveling waves were planar while many rotated around an anatomical point. The waves were modulated during performance of a working memory task. During baseline conditions, waves flowed bidirectionally along a specific axis of orientation. During task performance, there was an increase in waves in one direction over the other, especially in the beta band. We discuss functional implications.
Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes bursts of 30-50 Hz oscillations alternating with 0.1 to 10 Hz oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamines neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in 10 Hz frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma burst and slow oscillation activity, as well as intermediate states in between. The mean duration of the gamma burst state was 2.5s([1.9,3.4]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.7s([1.9,3.8]s) for the human subjects. The mean duration of the slow oscillation state was 1.6s([1.1,2.5]s) and 0.7s([0.6,0.9]s) for the two NHPs, and 2.8s([1.9,4.3]s) for the human subjects. Our beta-HMM framework provides a useful tool for experimental data analysis. Our characterizations of the gamma-burst process offer detailed, quantitative constraints that can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.
Working memory allows us to selectively remember and flexibly manipulate a limited amount of information. Importantly, once we learn a certain operation, it generalizes to any memory object, not just the objects it has been trained on. Here we propose a conceptual model for how this might be achieved on the neural network level. It relies on spatial computing, in which sensory information flows spatially within the network over time. As a result, information about, for instance, object order can be retrieved agnostically to the detailed synaptic connectivity responsible for encoding specific memory items. This spatial flow is reflected in low-dimensional brain activity complementing high-dimensional activity that accounts for storing the sensory information itself. By comparing the dimensionality of local field potentials and spiking activity from prefrontal cortex of rhesus macaques performing multi-item working memory tasks we verify predictions from this model. We discuss how spatial computing may be a principle to aid generalization and zero-shot learning by utilizing spatial dimensions as an additional information encoding dimension. The new model also helps explain several aspects of neurophysiological activity related to working memory control, including dimensionality, context-dependent selectivity as well as persistent and non-persistent delay activity.
Visual working memory (WM) storage is largely independent between the left and right visual hemifields/cerebral hemispheres, yet somehow WM feels seamless. We studied how WM is integrated across hemifields by recording neural activity bilaterally from lateral prefrontal cortex. An instructed saccade during the WM delay shifted the remembered location from one hemifield to the other. Before the shift, spike rates and oscillatory power showed clear signatures of memory laterality. After the shift, the lateralization inverted, consistent with transfer of the memory trace from one hemisphere to the other. Transferred traces initially used different neural ensembles from feedforward-induced ones but they converged at the end of the delay. Around the time of transfer, synchrony between the two prefrontal hemispheres peaked in theta and low-gamma frequencies, with a directionality consistent with memory trace transfer. This illustrates how dynamics between the two cortical hemispheres can stitch together WM traces across visual hemifields.