The successful development of motor neuroprosthetic devices hinges on the ability to accurately and reliably decode signals from the brain. Motor neuroprostheses are widely investigated in behaving non-human primates, but technical constraints have limited progress in optimizing performance. In particular, the organization of movement-related neuronal activity across cortical layers remains poorly understood due, in part, to the widespread use of fixed-geometry multielectrode arrays. In this study, we use chronically-implanted multielectrode arrays with individually movable electrodes to examine how the encoding of movement goals depends on cortical depth. In a series of recordings spanning several months, we varied the depth of each electrode in the pre-arcuate gyrus of frontal cortex in two monkeys as they performed memory-guided eye movements. We decode eye movement goals from local field potentials (LFPs) and multiunit spiking activity recorded across a range of depths up to 3 mm from the cortical surface. We show that both LFP and multiunit signals yield the highest decoding performance at superficial sites, within 0.5 mm of the cortical surface, while performance degrades substantially at sites deeper than 1 mm. We also analyze performance by varying bandpass filtering characteristics and simulating changes in microelectrode array channel count and density. The results indicate that the performance of LFP-based neuroprostheses strongly depends on recording configuration and that recording depth is a critical parameter limiting system performance.
Lateral prefrontal cortex (PFC) is regarded as the hub of the brain's working memory (WM) system, but it remains unclear whether WM is supported by a single distributed network or multiple specialized network components in this region. To investigate this problem, we recorded from neurons in PFC while monkeys made delayed eye movements guided by memory or vision. We show that neuronal responses during these tasks map to three anatomically specific modes of persistent activity. The first two modes encode early and late forms of information storage, whereas the third mode encodes response preparation. Neurons that reflect these modes are concentrated at different anatomical locations in PFC and exhibit distinct patterns of coordinated firing rates and spike timing during WM, consistent with distinct networks. These findings support multiple component models of WM and consequently predict distinct failures that could contribute to neurologic dysfunction.working memory | prefrontal cortex | macaque | coherence H igh-level cognition depends on the ability to translate stored information about recent experience into a behaviorally appropriate response, an ability known as working memory (WM). WM relies on a storage process that actively maintains information and a control process that manipulates stored information to support the selection and preparation of a contingent response (1-3). The neural mechanisms that support WM involve networks that are broadly distributed throughout the brain (4-7) and rely heavily on the prefrontal cortex (PFC) for normal operation (6-9). However, the degree to which WM is supported by a single distributed network or multiple specialized network components in PFC remains unclear (6, 10, 11), hindering progress in the search for neurocognitive therapies to treat disorders of cognition (12).Persistent spiking activity is commonly thought to reflect the mechanistic basis of WM in PFC (13-16). This activity manifests in different ways, including time-varying neuronal responses that decay, ramp up, or are stable in time during memory delays. Although such a diversity of responses could reflect distinct modes of persistent activity, it has long been a standard practice to treat all persistently active neurons in PFC as representative of a single composite WM function that supports the maintenance and manipulation of information necessary for memory-guided behavior (14,(17)(18)(19). The implicit assumption that the representations of stored information and contingent responses overlap at the neural circuit level contrasts with an alternate view, which suggests that PFC primarily encodes the selection and preparation of responses (6,10,11). This difference highlights the need to directly investigate the circuit-level organization of storage and response preparationrelated activity in PFC.We address this problem here, using a simple manipulation of WM in concert with large-scale recordings from neurons across lateral PFC of macaque monkeys. By mapping neural activity during memory and visual del...
During behavior, the oculomotor system is tasked with selecting objects from an ever-changing visual field and guiding eye movements to these locations. The attentional priority given to visual targets during selection can be strongly influenced by external stimulus properties or internal goals based on previous experience. Although these exogenous and endogenous drivers of selection are known to operate across partially overlapping time scales, the form of their interaction over time remains poorly understood. Using a novel choice task that simultaneously manipulates stimulus- and goal-driven attention, we demonstrate that exogenous and endogenous attentional biases change linearly as a function of time after stimulus onset and have an additive influence on the visual selection process in rhesus macaques (Macaca mulatta). We present a family of computational models that quantify this interaction over time and detail the history-dependence of both processes. The computational models reveal the existence of a critical 140-180 ms attentional “switching” time, when stimulus and goal-driven processes simultaneously favor competing visual targets. These results suggest that the brain uses a linear sum of attentional biases to guide visual selection.
Although gamma frequency oscillations are common in the brain, their functional contributions to neural computation are not understood. Here we report in vitro electrophysiological recordings to evaluate how noisy gamma frequency oscillatory input interacts with the overall activation level of a neuron to determine the precise timing of its action potentials. The experiments were designed to evaluate spike synchrony in a neural circuit architecture in which a population of neurons receives a common noisy gamma oscillatory synaptic drive while the firing rate of each individual neuron is determined by a slowly varying independent input. We demonstrate that similarity of firing rate is a major determinant of synchrony under common noisy oscillatory input: Near coincidence of spikes at similar rates gives way to substantial desynchronization at larger firing rate differences. Analysis of this rate-specific synchrony phenomenon reveals distinct spike timing ''fingerprints'' at different firing rates that emerge through a combination of phase shifting and abrupt changes in spike patterns. We further demonstrate that rate-specific synchrony permits robust detection of rate similarity in a population of neurons through synchronous activation of a postsynaptic neuron, supporting the biological plausibility of a Many Are Equal computation. Our results reveal that spatially coherent noisy oscillations, which are common throughout the brain, can generate previously unknown relationships among neural rate codes, noisy interspike intervals, and precise spike synchrony codes. All of these can coexist in a self-consistent manner because of rate-specific synchrony.gamma oscillations ͉ neural code ͉ neural computation G amma oscillations (30-100 Hz) are observed in field potential recordings from many brain areas (1-4). These oscillations are typically noisy, exhibiting fluctuations in amplitude and a broad frequency distribution. In vitro experiments using cortical brain slices (5) have demonstrated that gamma oscillations can be produced by sustained activation of networks of inhibitory neurons, which in turn produce highly correlated rhythmic membrane potential oscillations in the local population of pyramidal cells (6). This raises the general question of what role common noisy oscillatory synaptic inputs might play in producing synchronous action potentials across a population of neurons with differing mean firing rates. In the presence of correlated noisy gamma, neurons with the same mean firing rate would be expected to produce highly correlated spike trains (7). But what is the level of synchrony that will result when the mean firing rates are different? These correlations would be functionally important because relative spike timing on the millisecond time scale influences synaptic activation of postsynaptic targets (8, 9), timing-dependent short-term synaptic plasticity (10, 11), and pattern recognition (12).Previous work has shown that weakly correlated noisy input to a pair of neurons produces an output correlation p...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.