Widespread Decoding of Tactile Input Patterns Among Thalamic Neurons

High-dimensional cortical signals reveal rich bimodal and working memory-like representations among S1 neuron populations

Complexity is important for flexibility of natural behavior and for the remarkably efficient learning of the brain. Here we assessed the signal complexity among neuron populations in somatosensory cortex (S1). To maximize our chances of capturing population-level signal complexity, we used highly repeatable resolvable visual, tactile, and visuo-tactile inputs and neuronal unit activity recorded at high temporal resolution. We found the state space of the spontaneous activity to be extremely high-dimensional in S1 populations. Their processing of tactile inputs was profoundly modulated by visual inputs and even fine nuances of visual input patterns were separated. Moreover, the dynamic activity states of the S1 neuron population signaled the preceding specific input long after the stimulation had terminated, i.e., resident information that could be a substrate for a working memory. Hence, the recorded high-dimensional representations carried rich multimodal and internal working memory-like signals supporting high complexity in cortical circuitry operation.

Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces

Objective. Our goal is to decode firing patterns of single neurons in the left ventralis intermediate nucleus (Vim) of the thalamus, related to speech production, perception, and imagery. For realistic speech brain-machine interfaces (BMIs), we aim to characterize the amount of thalamic neurons necessary for high accuracy decoding.Approach. We intraoperatively recorded single neuron activity in the left Vim of eight neurosurgical patients undergoing implantation of deep brain stimulator or RF lesioning during production, perception and imagery of the five monophthongal vowel sounds. We utilized the Spade decoder, a machine learning algorithm that dynamically learns specific features of firing patterns and is based on sparse decomposition of the high dimensional feature space.Main results. Spade outperformed all algorithms compared with, for all three aspects of speech: production, perception and imagery, and obtained accuracies of 100%, 96%, and 92%, respectively (chance level: 20%) based on pooling together neurons across all patients. The accuracy was logarithmic in the amount of neurons for all three aspects of speech. Regardless of the amount of units employed, production gained highest accuracies, whereas perception and imagery equated with each other.Significance. Our research renders single neuron activity in the left Vim a promising source of inputs to BMIs for restoration of speech faculties for locked-in patients or patients with anarthria or dysarthria to allow them to communicate again. Our characterization of how many neurons are necessary to achieve a certain decoding accuracy is of utmost importance for planning BMI implantation.

ECoG activity distribution patterns detects global cortical responses following weak tactile inputs

Many studies have suggested that the neocortex operates as a global network of functionally interconnected neurons, indicating that any sensory input could shift activity distributions across the whole brain. A tool assessing the activity distribution across cortical regions with high temporal resolution could then potentially detect subtle changes that may pass unnoticed in regionalized analyses. We used eight-channel, distributed electrocorticogram (ECoG) recordings to analyze changes in global activity distribution caused by single pulse electrical stimulations of the paw. We analyzed the temporally evolving patterns of the activity distributions using principal component analysis (PCA). We found that the localized tactile stimulation caused clearly measurable changes in global ECoG activity distribution. These changes in signal activity distribution patterns were detectable across a small number of ECoG channels, even when excluding the somatosensory cortex, suggesting that the method has high sensitivity, potentially making it applicable to human electroencephalography (EEG) for detection of pathological changes.

Hippocampal output profoundly impacts the interpretation of tactile input patterns in SI cortical neurons

Due to continuous state variations in neocortical circuits, individual somatosensory cortex (SI) neurons in vivo display a variety of intracellular responses to the exact same spatiotemporal tactile input pattern. To manipulate the internal cortical state, we here used brief electrical stimulation of the output region of the hippocampus, which preceded the delivery of specific tactile afferent input patterns to digit 2 of the anesthetized rat. We find that hippocampal output had a diversified, remarkably strong impact on the intracellular response types displayed by each neuron in the primary SI to each given tactile input pattern. Qualitatively, this impact was comparable to that previously described for cortical output, which was surprising given the widely assumed specific roles of the hippocampus, such as in cortical memory formation. The findings show that hippocampal output can profoundly impact the state-dependent interpretation of tactile inputs and hence influence perception, potentially with affective and semantic components.

Remote cortical perturbation dynamically changes the network solutions to given tactile inputs in neocortical neurons

The neocortex has a globally encompassing network structure, which for each given input constrains the possible combinations of neuronal activations across it. Hence, its network contains solutions. But in addition, the cortex has an ever-changing multidimensional internal state, causing each given input to result in a wide range of specific neuronal activations. Here we use intracellular recordings in somatosensory cortex (SI) neurons of anesthetized rats to show that remote, subthreshold intracortical electrical perturbation can impact such constraints on the responses to a set of spatiotemporal tactile input patterns. Whereas each given input pattern normally induces a wide set of preferred response states, when combined with cortical perturbation response states that did not otherwise occur were induced and consequently made other response states less likely. The findings indicate that the physiological network structure can dynamically change as the state of any given cortical region changes, thereby enabling a rich, multifactorial, perceptual capability.

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Tactile sensory input patterns evoke multi-structure cortical neuron responses

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Multi-structure responses are shown to be impacted by remote cortical regions

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Highly dynamic neuron responses reflects global cortical information integration

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Perception hence depends on globally distributed activity at the time of input

Widely Different Correlation Patterns Between Pairs of Adjacent Thalamic Neurons In vivo

We have previously reported different spike firing correlation patterns among pairs of adjacent pyramidal neurons within the same layer of S1 cortex in vivo, which was argued to suggest that acquired synaptic weight modifications would tend to differentiate adjacent cortical neurons despite them having access to near-identical afferent inputs. Here we made simultaneous single-electrode loose patch-clamp recordings from 14 pairs of adjacent neurons in the lateral thalamus of the ketamine-xylazine anesthetized rat in vivo to study the correlation patterns in their spike firing. As the synapses on thalamic neurons are dominated by a high number of low weight cortical inputs, which would be expected to be shared for two adjacent neurons, and as far as thalamic neurons have homogenous membrane physiology and spike generation, they would be expected to have overall similar spike firing and therefore also correlation patterns. However, we find that across a variety of thalamic nuclei the correlation patterns between pairs of adjacent thalamic neurons vary widely. The findings suggest that the connectivity and cellular physiology of the thalamocortical circuitry, in contrast to what would be expected from a straightforward interpretation of corticothalamic maps and uniform intrinsic cellular neurophysiology, has been shaped by learning to the extent that each pair of thalamic neuron has a unique relationship in their spike firing activity.