Multi-structure Cortical States Deduced From Intracellular Representations of Fixed Tactile Input Patterns

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.

Local field potential sharp waves with diversified impact on cortical neuronal encoding of haptic input

Cortical sensory processing is greatly impacted by internally generated activity. But controlling for that activity is difficult since the thalamocortical network is a high-dimensional system with rapid state changes. Therefore, to unwind the cortical computational architecture there is a need for physiological 'landmarks' that can be used as frames of reference for computational state. Here we use a waveshape transform method to identify conspicuous local field potential sharp waves (LFP-SPWs) in the somatosensory cortex (S1). LFP-SPW events triggered short-lasting but massive neuronal activation in all recorded neurons with a subset of neurons initiating their activation up to 20 ms before the LFP-SPW onset. In contrast, LFP-SPWs differentially impacted the neuronal spike responses to ensuing tactile inputs, depressing the tactile responses in some neurons and enhancing them in others. When LFP-SPWs coactivated with more distant cortical surface (ECoG)-SPWs, suggesting an involvement of these SPWs in global cortical signaling, the impact of the LFP-SPW on the neuronal tactile response could change substantially, including inverting its impact to the opposite. These cortical SPWs shared many signal fingerprint characteristics as reported for hippocampal SPWs and may be a biomarker for a particular type of state change that is possibly shared byboth hippocampus and neocortex.

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.

Differential encoding of temporally evolving color patterns across nearby V1 neurons

Whereas studies of the V1 cortex have focused mainly on neural line orientation preference, color inputs are also known to have a strong presence among these neurons. Individual neurons typically respond to multiple colors and nearby neurons have different combinations of preferred color inputs. However, the computations performed by V1 neurons on such color inputs have not been extensively studied. Here we aimed to address this issue by studying how different V1 neurons encode different combinations of inputs composed of four basic colors. We quantified the decoding accuracy of individual neurons from multi-electrode array recordings, comparing multiple individual neurons located within 2 mm along the vertical axis of the V1 cortex of the anesthetized rat. We found essentially all V1 neurons to be good at decoding spatiotemporal patterns of color inputs and they did so by encoding them in different ways. Quantitative analysis showed that even adjacent neurons encoded the specific input patterns differently, suggesting a local cortical circuitry organization which tends to diversify rather than unify the neuronal responses to each given input. Using different pairs of monocolor inputs, we also found that V1 neocortical neurons had a diversified and rich color opponency across the four colors, which was somewhat surprising given the fact that rodent retina express only two different types of opsins. We propose that the processing of color inputs in V1 cortex is extensively composed of multiple independent circuitry components that reflect abstract functionalities resident in the internal cortical processing rather than the raw sensory information per se.

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