Contribution of behavioural variability to representational drift

Neuronal responses to similar stimuli change dynamically over time, raising the question of how internal representations can provide a stable substrate for neural coding. While the drift of these representations is mostly characterized in relation to external stimuli or tasks, behavioural or internal state of the animal is also known to modulate the neural activity. We therefore asked how the variability of such modulatory mechanisms can contribute to representational drift. By analysing publicly available datasets from the Allen Brain Observatory, we found that behavioural variability significantly contributes to changes in stimulus-induced neuronal responses across various cortical areas in the mouse. This effect could not be explained by a gain model in which change in the behavioural state scaled the signal or the noise. A better explanation was provided by a model in which behaviour contributed independently to neuronal tuning. Our results are consistent with a view in which behaviour modulates the low-dimensional, slowly-changing setpoints of neurons, upon which faster operations like sensory processing are performed. Importantly, our analysis suggests that reliable but variable behavioural signals might be misinterpreted as representational drift, if neuronal representations are only characterized in the stimulus space and marginalised over behavioural parameters.

V1 receptive field structure contributes to neuronal response latency

The timing of neuronal responses is considered to be important for information transferring and communication across individual neurons. However, the sources of variabilities in the timing of neuronal responses are not well understood and sometimes over-interpreted. A systematic variability in the response latencies of the primary visual cortex has been reported in presence of drifting grating stimulus. Whereas the response latencies are systematically dependent on stimulus orientation. To understand the underlying mechanism of these systematic latencies, we recorded the neuronal response of the cat visual cortex, area 17, and simulated the response latency of V1 neurons, with two geometric models. We showed that outputs of these two models significantly predict the response latencies of the electrophysiology recording during orientation tasks. The periodic patterns created in the raster plots were dependent on the relative position of the stimulus rotation center and the receptive-field sub-regions. We argue the position of stimulus is contributing to systematic response latencies, dependent on drifting orientation. Therefore, we provide a toolbox based on our geometrical model for determining the exact location of RF sub-regions. Our result indicates that a major source of neuronal variability is the lack of fine-tuning in the task parameters. Considering the simplicity of the orientation selectivity task, we argue fine-tuning of stimulus properties is crucial for deduction of neural variability in higher-order cortical areas and understanding their neural dynamics.

Hyaluronan: A Neuroimmune Modulator in the Microbiota-Gut Axis

The commensal microbiota plays a fundamental role in maintaining host gut homeostasis by controlling several metabolic, neuronal and immune functions. Conversely, changes in the gut microenvironment may alter the saprophytic microbial community and function, hampering the positive relationship with the host. In this bidirectional interplay between the gut microbiota and the host, hyaluronan (HA), an unbranched glycosaminoglycan component of the extracellular matrix, has a multifaceted role. HA is fundamental for bacterial metabolism and influences bacterial adhesiveness to the mucosal layer and diffusion across the epithelial barrier. In the host, HA may be produced and distributed in different cellular components within the gut microenvironment, playing a role in the modulation of immune and neuronal responses. This review covers the more recent studies highlighting the relevance of HA as a putative modulator of the communication between luminal bacteria and the host gut neuro-immune axis both in health and disease conditions, such as inflammatory bowel disease and ischemia/reperfusion injury.

Mapping the visual world to the human brain

The visual maps measured non-invasively in the brain of human and non-human primates reliably reflect the underlying neuronal responses recorded with invasive electrodes.

Relationship between high-frequency activity in the sensory and the motor hand areas, and regional myelin content

BackgroundThe human primary sensory (S1) and primary motor (M1) hand areas feature high-frequency neuronal responses. Electrical nerve stimulation evokes high-frequency oscillations (HFO) at around 650 Hz in the contralateral S1. Likewise, paired-pulse transcranial magnetic stimulation of M1 produces short interval intracortical facilitation (SICF) of motor evoked potentials in contralateral hand muscles. SICF features several peaks of facilitation which are separated by inter-peak intervals resembling HFO rhythmicity.HypothesisIn this study, we tested the hypothesis that the individual expressions of HFO and SICF are tightly related to each other and to the regional myelin content in the sensorimotor cortex.MethodsIn 24 healthy volunteers, we recorded HFO and SICF, and, in a subgroup of 20 participants, we mapped the cortical myelin content using the ratio between the T1- and T2-weighted MRI signal as read-out.ResultsThe individual frequencies and magnitudes of HFO and SICF were tightly correlated: the intervals between the first and second peak of cortical HFO and SICF showed a positive linear relationship (r= 0.703, p< 0.001), while their amplitudes were inversely related (r= −0.613, p= 0.001). The rhythmicity, but not the magnitude of the high-frequency responses, was related to the cortical myelin content: the higher the cortical myelin content, the shorter the inter-peak intervals of HFO and SICF.ConclusionThe results confirm a tight functional relationship between high-frequency responses in S1 (i.e., HFO) and M1 (i.e., SICF). They also establish a link between the degree of regional cortical myelination and the expression of high-frequency responses in the human cortex, giving further the opportunity to infer their possible generators.

Does the brain care about averages? A simple test.

Trial-averaged metrics, e.g. in the form of tuning curves and population response vectors, are a basic and widely accepted way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing in a specific context. The test probes two assumptions inherent in the usage of average neuronal metrics: 1) Reliability: Neuronal responses repeat consistently enough across single stimulus instances that the average response template they relate to remains recognizable to downstream regions. 2) Behavioural relevance: If a single-trial response is more similar to the average template, this should make it easier for the animal to identify the correct stimulus or action. We apply this test to a large publicly available data set featuring electrophysiological recordings from 42 cortical areas in behaving mice. In this data set, we show that single-trial responses were less correlated to the average response template than one would expect if they simply represented discrete versions of the template, down-sampled to a finite number of spikes. Moreover, single-trial responses were barely stimulus-specific — they could not be clearly assigned to the average response template of one stimulus. Most importantly, better-matched single-trial responses did not predict accurate behaviour for any of the recorded cortical areas. We conclude that in this data set, average responses do not seem particularly relevant to neuronal computation in a majority of brain areas, and we encourage other researchers to apply similar tests when using trial-averaged neuronal metrics.

Transient inhibition to light explains stronger V1 responses to dark stimuli

Neurons in the primary visual cortex (V1) receive excitation and inhibition from two different pathways processing lightness (ON) and darkness (OFF). V1 neurons overall respond more strongly to dark than light stimuli (Yeh, Xing and Shapley, 2010; Kremkow et al., 2014), consistent with a preponderance of darker regions in natural images (Ratliff et al., 2010), as well as human psychophysics (Buchner & Baumgartner, 2007). However, it has been unclear whether this "dark-dominance" is due to more excitation from the OFF pathway (Jin et al., 2008) or more inhibition from the ON pathway (Taylor et al., 2018). To understand the mechanisms behind dark-dominance, we record electrophysiological responses of individual simple-type V1 neurons to natural image stimuli and then train biologically inspired convolutional neural networks to predict the neuronal responses. Analyzing a sample of 74 neurons (in anesthetized, paralyzed cats) has revealed their responses to be more driven by dark than light stimuli, consistent with previous investigations (Yeh et al., 2010; Kremkow et al., 2013). We show this asymmetry to be predominantly due to slower inhibition to dark stimuli rather than by stronger excitation from the thalamocortical OFF pathway. Consistent with dark-dominant neurons having faster responses than light-dominant neurons (Komban et al., 2014), we find dark-dominance to solely occur in the early latencies of neuronal responses. Neurons that are strongly dark-dominated also tend to be less orientation selective. This novel approach gives us new insight into the dark-dominance phenomenon and provides an avenue to address new questions about excitatory and inhibitory integration in cortical neurons.

Visual prototypes in the ventral stream are attuned to complexity and gaze behavior

Early theories of efficient coding suggested the visual system could compress the world by learning to represent features where information was concentrated, such as contours. This view was validated by the discovery that neurons in posterior visual cortex respond to edges and curvature. Still, it remains unclear what other information-rich features are encoded by neurons in more anterior cortical regions (e.g., inferotemporal cortex). Here, we use a generative deep neural network to synthesize images guided by neuronal responses from across the visuocortical hierarchy, using floating microelectrode arrays in areas V1, V4 and inferotemporal cortex of two macaque monkeys. We hypothesize these images (“prototypes”) represent such predicted information-rich features. Prototypes vary across areas, show moderate complexity, and resemble salient visual attributes and semantic content of natural images, as indicated by the animals’ gaze behavior. This suggests the code for object recognition represents compressed features of behavioral relevance, an underexplored aspect of efficient coding.

Spontaneous traveling waves naturally emerge from horizontal fiber time delays and travel through locally asynchronous-irregular states

Studies of sensory-evoked neuronal responses often focus on mean spike rates, with fluctuations treated as internally-generated noise. However, fluctuations of spontaneous activity, often organized as traveling waves, shape stimulus-evoked responses and perceptual sensitivity. The mechanisms underlying these waves are unknown. Further, it is unclear whether waves are consistent with the low rate and weakly correlated “asynchronous-irregular” dynamics observed in cortical recordings. Here, we describe a large-scale computational model with topographically-organized connectivity and conduction delays relevant to biological scales. We find that spontaneous traveling waves are a general property of these networks. The traveling waves that occur in the model are sparse, with only a small fraction of neurons participating in any individual wave. Consequently, they do not induce measurable spike correlations and remain consistent with locally asynchronous irregular states. Further, by modulating local network state, they can shape responses to incoming inputs as observed in vivo.