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Magnetoencephalography (MEG) and Electroencephalography (EEG) provide direct electrophysiological measures at an excellent temporal resolution, but the spatial resolution of source-reconstructed current activity is limited to several millimetres. Here we show, using simulations of MEG signals and Bayesian model comparison, that non-invasive myelin estimates from high-resolution quantitative magnetic resonance imaging (MRI) can enhance MEG/EEG source reconstruction. Our approach assumes that MEG/EEG signals primarily arise from the synchronised activity of pyramidal cells, and since most of the myelin in the cortical sheet originates from these cells, myelin density can predict the strength of cortical sources measured by MEG/EEG. Leveraging recent advances in quantitative MRI, we exploit this structure-function relationship and scale the leadfields of the forward model according to the local myelin density estimates from in vivo quantitative MRI to inform MEG/EEG source reconstruction. Using Bayesian model comparison and dipole localisation errors (DLEs), we demonstrate that adapting local forward fields to reflect increased local myelination at the site of a simulated source explains the simulated data better than models without such leadfield scaling. Our model comparison framework proves sensitive to myelin changes in simulations with exact coregistration and moderate-to-high sensor-level signal-to-noise ratios (≥10 dB) for the multiple sparse priors (MSP) and empirical Bayesian beamformer (EBB) approaches. Furthermore, we sought to infer the microstructure giving rise to specific functional activation patterns by comparing the myelin-informed model which was used to generate the activation with a set of test forward models incorporating different myelination patterns. We found that the direction of myelin changes, however not their magnitude, can be inferred by Bayesian model comparison. Finally, we apply myelin-informed forward models to MEG data from a visuo-motor experiment. We demonstrate improved source reconstruction accuracy using myelin estimates from a quantitative longitudinal relaxation (R1) map and discuss the limitations of our approach.
Highlights
We use quantitative MRI to implement myelin-informed forward models for M/EEG
Local myelin density was modelled by adapting the local leadfields
Myelin-informed forward models can improve source reconstruction accuracy
We can infer the directionality of myelination patterns, but not their strength
We apply our approach to MEG data from a visuo-motor experiment
Snapshots of acetyl-CoA synthesis, the final step of CO₂ fixation in the Wood-Ljungdahl pathway
(2024)
In the ancient microbial Wood-Ljungdahl pathway, CO2 is fixed in a multi-step process with acetyl-CoA synthesis at the bifunctional carbon monoxide dehydrogenase/acetyl-CoA synthase complex (CODH/ACS). Here, we present catalytic snapshots of the CODH/ACS from the gas-converting acetogen Clostridium autoethanogenum, characterizing the molecular choreography of the overall reaction including electron transfer to the CODH for CO2 reduction, methyl transfer from the corrinoid iron-sulfur protein (CoFeSP) partner to the ACS active site and acetyl-CoA production. Unlike CODH, the multidomain ACS undergoes large conformational changes to form an internal connection to the CODH active site, accommodate the CoFeSP for methyl transfer and protect the reaction intermediates. Altogether, the structures allow us to draw a detailed reaction mechanism of this enzyme crucial for CO2 fixation in anaerobic organisms.
The intensity and the features of sensory stimuli are encoded in the activity of neurons in the cortex. In the visual and piriform cortices, the stimulus intensity rescales the activity of the population without changing its selectivity for the stimulus features. The cortical representation of the stimulus is therefore intensity invariant. This emergence of network-invariant representations appears robust to local changes in synaptic strength induced by synaptic plasticity, even though (i) synaptic plasticity can potentiate or depress connections between neurons in a feature-dependent manner, and (ii) in networks with balanced excitation and inhibition, synaptic plasticity determines the nonlinear network behavior. In this study we investigate the consistency of invariant representations with a variety of synaptic states in balanced networks. By using mean-field models and spiking network simulations, we show how the synaptic state controls the emergence of intensity-invariant or intensity-dependent selectivity. In particular, we demonstrate that an effective power-law synaptic transformation at the population level is necessary for invariance. In a range of firing rates, purely depressing short-term synapses fulfills this condition, and in this case, the network is contrast-invariant. Instead, facilitating short-term plasticity generally narrows the network selectivity. We found that facilitating and depressing short-term plasticity can be combined to approximate a power-law that leads to contrast invariance. These results explain how the physiology of individual synapses is linked to the emergence of invariant representations of sensory stimuli at the network level.
Owing to their morphological complexity and dense network connections, neurons modify their proteomes locally, using mRNAs and ribosomes present in the neuropil (tissue enriched for dendrites and axons). Although ribosome biogenesis largely takes place in the nucleus and perinuclear region, neuronal ribosomal protein (RP) mRNAs have been frequently detected remotely, in dendrites and axons. Here, using imaging and ribosome profiling, we directly detected the RP mRNAs and their translation in the neuropil. Combining brief metabolic labeling with mass spectrometry, we found that a group of RPs rapidly associated with translating ribosomes in the cytoplasm and that this incorporation was independent of canonical ribosome biogenesis. Moreover, the incorporation probability of some RPs was regulated by location (neurites vs. cell bodies) and changes in the cellular environment (following oxidative stress). Our results suggest new mechanisms for the local activation, repair and/or specialization of the translational machinery within neuronal processes, potentially allowing neuronal synapses a rapid means to regulate local protein synthesis.
Owing to their morphological complexity and dense network connections, neurons modify their proteomes locally, using mRNAs and ribosomes present in the neuropil (tissue enriched for dendrites and axons). Although ribosome biogenesis largely takes place in the nucleus and perinuclear region, neuronal ribosomal protein (RP) mRNAs have been frequently detected remotely, in dendrites and axons. Here, using imaging and ribosome profiling, we directly detected the RP mRNAs and their translation in the neuropil. Combining brief metabolic labeling with mass spectrometry, we found that a group of RPs quickly associated with translating ribosomes in the cytoplasm and that this incorporation is independent of canonical ribosome biogenesis. Moreover, the incorporation probability of some RPs was regulated by location (neurites vs. cell bodies) and changes in the cellular environment (in response to oxidative stress). Our results suggest new mechanisms for the local activation, repair and/or specialization of the translational machinery within neuronal processes, potentially allowing remote neuronal synapses a rapid solution to the relatively slow and energy-demanding requirement of nuclear ribosome biogenesis.
Protein turnover, the net result of protein synthesis and degradation, enables cells to remodel their proteomes in response to internal and external cues. Previously, we analyzed protein turnover rates in cultured brain cells under basal neuronal activity and found that protein turnover is influenced by subcellular localization, protein function, complex association, cell type of origin, and by the cellular environment (Dörrbaum et al., 2018). Here, we advanced our experimental approach to quantify changes in protein synthesis and degradation, as well as the resulting changes in protein turnover or abundance in rat primary hippocampal cultures during homeostatic scaling. Our data demonstrate that a large fraction of the neuronal proteome shows changes in protein synthesis and/or degradation during homeostatic up- and down-scaling. More than half of the quantified synaptic proteins were regulated, including pre- as well as postsynaptic proteins with diverse molecular functions.
EphrinB2 and GRIP1 stabilize mushroom spines during denervation-induced homeostatic plasticity
(2021)
Highlights
• Denervation induces mushroom spine loss and AMPAR redistribution to the surface
• GRIP1 and ephrinB2 mediate homeostatic mechanisms after lesion
• Stimulation with the ephrinB2 receptor EphB4 promotes a surface shift of AMPARs
• AMPARs surface shift restores impaired spine recovery after lesion in GRIP1 mutants
Summary
Despite decades of work, much remains elusive about molecular events at the interplay between physiological and structural changes underlying neuronal plasticity. Here, we combined repetitive live imaging and expansion microscopy in organotypic brain slice cultures to quantitatively characterize the dynamic changes of the intracellular versus surface pools of GluA2-containing α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) across the different dendritic spine types and the shaft during hippocampal homeostatic plasticity. Mechanistically, we identify ephrinB2 and glutamate receptor interacting protein (GRIP) 1 as mediating AMPAR relocation to the mushroom spine surface following lesion-induced denervation. Moreover, stimulation with the ephrinB2 specific receptor EphB4 not only prevents the lesion-induced disappearance of mushroom spines but is also sufficient to shift AMPARs to the surface and rescue spine recovery in a GRIP1 dominant-negative background. Thus, our results unravel a crucial role for ephrinB2 during homeostatic plasticity and identify a potential pharmacological target to improve dendritic spine plasticity upon injury.
An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. brain signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded redundant and synergistic information during auditory prediction error processing. In both tasks, we observed multiple patterns of synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between auditory and frontal regions. Using a brain-constrained neural network, we simulated the spatio-temporal patterns of synergy and redundancy observed in the experimental results and further demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback and feedforward connections. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.
Parallel multisite recordings in the visual cortex of trained monkeys revealed that the responses of spatially distributed neurons to natural scenes are ordered in sequences. The rank order of these sequences is stimulus-specific and maintained even if the absolute timing of the responses is modified by manipulating stimulus parameters. The stimulus specificity of these sequences was highest when they were evoked by natural stimuli and deteriorated for stimulus versions in which certain statistical regularities were removed. This suggests that the response sequences result from a matching operation between sensory evidence and priors stored in the cortical network. Decoders trained on sequence order performed as well as decoders trained on rate vectors but the former could decode stimulus identity from considerably shorter response intervals than the latter. A simulated recurrent network reproduced similarly structured stimulus-specific response sequences, particularly once it was familiarized with the stimuli through non-supervised Hebbian learning. We propose that recurrent processing transforms signals from stationary visual scenes into sequential responses whose rank order is the result of a Bayesian matching operation. If this temporal code were used by the visual system it would allow for ultrafast processing of visual scenes.
Solving the problem of consciousness remains one of the biggest challenges in modern science. One key step towards understanding consciousness is to empirically narrow down neural processes associated with the subjective experience of a particular content. To unravel these neural correlates of consciousness (NCC) a common scientific strategy is to compare perceptual conditions in which consciousness of a particular content is present with those in which it is absent, and to determine differences in measures of brain activity (the so called "contrastive analysis"). However, this comparison appears not to reveal exclusively the NCC, as the NCC proper can be confounded with prerequisites for and consequences of conscious processing of the particular content. This implies that previous results cannot be unequivocally interpreted as reflecting the neural correlates of conscious experience. Here we review evidence supporting this conjecture and suggest experimental strategies to untangle the NCC from the prerequisites and consequences of conscious experience in order to further develop the otherwise valid and valuable contrastive methodology.
In order to investigate the involvement of primary visual cortex (V1) in working memory (WM), parallel, multisite recordings of multiunit activity were obtained from monkey V1 while the animals performed a delayed match-to-sample (DMS) task. During the delay period, V1 population firing rate vectors maintained a lingering trace of the sample stimulus that could be reactivated by intervening impulse stimuli that enhanced neuronal firing. This fading trace of the sample did not require active engagement of the monkeys in the DMS task and likely reflects the intrinsic dynamics of recurrent cortical networks in lower visual areas. This renders an active, attention-dependent involvement of V1 in the maintenance of working memory contents unlikely. By contrast, population responses to the test stimulus depended on the probabilistic contingencies between sample and test stimuli. Responses to tests that matched expectations were reduced which agrees with concepts of predictive coding.
Emerging evidence indicates that protein synthesis and degradation are necessary for the remodeling of synapses. These two processes govern cellular protein turnover, are tightly regulated, and are modulated by neuronal activity in time and space. The anisotropic anatomy of the neurons presents a challenge for the study of protein turnover, but the understanding of protein turnover in neurons and its modulation in response to activity can help us to unravel the fine-tuned changes that occur at synapses in response to activity. Here we review the key experimental evidence demonstrating the role of protein synthesis and degradation in synaptic plasticity, as well as the turnover rates of specific neuronal proteins.
In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g. sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a ‘goal function’, of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. ‘edge filtering’, ‘working memory’). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon’s mutual information), we argue that neural information processing crucially depends on the combination of multiple inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax and coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called ‘coding with synergy’, which builds on combining external input and prior knowledge in a synergistic manner. We suggest that this novel goal function may be highly useful in neural information processing.
Gephyrin is an ubiquitously expressed protein that, in the nervous system, is essential for synaptic anchoring of glycine receptors (GlyRs) and major GABAA receptor subtypes. The binding of gephyrin to the GlyR depends on an amphipathic motif within the large intracellular loop of the GlyRβ subunit. The mouse gephyrin gene consists of 30 exons. Ten of these exons, encoding cassettes of 5–40 amino acids, are subject to alternative splicing (C1–C7, C4′–C6′). Since one of the cassettes, C5′, has recently been reported to exclude GlyRs from GABAergic synapses, we investigated which cassettes are found in gephyrin associated with the GlyR. Gephyrin variants were purified from rat spinal cord, brain, and liver by binding to the glutathione S-transferase-tagged GlyRβ loop or copurified with native GlyR from spinal cord by affinity chromatography and analyzed by mass spectrometry. In addition to C2 and C6′, already known to be prominent, C4 was found to be abundant in gephyrin from all tissues examined. The nonneuronal cassette C3 was easily detected in liver but not in GlyR-associated gephyrin from spinal cord. C5 was present in brain and spinal cord polypeptides, whereas C5′ was coisolated mainly from liver. Notably C5′-containing gephyrin bound to the GlyRβ loop, inconsistent with its proposed selectivity for GABAA receptors. Our data show that GlyR-associated gephyrin, lacking C3, but enriched in C4 without C5, differs from other neuronal and nonneuronal gephyrin isoforms.
The inhibitory glycine receptor (GlyR) in developing spinal neurones is internalized efficiently upon antagonist inhibition. Here we used surface labeling combined with affinity purification to show that homopentameric α1 GlyRs generated inXenopus oocytes are proteolytically nicked into fragments of 35 and 13 kDa upon prolonged incubation. Nicked GlyRs do not exist at the cell surface, indicating that proteolysis occurs exclusively in the endocytotic pathway. Consistent with this interpretation, elevation of the lysosomal pH, but not the proteasome inhibitor lactacystin, prevents GlyR cleavage. Prior to internalization, α1 GlyRs are conjugated extensively with ubiquitin in the plasma membrane. Our results are consistent with ubiquitination regulating the endocytosis and subsequent proteolysis of GlyRs residing in the plasma membrane. Ubiquitin-conjugating enzymes thus may have a crucial role in synaptic plasticity by determining postsynaptic receptor numbers.
In a dynamic environment, the already limited information that human working memory can maintain needs to be constantly updated to optimally guide behaviour. Indeed, previous studies showed that working memory representations are continuously being transformed during delay periods leading up to a response. This goes hand-in-hand with the removal of task-irrelevant items. However, does such removal also include veridical, original stimuli, as they were prior to transformation? Here we aimed to assess the neural representation of task-relevant transformed representations, compared to the no-longer-relevant veridical representations they originated from. We applied multivariate pattern analysis to electroencephalographic data during maintenance of orientation gratings with and without mental rotation. During maintenance, we perturbed the representational network by means of a visual impulse stimulus, and were thus able to successfully decode veridical as well as imaginary, transformed orientation gratings from impulse-driven activity. On the one hand, the impulse response reflected only task-relevant (cued), but not task-irrelevant (uncued) items, suggesting that the latter were quickly discarded from working memory. By contrast, even though the original cued orientation gratings were also no longer task-relevant after mental rotation, these items continued to be represented next to the rotated ones, in different representational formats. This seemingly inefficient use of scarce working memory capacity was associated with reduced probe response times and may thus serve to increase precision and flexibility in guiding behaviour in dynamic environments.
Quantitative MRI maps of human neocortex explored using cell type-specific gene expression analysis
(2022)
Quantitative magnetic resonance imaging (qMRI) allows extraction of reproducible and robust parameter maps. However, the connection to underlying biological substrates remains murky, especially in the complex, densely packed cortex. We investigated associations in human neocortex between qMRI parameters and neocortical cell types by comparing the spatial distribution of the qMRI parameters longitudinal relaxation rate (equation ImEquation1), effective transverse relaxation rate (equation ImEquation2), and magnetization transfer saturation (MTsat) to gene expression from the Allen Human Brain Atlas, then combining this with lists of genes enriched in specific cell types found in the human brain. As qMRI parameters are magnetic field strength-dependent, the analysis was performed on MRI data at 3T and 7T. All qMRI parameters significantly covaried with genes enriched in GABA- and glutamatergic neurons, i.e. they were associated with cytoarchitecture. The qMRI parameters also significantly covaried with the distribution of genes enriched in astrocytes (equation ImEquation3 at 3T, equation ImEquation4 at 7T), endothelial cells (equation ImEquation5 and MTsat at 3T), microglia (equation ImEquation6 and MTsat at 3T, equation ImEquation7 at 7T), and oligodendrocytes and oligodendrocyte precursor cells (equation ImEquation8 at 7T). These results advance the potential use of qMRI parameters as biomarkers for specific cell types.
Quantitative MRI maps of human neocortex explored using cell type-specific gene expression analysis
(2022)
Quantitative MRI (qMRI) allows extraction of reproducible and robust parameter maps. However, the connection to underlying biological substrates remains murky, especially in the complex, densely packed cortex. We investigated associations in human neocortex between qMRI parameters and neocortical cell types by comparing the spatial distribution of the qMRI parameters longitudinal relaxation rate (R1), effective transverse relaxation rate (R2∗), and magnetization transfer saturation (MTsat) to gene expression from the Allen Human Brain Atlas, then combining this with lists of genes enriched in specific cell types found in the human brain. As qMRI parameters are magnetic field strength-dependent, the analysis was performed on MRI data at 3T and 7T. All qMRI parameters significantly covaried with genes enriched in GABA- and glutamatergic neurons, i.e. they were associated with cytoarchitecture. The qMRI parameters also significantly covaried with the distribution of genes enriched in astrocytes (R2∗ at 3T, R1 at 7T), endothelial cells (R1 and MTsat at 3T), microglia (R1 and MTsat at 3T, R1 at 7T), and oligodendrocytes (R1 at 7T). These results advance the potential use of qMRI parameters as biomarkers for specific cell types.
We explore the potential of optically-pumped magnetometers (OPMs) to infer the laminar origins of neural activity non-invasively. OPM sensors can be positioned closer to the scalp than conventional cryogenic MEG sensors, opening an avenue to higher spatial resolution when combined with high-precision forward modelling. By simulating the forward model projection of single dipole sources onto OPM sensor arrays with varying sensor densities and measurement axes, and employing sparse source reconstruction approaches, we find that laminar inference with OPM arrays is possible at relatively low sensor counts at moderate to high signal-to-noise ratios (SNR). We observe improvements in laminar inference with increasing spatial sampling densities and number of measurement axes. Surprisingly, moving sensors closer to the scalp is less advantageous than anticipated - and even detrimental at high SNRs. Biases towards both the superficial and deep surfaces at very low SNRs and a notable bias towards the deep surface when combining empirical Bayesian beamformer (EBB) source reconstruction with a whole-brain analysis pose further challenges. Adequate SNR through appropriate trial numbers and shielding, as well as precise co-registration, is crucial for reliable laminar inference with OPMs.
An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. brain signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded redundant and synergistic information during auditory prediction error processing. In both tasks, we observed multiple patterns of synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between auditory and frontal regions. Using a brain-constrained neural network, we simulated the spatio-temporal patterns of synergy and redundancy observed in the experimental results and further demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback and feedforward connections. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.
An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. convey information beyond what can be performed by isolated signals. In other words, any two signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks, and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics exhibit redundancy and synergy for auditory prediction error signals. We observed multiple patterns of redundancy and synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between lower and higher areas in the frontal cortex. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.
An important question concerning inter-areal communication in the cortex, is whether these interactions are synergistic, i.e. convey information beyond what can be performed by isolated signals. Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in three common awake marmosets and investigated to what extent event-related-potentials (ERP) and broadband (BB) dynamics exhibit redundancy and synergy in auditory prediction error signals. We observed multiple patterns of redundancy and synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between lower and higher areas in the frontal cortex. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.
Decades of work have demonstrated that messenger RNAs (mRNAs) are localized and translated within neuronal dendrites and axons to provide proteins for remodeling and maintaining growth cones or synapses. It remains unknown, however, whether specific forms of plasticity differentially regulate the dynamics and translation of individual mRNA species. To address this, we targeted three individual synaptically localized mRNAs, CamkIIa, β-actin, Psd95, and used molecular beacons to track endogenous mRNA movements. We used reporters and CRISPR/Cas9 gene editing to track mRNA translation in cultured neurons. We found alterations in mRNA dynamic properties occurred during two forms of synaptic plasticity, long-term potentiation (cLTP) and depression (mGluR-LTD). Changes in mRNA dynamics following either form of plasticity resulted in an enrichment of mRNA in the vicinity of dendritic spines. Both the reporters and tagging of endogenous proteins revealed the transcript-specific stimulation of protein synthesis following cLTP or mGluR-LTD. As such, the plasticity-induced enrichment of mRNA near synapses could be uncoupled from its translational status. The enrichment of mRNA in the proximity of spines allows for localized signaling pathways to decode plasticity milieus and stimulate a specific translational profile, resulting in a customized remodeling of the synaptic proteome.
Decades of work have demonstrated that mRNAs are localized and translated within neuronal dendrites and axons to provide proteins for remodeling and maintaining growth cones or synapses. It remains unknown, however, whether specific forms of plasticity differentially regulate the dynamics and translation of individual mRNA species. To address these issues, we targeted three individual synaptically-localized mRNAs, CamkIIa, Beta actin, Psd95, and used molecular beacons to track endogenous mRNA movements and reporters and Crispr-Cas9 gene editing to track their translation. We found widespread alterations in mRNA behavior during two forms of synaptic plasticity, long-term potentiation (LTP) and depression (LTD). Changes in mRNA dynamics following plasticity resulted in an enrichment of mRNA in the vicinity of dendritic spines. Both the reporters and tagging of endogenous proteins revealed the transcript-specific stimulation of protein synthesis following LTP or LTD. The plasticity-induced enrichment of mRNA near synapses could be uncoupled from its translational status. The enrichment of mRNA in the proximity of spines allows for localized signaling pathways to decode plasticity milieus and stimulate a specific translational profile, resulting in a customized remodeling of the synaptic proteome.
Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. Here, we hypothesized that attentional and contextual top-down signals interact in V1, in a manner that primarily benefits the representation of natural visual stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we found that attention enhanced the decodability of stimulus identity from population responses evoked by natural scenes but, critically, not by synthetic stimuli in which higher-order statistical regularities were eliminated. Population analysis revealed that neuronal responses converged to a low dimensional subspace for natural but not for synthetic images. Critically, we determined that the attentional enhancement in stimulus decodability was captured by the dominant low dimensional subspace, suggesting an alignment between the attentional and natural stimulus variance. The alignment was pronounced for late evoked responses but not for early transient responses of V1 neurons, supporting the notion that top-down feedback was required. We argue that attention and perception share top-down pathways, which mediate hierarchical interactions optimized for natural vision.
Anticipating future events is a key computational task for neuronal networks. Experimental evidence suggests that reliable temporal sequences in neural activity play a functional role in the association and anticipation of events in time. However, how neurons can differentiate and anticipate multiple spike sequences remains largely unknown. We implement a learning rule based on predictive processing, where neurons exclusively fire for the initial, unpredictable inputs in a spiking sequence, leading to an efficient representation with reduced post-synaptic firing. Combining this mechanism with inhibitory feedback leads to sparse firing in the network, enabling neurons to selectively anticipate different sequences in the input. We demonstrate that intermediate levels of inhibition are optimal to decorrelate neuronal activity and to enable the prediction of future inputs. Notably, each sequence is independently encoded in the sparse, anticipatory firing of the network. Overall, our results demonstrate that the interplay of self-supervised predictive learning rules and inhibitory feedback enables fast and efficient classification of different input sequences.
Inter-areal coherence has been hypothesized as a mechanism for inter-areal communication. Indeed, empirical studies have observed an increase in inter-areal coherence with attention. Yet, the mechanisms underlying changes in coherence remain largely unknown. Both attention and stimulus salience are associated with shifts in the peak frequency of gamma oscillations in V1, which suggests that the frequency of oscillations may play a role in facilitating changes in inter-areal communication and coherence. In this study, we used computational modeling to investigate how the peak frequency of a sender influences inter-areal coherence. We show that changes in the magnitude of coherence are largely determined by the peak frequency of the sender. However, the pattern of coherence depends on the intrinsic properties of the receiver, specifically whether the receiver integrates or resonates with its synaptic inputs. Because resonant receivers are frequency-selective, resonance has been proposed as a mechanism for selective communication. However, the pattern of coherence changes produced by a resonant receiver is inconsistent with empirical studies. By contrast, an integrator receiver does produce the pattern of coherence with frequency shifts in the sender observed in empirical studies. These results indicate that coherence can be a misleading measure of inter-areal interactions. This led us to develop a new measure of inter-areal interactions, which we refer to as Explained Power. We show that Explained Power maps directly to the signal transmitted by the sender filtered by the receiver, and thus provides a method to quantify the true signals transmitted between the sender and receiver. Together, these findings provide a model of changes in inter-areal coherence and Granger-causality as a result of frequency shifts.
Sensory processing relies on interactions between excitatory and inhibitory neurons, which are often coordinated by 30-80Hz gamma oscillations. However, the specific contributions of distinct interneurons to gamma synchronization remain unclear. We performed high-density recordings from V1 in awake mice and used optogenetics to identify PV+ (Parvalbumin) and Sst+ (Somatostatin) interneurons. PV interneurons were highly phase-locked to visually-induced gamma oscillations. Sst cells were heterogeneous, with only a subset of narrow-waveform cells showing strong gamma phase-locking. Interestingly, PV interneurons consistently fired at an earlier phase in the gamma cycle (≈6ms or 60 degrees) than Sst interneurons. Consequently, PV and Sst activity showed differential temporal relations with excitatory cells. In particular, the 1st and 2nd spikes in burst events, which were strongly gamma phase-locked, shortly preceded PV and Sst activity, respectively. These findings indicate a primary role of PV interneurons in synchronizing excitatory cells and suggest that PV and Sst interneurons control the excitability of somatic and dendritic neural compartments with precise time delays coordinated by gamma oscillations.
When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here we show in awake macaque area V1 that both, repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects showed some persistence on the timescale of minutes. Further, gamma increases were specific to the presented stimulus location. Importantly, repetition effects on gamma and on firing rates generalized to natural images. These findings suggest that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters, both for generating efficient stimulus responses and possibly for memory formation.
When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here, we show in awake macaque area V1 that both repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects show some persistence on the timescale of minutes. Gamma increases are specific to the presented stimulus location. Further, repetition effects on gamma and on firing rates generalize to images of natural objects. These findings support the notion that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters.
Evading imminent predator threat is critical for survival. Effective defensive strategies can vary, even between closely related species. However, the neural basis of such species-specific behaviours is still poorly understood. Here we find that two sister species of deer mice (genus Peromyscus) show different responses to the same looming stimulus: P. maniculatus, which occupy densely vegetated habitats, predominantly dart to escape, while the open field specialist, P. polionotus, pause their movement. This difference arises from species-specific escape thresholds, is largely context-independent, and can be triggered by both visual and auditory threat stimuli. Using immunohistochemistry and electrophysiological recordings, we find that although visual threat activates the superior colliculus in both species, the role of the dorsal periaqueductal gray (dPAG) in driving behaviour differs. While dPAG activity scales with running speed and involves both excitatory and inhibitory neurons in P. maniculatus, the dPAG is largely silent in P. polionotus, even when darting is triggered. Moreover, optogenetic activation of excitatory dPAG neurons reliably elicits darting behaviour in P. maniculatus but not P. polionotus. Together, we trace the evolution of species-specific escape thresholds to a central circuit node, downstream of peripheral sensory neurons, localizing an ecologically relevant behavioural difference to a specific region of the complex mammalian brain.
Individual differences in perception are widespread. Considering inter-individual variability, synesthetes experience stable additional sensations; schizophrenia patients suffer perceptual deficits in e.g. perceptual organization (alongside hallucinations and delusions). Is there a unifying principle explaining inter-individual variability in perception? There is good reason to believe perceptual experience results from inferential processes whereby sensory evidence is weighted by prior knowledge about the world. Different perceptual phenotypes may result from different precision weighting of sensory evidence and prior knowledge. We tested this hypothesis by comparing visibility thresholds in a perceptual hysteresis task across medicated schizophrenia patients, synesthetes, and controls. Participants rated the subjective visibility of stimuli embedded in noise while we parametrically manipulated the availability of sensory evidence. Additionally, precise long-term priors in synesthetes were leveraged by presenting either synesthesia-inducing or neutral stimuli. Schizophrenia patients showed increased visibility thresholds, consistent with overreliance on sensory evidence. In contrast, synesthetes exhibited lowered thresholds exclusively for synesthesia-inducing stimuli suggesting high-precision long-term priors. Additionally, in both synesthetes and schizophrenia patients explicit, short-term priors – introduced during the hysteresis experiment – lowered thresholds but did not normalize perception. Our results imply that distinct perceptual phenotypes might result from differences in the precision afforded to prior beliefs and sensory evidence, respectively.
Ribosomes translate the genetic code into proteins. Recent technical advances have facilitated in situ structural analyses of ribosome functional states inside eukaryotic cells and the minimal bacterium Mycoplasma. However, such analyses of Gram-negative bacteria are lacking, despite their ribosomes being major antimicrobial drug targets. Here we compare two E. coli strains, a lab E. coli K-12 and human gut isolate E. coli ED1a, for which tetracycline exhibits bacteriostatic and bactericidal action, respectively. The in situ ribosome structures upon tetracycline treatment show a virtually identical drug binding-site in both strains, yet the distribution of ribosomal complexes clearly differs. While K-12 retains ribosomes in a translation competent state, tRNAs are lost in the vast majority of ED1a ribosomes. A differential response is also reflected in proteome-wide abundance and thermal stability assessment. Our study underlines the need to include molecular analyses and to consider gut bacteria when addressing antibiotic mode of action.
Taraxerol und 3α, 7α, 22α-Trihydroxy-stigmasten-(5) in den Blättern der Haselnuß (Corylus avellana)
(1966)
Aus den Blättern der Haselnuß (Corylus avellana) konnte Taraxerol, β-Sitosterin und 3α,7α,22α-Trihydroxy-stigmasten- (5) isoliert werden. Letzteres war bisher lediglich in den Blättern der Roßkastanie (Aesculus hyppocastanum) nachgewiesen worden. Triterpene mit dem Dammaranskelett waren in den Haselnußblättern nicht auffindbar.
An der Umwandlung von Tritium-markiertem Tropin- (3β-T) zu Pseudotropin- (3α-T) in Hirnhomogenat, unter der synergistischen Wirkung eines Sporenbildners und eines Enterococcen-Stammes, konnte bewiesen werden, daß diese trans-cis-Umlagerung durch Abspaltung und Wiederanlagerung von Wasser erfolgt. Die Abspaltung von Wasser aus 3α-Tropanol zu Tropen- (2) ist reversibel, wie aus dem Einbau von Tritiumwasser in das Tropin hervorgeht.
Unter dem synergistischen Einfluß zweier Bakterien-Stämme, eines aeroben Sporenbildners (Bac. alvei) und eines Enterococcen-Stammes (Diplococcus I) wird Tropin vollständig in Pseudotropin umgewandelt. Der Mechanismus dieser trans-cis-Umlagerung wird diskutiert.
Zur Trennung von Tropan-Alkaloiden und deren Derivate werden geeignete chromatographische Laufmittelsysteme angegeben.
The traditional view on coding in the cortex is that populations of neurons primarily convey stimulus information through the spike count. However, given the speed of sensory processing, it has been hypothesized that sensory encoding may rely on the spike-timing relationships among neurons. Here, we use a recently developed method based on Optimal Transport Theory called SpikeShip to study the encoding of natural movies by high-dimensional ensembles of neurons in visual cortex. SpikeShip is a generic measure of dissimilarity between spike train patterns based on the relative spike-timing relations among all neurons and with computational complexity similar to the spike count. We compared spike-count and spike-timing codes in up to N > 8000 neurons from six visual areas during natural video presentations. Using SpikeShip, we show that temporal spiking sequences convey substantially more information about natural movies than population spike-count vectors when the neural population size is larger than about 200 neurons. Remarkably, encoding through temporal sequences did not show representational drift both within and between blocks. By contrast, population firing rates showed better coding performance when there were few active neurons. Furthermore, the population firing rate showed memory across frames and formed a continuous trajectory across time. In contrast to temporal spiking sequences, population firing rates exhibited substantial drift across repetitions and between blocks. These findings suggest that spike counts and temporal sequences constitute two different coding schemes with distinct information about natural movies.
Human language relies on hierarchically structured syntax to facilitate efficient and robust communication. The correct processing of syntactic information is essential for successful communication between speakers. As an abstract level of language, syntax has often been studied separately from the physical form of the speech signal, thus often masking the interactions that can promote better syntactic processing in the human brain. We analyzed a MEG dataset to investigate how acoustic cues, specifically prosody, interact with syntactic operations. We examined whether prosody enhances the cortical encoding of syntactic representations. We decoded left-sided dependencies directly from brain activity and evaluated possible modulations of the decoding by the presence of prosodic boundaries. Our findings demonstrate that prosodic boundary presence improves the representation of left-sided dependencies, indicating the facilitative role of prosodic cues in processing abstract linguistic features. This study gives neurobiological evidence for the boosting of syntactic processing via interaction with prosody.
Neural computations emerge from recurrent neural circuits that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require tractable spiking network models that can consistently incorporate new information about the network structure and reproduce the recorded neural activity features. However, it is challenging to predict which spiking network connectivity configurations and neural properties can generate fundamental operational states and specific experimentally reported nonlinear cortical computations. Theoretical descriptions for the computational state of cortical spiking circuits are diverse, including the balanced state where excitatory and inhibitory inputs balance almost perfectly or the inhibition stabilized state (ISN) where the excitatory part of the circuit is unstable. It remains an open question whether these states can co-exist with experimentally reported nonlinear computations and whether they can be recovered in biologically realistic implementations of spiking networks. Here, we show how to identify spiking network connectivity patterns underlying diverse nonlinear computations such as XOR, bistability, inhibitory stabilization, supersaturation, and persistent activity. We established a mapping between the stabilized supralinear network (SSN) and spiking activity which allowed us to pinpoint the location in parameter space where these activity regimes occur. Notably, we found that biologically-sized spiking networks can have irregular asynchronous activity that does not require strong excitation-inhibition balance or large feedforward input and we showed that the dynamic firing rate trajectories in spiking networks can be precisely targeted without error-driven training algorithms.
The firing pattern of ventral midbrain dopamine neurons is controlled by afferent and intrinsic activity to generate prediction error signals that are essential for reward-based learning. Given the absence of intracellular in vivo recordings in the last three decades, the subthreshold membrane potential events that cause changes in dopamine neuron firing patterns remain unknown. By establishing stable in vivo whole-cell recordings of >100 spontaneously active midbrain dopamine neurons in anaesthetized mice, we identified the repertoire of subthreshold membrane potential signatures associated with distinct in vivo firing patterns. We demonstrate that dopamine neuron in vivo activity deviates from a single spike pacemaker pattern by eliciting transient increases in firing rate generated by at least two diametrically opposing biophysical mechanisms: a transient depolarization resulting in high frequency plateau bursts associated with a reactive, depolarizing shift in action potential threshold; and a prolonged hyperpolarization preceding slower rebound bursts characterized by a predictive, hyperpolarizing shift in action potential threshold. Our findings therefore illustrate a framework for the biophysical implementation of prediction error and sensory cue coding in dopamine neurons by tuning action potential threshold dynamics.
Several studies have probed perceptual performance at different times after a self-paced motor action and found frequency-specific modulations of perceptual performance phase-locked to the action. Such action-related modulation has been reported for various frequencies and modulation strengths. In an attempt to establish a basic effect at the population level, we had a relatively large number of participants (n=50) perform a self-paced button press followed by a detection task at threshold, and we applied both fixed- and random-effects tests. The combined data of all trials and participants surprisingly did not show any significant action-related modulation. However, based on previous studies, we explored the possibility that such modulation depends on the participant’s internal state. Indeed, when we split trials based on performance in neighboring trials, then trials in periods of low performance showed an action-related modulation at ≈17 Hz. When we split trials based on the performance in the preceding trial, we found that trials following a “miss” showed an action-related modulation at ≈17 Hz. Finally, when we split participants based on their false-alarm rate, we found that participants with no false alarms showed an action-related modulation at ≈17 Hz. All these effects were significant in random-effects tests, supporting an inference on the population. Together, these findings indicate that action-related modulations are not always detectable. However, the results suggest that specific internal states such as lower attentional engagement and/or higher decision criterion are characterized by a modulation in the beta-frequency range.
Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the discrete Fourier transform (DFT) and the least square spectrum (LSS). DFT and LSS can be applied both on the average accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effects) or on the population (random-effects) of simulated participants. Multiple comparisons across frequencies were corrected using False Discovery Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated sensitivity, specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the average accuracy time course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effects approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.
Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the Discrete Fourier Transform (DFT) and the Least Square Spectrum (LSS). DFT and LSS can be applied both on the averaged accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effect) or on the population (random-effect) of simulated participants. Multiple comparisons across frequencies were corrected using False-Discovery-Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated Sensitivity, Specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the averaged-accuracy-time-course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved Sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest Specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effect approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
Entorhinal-retrosplenial circuits for allocentric-egocentric transformation of boundary coding
(2020)
Spatial navigation requires landmark coding from two perspectives, relying on viewpoint-invariant and self-referenced representations. The brain encodes information within each reference frame but their interactions and functional dependency remains unclear. Here we investigate the relationship between neurons in the rat's retrosplenial cortex (RSC) and entorhinal cortex (MEC) that increase firing near boundaries of space. Border cells in RSC specifically encode walls, but not objects, and are sensitive to the animal’s direction to nearby borders. These egocentric representations are generated independent of visual or whisker sensation but are affected by inputs from MEC that contains allocentric spatial cells. Pharmaco- and optogenetic inhibition of MEC led to a disruption of border coding in RSC, but not vice versa, indicating allocentric-to-egocentric transformation. Finally, RSC border cells fire prospective to the animal’s next motion, unlike those in MEC, revealing the MEC-RSC pathway as an extended border coding circuit that implements coordinate transformation to guide navigation behavior.
Borders and edges are salient and behaviourally relevant features for navigating the environment. The brain forms dedicated neural representations of environmental boundaries, which are assumed to serve as a reference for spatial coding. Here we expand this border coding network to include the retrosplenial cortex (RSC) in which we identified neurons that increase their firing near all boundaries of an arena. RSC border cells specifically encode walls, but not objects, and maintain their tuning in the absence of direct sensory detection. Unlike border cells in the medial entorhinal cortex (MEC), RSC border cells are sensitive to the animal’s direction to nearby walls located contralateral to the recorded hemisphere. Pharmacogenetic inactivation of MEC led to a disruption of RSC border coding, but not vice versa, indicating network directionality. Together these data shed light on how information about distance and direction of boundaries is generated in the brain for guiding navigation behaviour.
Brookshire (2022) claims that previous analyses of periodicity in detection performance after a reset event suffer from extreme false-positive rates. Here we show that this conclusion is based on an incorrect implemention of a null-hypothesis of aperiodicity, and that a correct implementation confirms low false-positive rates. Furthermore, we clarify that the previously used method of shuffling-in-time, and thereby shuffling-in-phase, cleanly implements the null hypothesis of no temporal structure after the reset, and thereby of no phase locking to the reset. Moving from a corresponding phase-locking spectrum to an inference on the periodicity of the underlying process can be accomplished by parameterizing the spectrum. This can separate periodic from non-periodic components, and quantify the strength of periodicity.