We need to keep track of our heading direction, and head direction cells in various brain regions represent exactly that; therefore, they have been identified as key substrates of direction coding. But how is such a variable computed? Head direction representation is thought to arise from the integration of angular velocity, suggesting the existence of angular head velocity (AHV) neurons. Theoretically, vestibular, visual, proprioceptive and motor command signals can all contribute to angular velocity computations, while the actual coding mechanisms have remained unclear.Keshavarzi et al. addressed this by separately controlling vestibular and visual cues in a head-fixed configuration (Keshavarzi et al., 2022b). They targeted the retrosplenial cortex (RSC), a multisensory associational area known to be important for the integration of egocentric and allocentric information during navigation (Solari & Hangya, 2018). The use of high channel-count multielectrode arrays (silicon probes) allowed the authors to track a large number of neurons through different conditions, including free exploration of an open field arena. They identified subsets of neurons representing head direction, AHV, locomotion speed or a combination of these variables.

A large fraction of AHV neurons showed similar tuning properties during free exploration and passive rotation in the dark, demonstrating the importance of vestibular input. In agreement, lesioning the semi-circular canals largely reduced these responses. Next, mice were presented with visual motion stimuli while remaining stationary. This showed that visual signals also contributed to AHV coding and specifically suggested that they increased the gain and improved the signal-to-noise ratio of AHV representations. Mice trained on discriminating rotational stimuli showed improved performance when both visual and vestibular information was available compared to either vestibular-only or visual-only stimuli, further demonstrating the integration of the two types of sensory cues. Finally, the availability of both vestibular and visual information improved decoding accuracy of angular speed from ensembles of retrosplenial cortex neurons compared to single modality stimuli, at least at the beginning of motion.

Keshavarzi et al. provide compelling evidence for the critical role of vestibular input in encoding AHV within the RSC. While the widespread presence of vestibular signals in rodent cortical circuits is well-documented (Rancz et al., 2015), this study significantly advances our understanding by demonstrating that RSC neurons can also encode AHV. These findings align with research that identified AHV representations in the RSC and adjacent cortical regions, including primary visual, secondary visual, posterior parietal, primary motor, secondary motor and primary somatosensory cortices (Hennestad et al., 2021), and with later work that showed AHV in parahippocampal circuits (Spalla et al., 2022).

Notably, the relative contributions of vestibular and visual signals to AHV encoding likely depend on the specific cortical area and the AHV amplitude. Visual flow may be more prominent at lower AHV ranges (0-90 deg/s), while vestibular input likely dominates AHV representation at higher speeds (Stahl, 2004; Hennestad et al., 2021). This suggests a complementary contribution of vestibular and visual information, enabling encoding a broader range of angular velocity and driving a widespread AHV signal across cortical areas.

This is an elegant study in which a creative and clear experimental design helps teasing apart different contributors of a specific computation that normally appear linked during natural behaviors. This way it also demonstrates the power of precise experimental control, while immediately extrapolating to natural behavior by examining the same neurons during free exploration. It additionally demonstrates a non-trivial multisensory integration in the retrosplenial cortex that can directly contribute to egocentric spatial representations during navigation (Alexander & Nitz, 2015, 2017).

Editorial note: A preprint version of this article was peer-reviewed by PCI Neuroscience. The refereed preprint can be found through the cited link here (Keshavarzi et al., 2022a), and the peer-review process here.

References

Alexander AS, Nitz DA (2015) Retrosplenial cortex maps the conjunction of internal and external spaces. Nature Neuroscience, 18, 1143–1151. https://doi.org/10.1038/nn.4058

Alexander AS, Nitz DA (2017) Spatially Periodic Activation Patterns of Retrosplenial Cortex Encode Route Sub-spaces and Distance Traveled. Current Biology, 27, 1551-1560.e4. https://doi.org/10.1016/j.cub.2017.04.036

Hennestad E, Witoelar A, Chambers AR, Vervaeke K (2021) Mapping vestibular and visual contributions to angular head velocity tuning in the cortex. Cell Reports, 37, 110134. https://doi.org/10.1016/j.celrep.2021.110134

Keshavarzi S, Bracey EF, Faville RA, Campagner D, Tyson AL, Lenzi SC, Branco T, Margrie TW (2022a) The retrosplenial cortex combines internal and external cues to encode head velocity during navigation. https://doi.org/10.5281/zenodo.5834392

Keshavarzi S, Bracey EF, Faville RA, Campagner D, Tyson AL, Lenzi SC, Branco T, Margrie TW (2022b) Multisensory coding of angular head velocity in the retrosplenial cortex. Neuron, 110, 532-543.e9. https://doi.org/10.1016/j.neuron.2021.10.031

Rancz EA, Moya J, Drawitsch F, Brichta AM, Canals S, Margrie TW (2015) Widespread vestibular activation of the rodent cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 35, 5926–5934. https://doi.org/10.1523/JNEUROSCI.1869-14.2015

Solari N, Hangya B (2018) Cholinergic modulation of spatial learning, memory and navigation. European Journal of Neuroscience, 48, 2199–2230. https://doi.org/10.1111/ejn.14089

Spalla D, Treves A, Boccara CN (2022) Angular and linear speed cells in the parahippocampal circuits. Nature Communications, 13, 1907. https://doi.org/10.1038/s41467-022-29583-z

Stahl JS (2004) Using eye movements to assess brain function in mice. Vision Research, 44, 3401–3410. https://doi.org/10.1016/j.visres.2004.09.011

The study conducted by Carneiro and colleagues (Carneiro et al., 2023) seeks to explore the specific circumstances that influence the impact of protein synthesis inhibitors on the formation and endurance of fear-related memories. This investigation takes the form of a comprehensive meta-analysis of existing literature, making two significant contributions. Firstly, it enhances our understanding of fear conditioning by corroborating established interpretations (Schroyens et al., 2019, 2021) while also introducing novel insights. Secondly, it contributes to the ongoing discourse within behavioral neuroscience regarding the practicality and challenges of applying systematic reviews and meta-analyses to pre-clinical research (Prinz et al., 2011; Errington et al., 2021).

To delve deeper into the subject, the authors conducted distinct meta-analyses for different injection sites and target sessions, thus examining the intervention's effects under varying conditions. Their findings highlight the robust influence of protein synthesis inhibitors on memory consolidation and reconsolidation, but suggest a lack of significant impact on extinction, potentially attributed to the limited number of studies on this topic. Notably, their analysis pinpoints certain well-recognized influencing factors, such as intervention timing and re-exposure duration. However, other proposed boundary conditions, such as memory age and training strength, do not appear to significantly influence the effect size, possibly due to a limited number of studies. This leads to the conclusion that while meta-analyses are valuable for consolidating existing knowledge, substantiation through well-powered, confirmatory experiments is imperative.

Moreover, the research underlines the substantial heterogeneity among individual experiments, particularly within studies, which poses challenges for meta-analysis. Aggregating studies using various methodologies increases the capacity to identify influencing factors, emphasizing the importance of these approaches. The study also addresses the limitations of existing meta-analysis methods and suggests that additional sources of variability and difficulties in replication may exist, extending beyond the usual boundary conditions. These challenges could be attributed to biases within the literature, random error, or variations in experimental protocols.

The paper highlights the significance of rigorous and reproducible research practices in pre-clinical investigations, emphasizing the need for an iterative process that combines data synthesis with empirical testing. While meta-analyses serve as valuable tools for knowledge consolidation, the authors stress that they cannot replace high-powered, confirmatory replication studies. Consequently, they advocate for a more holistic and interconnected approach to experimental science, incorporating data synthesis with empirical validation to enhance the reliability of research findings.

References

Carneiro CFD, Amorim FE, Amaral OB (2023) A meta-analysis of the effect of protein synthesis inhibitors on rodent fear conditioning. , 2022.10.11.509645. https://doi.org/10.1101/2022.10.11.509645

Errington TM, Mathur M, Soderberg CK, Denis A, Perfito N, Iorns E, Nosek BA (2021) Investigating the replicability of preclinical cancer biology. eLife, 10, e71601. https://doi.org/10.7554/eLife.71601

Prinz F, Schlange T, Asadullah K (2011) Believe it or not: how much can we rely on published data on potential drug targets? Nature Reviews. Drug Discovery, 10, 712. https://doi.org/10.1038/nrd3439-c1

Schroyens N, Alfei JM, Schnell AE, Luyten L, Beckers T (2019) Limited replicability of drug-induced amnesia after contextual fear memory retrieval in rats. Neurobiology of Learning and Memory, 166, 107105. https://doi.org/10.1016/j.nlm.2019.107105

Schroyens N, Sigwald EL, Van Den Noortgate W, Beckers T, Luyten L (2021) Reactivation-Dependent Amnesia for Contextual Fear Memories: Evidence for Publication Bias. eNeuro, 8, ENEURO.0108-20.2020. https://doi.org/10.1523/ENEURO.0108-20.2020

Up to now, the automatic approach-avoidance tendency towards physical activity and sedentary stimuli has been studied in various categories of the population. Previous studies showed faster reaction times (RTs) when approaching physical activity stimuli and avoiding sedentary stimuli, especially in healthy young individuals (Cheval et al., 2014; Locke & Berry, 2021). However, the whole spectrum of adulthood has never been tested within the same experiment.

A first strength of the study of Farajzadeh et al. (2023) is that they constructed an online paradigm and analyzed the results of 130 participants aged between 21 and 77 years. It should be noted that this study has been pre-registered (https://doi.org/10.17605/OSF.IO/7GXZR). Overall the authors performed the experiment as first described. They updated their a priori power analysis, including the highest number of predictors (six tested predictors including two interaction effects and a total of eleven predictors). This increased the planned number of participants recruited from 85 to 144. Also, in addition to planned hypotheses, exploratory analyses were conducted to test whether automatic approach-avoidance tendencies toward physical activity and sedentary behaviors were associated with explicit attitudes and the intention to be physically active across aging.

The authors recorded RTs and errors when participants had to approach/move an avatar towards/away from physical activity and sedentary stimuli.

Another strength lies in data analysis and statistical design. To avoid any misinterpretation of the results, which could arise from an increase in RTs relative to age, the authors had the foresight to measure RTs when approaching/avoiding neutral stimuli (ellipses and rectangles).

Taking into account such individuals differences, Farajzadeh et al. confirmed a main tendency to approach physical activity stimuli and to avoid sedentary stimuli throughout the lifespan. 

When the participants considered physical activity as the most pleasant and enjoyable (explicit affective attitude toward physical activity), the RTs were shorter when approaching physical activity and avoiding sedentary stimuli, irrespective of age. However, the intention to be physically active did not influence the individual's RTs. 

Altogether, the study by Farajzadeh et al. suggests that age and explicit attitudes modulate the time to respond to physical activity and sedentary stimuli.

References

Cheval, B., Sarrazin, P., and Pelletier, L. (2014). Impulsive approach tendencies toward physical activity and sedentary behaviors, but not reflective intentions, prospectively predict non-exercise activity thermogenesis. PLoS One, 9(12), e115238. https://doi.org/10.1371/journal.pone.0115238

Farajzadeh, A., Goubran, M., Beehler, A., Cherkawi, N., Morrison, P., de Chanaleilles, M., Maltagliati, S., Cheval, B., Miller, M.W., Sheehy, L., Bilodeau, M., Orsholits, D., and Boisgontier, M.P. (2023) Automatic approach-avoidance tendency toward physical activity, sedentary, and neutral stimuli as a function of age, explicit affective attitude, and intention to be active. MedRxiv. https://doi.org/10.1101/2022.09.05.22279509

Locke, S. R., and Berry, T. R. (2021). Examining the relationship between exercise-related cognitive errors, exercise schema, and implicit associations. Journal of Sport and Exercise Psychology, 43(4), 345–352. https://doi.org/10.1123/jsep.2021-0031 

Artificial (spiking) neural networks (ANNs) have become an important tool in the modelling of biological neuronal circuits. However, they come with caveats: their typical training can be laborious, and after it is done, the complexity of the connectivity obtained can be almost as daunting as the original biological systems we are trying to model.

In this work [1], Nardin and colleagues summarize and expand upon the Spike Coding Network (SCN) framework [2], which originally provides a direct method to derive the connectivity of a spiking ANN representing any given linear system. They generalize this framework to approximate any (non-linear) dynamical system, by yielding the connectivity necessary to represent its polynomial expansion. This is achieved by including multiplicative synapses in their network connections. They show that higher polynomial orders can be efficiently represented with hierarchical network structures. The resulting networks not only enjoy many of the desirable features of traditional ANNs, like robustness to (artificial) cell death and realistic patterns of activity, but also a much more interpretable connectivity. This is promptly leveraged to derive how densely connected a neural network of this type needs to be to be able to represent dynamical systems of different complexities.

The derivations in this work are self-contained and the mathematically inclined neuroscientist can quickly get up to speed with the new multiplicative SCN framework, without the need for prior specific knowledge of SCNs. All the code is available and well commented in https://github.com/michnard/mult_synapses making this introduction even more accessible to its readers. This paper is relevant for those interested in neural representations of dynamical systems and the possible roles for multiplicative synapses and dendritic non-linearities. Those interested in neuromorphic computations will find here an efficient and direct way of representing non-linear dynamical systems (at least those well approximated by low-order polynomials). Finally, those interested in neural temporal pattern generators might find it surprising that only 10 integrate and fire neurons can already very reasonably approximate a chaotic Lorenz system.

[1] Nardin, M., Phillips, J. W., Podlaski, W. F., and Keemink, S. W. (2021) Nonlinear computations in spiking neural networks through multiplicative synapses. arXiv, ver. 4 peer-reviewed and recommended by Peer Community in Neuroscience. https://arxiv.org/abs/2009.03857v4

[2] Boerlin, M., Machens, C. K., and Denève, S. (2013). Predictive coding of dynamical variables in
balanced spiking networks. PLoS Comput Biol, 9(11):e1003258. https://doi.org/10.1371/journal.pcbi.1003258

Entropy and mutual information are useful metrics for quantitative analyses of various signals across the sciences including neuroscience (Verdú, 2019). The information that a neuron transfers about a sensory stimulus is just one of many examples of this. However, estimating the entropy of neural data is often difficult due to limited sampling (Tovée et al., 1993; Treves and Panzeri, 1995). This manuscript overcomes this problem with a 'quick and dirty' trick: just save the corresponding plots as PNG files and measure the file sizes! The idea is that the size of the PNG file obtained by saving a particular set of data will reflect the amount of variability present in the data and will therefore provide an indirect estimation of the entropy content of the data. 

The method the study employs is based on Shannon’s Source Coding Theorem - an approach used in the field of compressed sensing - which is still not widely used in neuroscience. The resulting algorithm is very straightforward, essentially consisting of just saving a figure of your data as a PNG file. Therefore it provides a useful tool for a fast and computationally efficient evaluation of the information content of a signal, without having to resort to more math-heavy methods (as the computation is done “for free” by the PNG compression software). It also opens up the possibility to pursue a similar strategy with other (than PNG) image compression software. The main limitation is that the PNG conversion method presented here allows only a relative entropy estimation: the size of the file is not the absolute value of entropy, due to the fact that the PNG algorithm also involves filtering for 2D images. 

The study comprehensively reviews the use of entropy estimation in circuit neuroscience, and then tests the PNG method against other math-heavy methods, which have also been made accessible elsewhere (Ince et al., 2010). The study demonstrates use of the method in several applications.  First, the mutual information between stimulus and neural response in whole-cell and unit recordings is estimated. Second, the study applies the method to experimental situations with less experimental control - such as recordings of hippocampal place cells (O’Keefe & Dostrovsky,1971) as animals freely explore an environment. The study shows the method can replicate previously established metrics in the field (e.g. Skaggs information, Skaggs et al. 1993). Importantly, it does this while making fewer assumptions on the data than traditional methods. Third, he study extends the use of the method to imaging data of neuronal morphology, such as charting the growth stage of neuronal cultures. However, the radial entropy of a dendritic tree seems at first more difficult to interpret than the common Sholl analysis of radial crossings of dendrite segments (Figure 6Ac of Zbili and Rama, 2021). As the authors note, a similar technique is used in paleobiology to discriminate pictures of biogenic rocks from abiogenic ones (Wagstaff and Corsetti, 2010). Perhaps neuronal subtypes could also be easily distinguished through PNG file size (Yuste et al., 2020). These examples are generally promising and creative applications.The authors used open source software and openly shared their code so anyone can give it a spin (https://github.com/Sylvain-Deposit/PNG-Entropy).

We were inspired by the wide applicability of the presented back-of-the-envelope technique, so we used it in a situation that the study had not tested: namely,  the dissection of microcircuits via optogenetic tagging of target neurons. In this process, one is often confronted with the problem that not only the opsin-carrying cells will spike in response to light, but also other nearby neurons which are activated synaptically (via the opto-tagged cell). Separating these two types of responses is typically done using a latency or jitter analysis, which requires the experimenter subjectively searching for detection parameters. Therefore a rapid and objective technique is preferable. The PNG rate difference method on slice whole cell recordings of opsin tagged neurons revealed higher mutual information metrics for direct optogenetic activation than for postsynaptic responses, showing the method can be easily used to objectively segregate different spike triggers. 

Figure caption: Using a PNG entropy metric to distinguish between direct optogenetic responses and postsynaptic excitatory responses. Left, PNG rate difference calculated for whole cell recordings of optogenetic activation in brain slices. About 20 consecutive 60ms sweeps were analysed from each of 7 postsynaptic cells and 8 directly activated cells. Analysis was performed as in Fig4B of the preprint (https://doi.org/10.1101/2020.08.04.236174) using code from https://github.com/Sylvain-Deposit/PNG-Entropy/blob/master/BatchSaveAsPNG.py. Right, six example traces from a cell carrying channelrhodopsin (black, top) and a cell that was excited synaptically (gray, bottom).

References

Ince, R.A.A., Mazzoni, A., Petersen, R.S., and Panzeri, S. (2010). Open source tools for the information theoretic analysis of neural data. Front Neurosci 4. https://doi.org/10.3389/neuro.01.011.2010
 
O'Keefe, J., & Dostrovsky, J. (1971). The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Research, 34(1), 171-175. https://doi.org/10.1016/0006-8993(71)90358-1
 
Skaggs, M. E., McNaughton, B. L., Gothard, K. M., and Markus, E. J. (1993). An information-theoretic approach to deciphering the hippocampal code. Adv. Neural Inform. Process Syst. 5, 1030-1037.
 
Tovée, M.J., Rolls, E.T., Treves, A., and Bellis, R.P. (1993). Information encoding and the responses of single neurons in the primate temporal visual cortex. J Neurophysiol 70, 640-654. https://doi.org/10.1152/jn.1993.70.2.640
 
Treves, A., and Panzeri, S. (1995). The Upward Bias in Measures of Information Derived from Limited Data Samples. Neural Computation 7, 399-407. https://doi.org/10.1162/neco.1995.7.2.399
 
Verdú, S. (2019). Empirical Estimation of Information Measures: A Literature Guide. Entropy (Basel) 21. https://doi.org/10.3390/e21080720
 
Wagstaff, K.L., and Corsetti, F.A. (2010). An evaluation of information-theoretic methods for detecting structural microbial biosignatures. Astrobiology 10, 363-379. https://doi.org/10.1089/ast.2008.0301
 
Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Armañanzas, R., Ascoli, G.A., Bielza, C., Bokharaie, V., Bergmann, T.B., et al. (2020). A community-based transcriptomics classification and nomenclature of neocortical cell types. Nat Neurosci 23, 1456-1468. https://doi.org/10.1038/s41593-020-0685-8
 
Zbili, M., and Rama, S. (2021). A quick and easy way to estimate entropy and mutual information for neuroscience. BioRxiv 2020.08.04.236174. https://doi.org/10.1101/2020.08.04.236174