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1. Robust point-process Granger causality analysis in presence of exogenous temporal modulations and trial-by-trial variability in spike trains

   https://doi.org/10.1371/journal.pcbi.1007675  

   Casile, Antonino ; Faghih, Rose T. ; Brown, Emery N. ( January 2021 , PLOS Computational Biology)

   Morrison, Abigail (Ed.)

   Assessing directional influences between neurons is instrumental to understand how brain circuits process information. To this end, Granger causality, a technique originally developed for time-continuous signals, has been extended to discrete spike trains. A fundamental assumption of this technique is that the temporal evolution of neuronal responses must be due only to endogenous interactions between recorded units, including self-interactions. This assumption is however rarely met in neurophysiological studies, where the response of each neuron is modulated by other exogenous causes such as, for example, other unobserved units or slow adaptation processes. Here, we propose a novel point-process Granger causality technique that is robust with respect to the two most common exogenous modulations observed in real neuronal responses: within-trial temporal variations in spiking rate and between-trial variability in their magnitudes. This novel method works by explicitly including both types of modulations into the generalized linear model of the neuronal conditional intensity function (CIF). We then assess the causal influence of neuron i onto neuron j by measuring the relative reduction of neuron j ’s point process likelihood obtained considering or removing neuron i . CIF’s hyper-parameters are set on a per-neuron basis by minimizing Akaike’s information criterion. In synthetic data sets, generated by means of random processes or networks of integrate-and-fire units, the proposed method recovered with high accuracy, sensitivity and robustness the underlying ground-truth connectivity pattern. Application of presently available point-process Granger causality techniques produced instead a significant number of false positive connections. In real spiking responses recorded from neurons in the monkey pre-motor cortex (area F5), our method revealed many causal relationships between neurons as well as the temporal structure of their interactions. Given its robustness our method can be effectively applied to real neuronal data. Furthermore, its explicit estimate of the effects of unobserved causes on the recorded neuronal firing patterns can help decomposing their temporal variations into endogenous and exogenous components. 

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2. Neurophysiological coding of space and time in the hippocampus, entorhinal cortex, and retrosplenial cortex

   https://doi.org/10.1177/2398212820972871  

   Alexander, Andrew S. ; Robinson, Jennifer C. ; Dannenberg, Holger ; Kinsky, Nathaniel R. ; Levy, Samuel J. ; Mau, William ; Chapman, G. William ; Sullivan, David W. ; Hasselmo, Michael E. ( January 2020 , Brain and Neuroscience Advances)

   null (Ed.)

   Neurophysiological recordings in behaving rodents demonstrate neuronal response properties that may code space and time for episodic memory and goal-directed behaviour. Here, we review recordings from hippocampus, entorhinal cortex, and retrosplenial cortex to address the problem of how neurons encode multiple overlapping spatiotemporal trajectories and disambiguate these for accurate memory-guided behaviour. The solution could involve neurons in the entorhinal cortex and hippocampus that show mixed selectivity, coding both time and location. Some grid cells and place cells that code space also respond selectively as time cells, allowing differentiation of time intervals when a rat runs in the same location during a delay period. Cells in these regions also develop new representations that differentially code the context of prior or future behaviour allowing disambiguation of overlapping trajectories. Spiking activity is also modulated by running speed and head direction, supporting the coding of episodic memory not as a series of snapshots but as a trajectory that can also be distinguished on the basis of speed and direction. Recent data also address the mechanisms by which sensory input could distinguish different spatial locations. Changes in firing rate reflect running speed on long but not short time intervals, and few cells code movement direction, arguing against path integration for coding location. Instead, new evidence for neural coding of environmental boundaries in egocentric coordinates fits with a modelling framework in which egocentric coding of barriers combined with head direction generates distinct allocentric coding of location. The egocentric input can be used both for coding the location of spatiotemporal trajectories and for retrieving specific viewpoints of the environment. Overall, these different patterns of neural activity can be used for encoding and disambiguation of prior episodic spatiotemporal trajectories or for planning of future goal-directed spatiotemporal trajectories. 

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3. Dynamic attention signalling in V4: Relation to fast‐spiking/non‐fast‐spiking cell class and population coupling

   https://doi.org/10.1111/ejn.15928  

   Sachse, Elizabeth M. ; Snyder, Adam C. ( March 2023 , European Journal of Neuroscience)

   The computational role of a neuron during attention depends on its firing properties, neurotransmitter expression and functional connectivity. Neurons in the visual cortical area V4 are reliably engaged by selective attention but exhibit diversity in the effect of attention on firing rates and correlated variability. It remains unclear what specific neuronal properties shape these attention effects. In this study, we quantitatively characterised the distribution of attention modulation of firing rates across populations of V4 neurons. Neurons exhibited a continuum of time‐varying attention effects. At one end of the continuum, neurons' spontaneous firing rates were slightly depressed with attention (compared to when unattended), whereas their stimulus responses were enhanced with attention. The other end of the continuum showed the converse pattern: attention depressed stimulus responses but increased spontaneous activity. We tested whether the particular pattern of time‐varying attention effects that a neuron exhibited was related to the shape of their actions potentials (so‐called ‘fast‐spiking’ [FS] neurons have been linked to inhibition) and the strength of their coupling to the overall population. We found an interdependence among neural attention effects, neuron type and population coupling. In particular, we found neurons for which attention enhanced spontaneous activity but suppressed stimulus responses were less likely to be fast‐spiking (more likely to be non‐fast‐spiking) and tended to have stronger population coupling, compared to neurons with other types of attention effects. These results add important information to our understanding of visual attention circuits at the cellular level.

    

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4. Temporal coupling of field potentials and action potentials in the neocortex

   https://doi.org/10.1111/ejn.13807  

   Watson, Brendon O. ; Ding, Mingxin ; Buzsáki, György ( January 2018 , European Journal of Neuroscience)

   The local field potential (LFP) is an aggregate measure of group neuronal activity and is often correlated with the action potentials of single neurons. In recent years, investigators have found that action potential firing rates increase during elevations in power high‐frequency band oscillations (50–200 Hz range). However, action potentials also contribute to theLFPsignal itself, making the spike–LFPrelationship complex. Here, we examine the relationship between spike rates andLFPin varying frequency bands in rat neocortical recordings. We find that 50–180 Hz oscillations correlate most consistently with high firing rates, but that otherLFPbands also carry information relating to spiking, including in some cases anti‐correlations. Relatedly, we find that spiking itself and electromyographic activity contribute toLFPpower in these bands. The relationship between spike rates andLFPpower varies between brain states and between individual cells. Finally, we create an improved oscillation‐based predictor of action potential activity by specifically utilizing information from across the entire recorded frequency spectrum ofLFP. The findings illustrate both caveats and improvements to be taken into account in attempts to infer spiking activity fromLFP.

    

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5. Quantitative firing pattern phenotyping of hippocampal neuron types

   https://doi.org/10.1038/s41598-019-52611-w  

   Komendantov, Alexander O. ; Venkadesh, Siva ; Rees, Christopher L. ; Wheeler, Diek W. ; Hamilton, David J. ; Ascoli, Giorgio A. ( November 2019 , Scientific Reports)

   Systematically organizing the anatomical, molecular, and physiological properties of cortical neurons is important for understanding their computational functions. Hippocampome.org defines 122 neuron types in the rodent hippocampal formation based on their somatic, axonal, and dendritic locations, putative excitatory/inhibitory outputs, molecular marker expression, and biophysical properties. We augmented the electrophysiological data of this knowledge base by collecting, quantifying, and analyzing the firing responses to depolarizing current injections for every hippocampal neuron type from published experiments. We designed and implemented objective protocols to classify firing patterns based on 5 transients (delay, adapting spiking, rapidly adapting spiking, transient stuttering, and transient slow-wave bursting) and 4 steady states (non-adapting spiking, persistent stuttering, persistent slow-wave bursting, and silence). This automated approach revealed 9 unique (plus one spurious) families of firing pattern phenotypes while distinguishing potential new neuronal subtypes. Novel statistical associations emerged between firing responses and other electrophysiological properties, morphological features, and molecular marker expression. The firing pattern parameters, experimental conditions, spike times, references to the original empirical evidences, and analysis scripts are released open-source through Hippocampome.org for all neuron types, greatly enhancing the existing search and browse capabilities. This information, collated online in human- and machine-accessible form, will help design and interpret both experiments and model simulations.

    

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