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The intertwining of space and time poses a significant scientific challenge, transcending disciplines from philosophy and physics to neuroscience. Deciphering neural coding, marked by its inherent spatial and temporal dimensions, has proven to be a complex task. In this paper, we present insights into temporal and spatial modes of neural coding and their intricate interplay, drawn from neuroscientific findings. We illustrate the conversion of a purely spatial input into the temporal form of a singular spike train, demonstrating storage, transmission to remote locations, and recall through spike bursts corresponding to Sharp Wave Ripples. Moreover, the converted temporal representation can be transformed back into a spatiotemporal pattern. The principles of the transformation process are illustrated using a simple feed-forward spiking neural network. The frequencies and phases of Subthreshold Membrane potential Oscillations play a pivotal role in this framework. The model offers insights into information multiplexing and phenomena such as stretching or compressing time of spike patterns., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Connectivity is a relevant parameter for the information flow within neuronal networks. Network connectivity can be reconstructed from recorded spike train data. Various methods have been developed to estimate connectivity from spike trains., New Method: In this work, a novel effective connectivity estimation algorithm called Total Spiking Probability Edges (TSPE) is proposed and evaluated. First, a cross-correlation between pairs of spike trains is calculated. Second, to distinguish between excitatory and inhibitory connections, edge filters are applied on the resulting cross-correlogram., Results: TSPE was evaluated with large scale in silico networks and enables almost perfect reconstructions (true positive rate of approx. 99% at a false positive rate of 1% for low density random networks) depending on the network topology and the spike train duration. A distinction between excitatory and inhibitory connections was possible. TSPE is computational effective and takes less than 3 min on a high-performance computer to estimate the connectivity of an 1 h dataset of 1000 spike trains., Comparison of Existing Methods: TSPE was compared with connectivity estimation algorithms like Transfer Entropy based methods, Filtered and Normalized Cross-Correlation Histogram and Normalized Cross-Correlation. In all test cases, TSPE outperformed the compared methods in the connectivity reconstruction accuracy., Conclusions: The results show that the accuracy of functional connectivity estimation of large scale neuronal networks has been enhanced by TSPE compared to state of the art methods. Furthermore, TSPE enables the classification of excitatory and inhibitory synaptic effects., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Chopper neurons in the cochlear nucleus are characterized by intrinsic oscillations with short average interspike intervals (ISIs) and relative level independence of their response (Pfeiffer, Exp Brain Res 1:220-235, 1966; Blackburn and Sachs, J Neurophysiol 62:1303-1329, 1989), properties which are unattained by models of single chopper neurons (e.g., Rothman and Manis, J Neurophysiol 89:3070-3082, 2003a). In order to achieve short ISIs, we optimized the time constants of Rothman and Manis single neuron model with genetic algorithms. Some parameters in the optimization, such as the temperature and the capacity of the cell, turned out to be crucial for the required acceleration of their response. In order to achieve the relative level independence, we have simulated an interconnected network consisting of Rothman and Manis neurons. The results indicate that by stabilization of intrinsic oscillations, it is possible to simulate the physiologically observed level independence of ISIs. As previously reviewed and demonstrated (Bahmer and Langner, Biol Cybern 95:371-379, 2006a), chopper neurons show a preference for ISIs which are multiples of 0.4 ms. It was also demonstrated that the network consisting of two optimized Rothman and Manis neurons which activate each other with synaptic delays of 0.4 ms shows a preference for ISIs of 0.8 ms. Oscillations with various multiples of 0.4 ms as ISIs may be derived from neurons in a more complex network that is activated by simultaneous input of an onset neuron and several auditory nerve fibers.

The unique temporal and spectral properties of chopper neurons in the cochlear nucleus cannot be fully explained by current popular models. A new model of sustained chopper neurons was therefore suggested based on the assumption that chopper neurons receive input both from onset neurons and the auditory nerve (Bahmer and Langner in Biol Cybern 95:4, 2006). As a result of the interaction of broadband input from onset neurons and narrowband input from the auditory nerve, the chopper neurons in our model are characterized by a remarkable combination of sharp frequency tuning to pure tones and faithful periodicity coding. Our simulations show that the width of the spectral integration of the onset neuron is crucial for both the precision of periodicity coding and their resolution of single components of sinusoidally amplitude-modulated sine waves. One may hypothesize, therefore, that it would be an advantage if the hearing system were able to adapt the spectral integration of onset neurons to varying stimulus conditions.

Anatomical and physiological auditory data and pitch measurements are presented including some additional analysis. The data provide the basis for a new computer model of sustained chopper neurons in the ventral cochlear nucleus. New and old evidence indicating a preference for multiples of 0.4 ms in oscillations of chopper neurons in the cochlear nucleus of different species such as man, cats, and Guinea fowls, is summarized. Our hypothesis is that the time constant of 0.4 ms is due to the minimum synaptic delay of chopper neuron connections. Anatomical findings show that chopper neurons are indeed connected and can excite each other; a model of a circular network of neurons that are connected via synapses with a delay of 0.4 ms is thus plausible. Results concerning frequency tuning and dynamical properties of periodicity encoding of chopper neurons are reviewed. It is concluded that chopper neurons receive input both from auditory nerve fibres and onset neurons.

A computer model of sustained chopper neurons in the ventral cochlear nucleus is presented and investigated. In the companion paper, the underlying neurophysiological and neuroanatomical data are demonstrated. To explain the preference of chopper neurons for oscillations with periods which are multiples of a 0.4 ms synaptic delay, we suggest a model of circularly connected chopper neurons. In order to simulate chopper neurons within a physiological dynamic range for periodicity encoding, it is necessary to assume that they receive an input from onset neurons. Our computer analysis of the resulting simple neuronal network shows that it can produce stable oscillations. The chopping can be triggered by an amplitude-modulated signal (AM). The dynamic range and the synchronous response of the simulated chopper neurons to AM are enhanced significantly by an additional input from onset neurons. Physiological properties of chopper neurons in the cat, such as mean, standard deviation, and coefficient of variation of the interspike interval are matched precisely by our simulations.