Neural coding: A single neuron’s perspective

Abstract

What any sensory neuron knows about the world is one of the cardinal questions in Neuroscience. Information from the sensory periphery travels across synaptically coupled neurons as each neuron encodes information by varying the rate and timing of its action potentials (spikes). Spatiotemporally correlated changes in this spiking regimen across neuronal populations are the neural basis of sensory representations. In the somatosensory cortex, however, spiking of individual (or pairs of) cortical neurons is only minimally informative about the world. Recent studies showed that one solution neurons implement to counteract this information loss is adapting their rate of information transfer to the ongoing synaptic activity by changing the membrane potential at which spike is generated. Here we first introduce the principles of information flow from the sensory periphery to the primary sensory cortex in a model sensory (whisker) system, and subsequently discuss how the adaptive spike threshold gates the intracellular information transfer from the somatic post-synaptic potential to action potentials, controlling the information content of communication across somatosensory cortical neurons.

Prominent Inhibitory Projections Guide Sensorimotor Computation: An Invertebrate Perspective

“…The ability of PCs to contribute to motor control is owing to the fact that, firstly, they are inhibitory neurons and secondly they have a relatively high spontaneous firing frequency that can be modulated. [96] The sensorimotor circuit organization across the evolutionary tree might serve as an example for the adaptive changes in circuit organization. [91,92] Indeed, this type of modulation of inhibitory neurons is not limited to mammals; it has recently been shown that the C. elegans AWA neuron is capable of firing an all-or-none calcium-based action potentials.…”

“…Such circuitry is likely to have appeared early during multicellular organisms' evolution and as organisms are exposed to new environments, the sensorimotor computation must also increase in complexity to allow existing networks to adapt and gain new functionality. [96] The sensorimotor circuit organization across the evolutionary tree might serve as an example for the adaptive changes in circuit organization. Over time, the basic circuit elements evolve into more complex, parallel circuit loops that enable advanced multimodal computation, integration of mnemonic information, and executive control for top-down regulation of the www.advancedsciencenews.com www.bioessays-journal.com sensorimotor control (see Figure 2 for a phylogenetic view on the circuits from the sea slug to fruit fly).…”

PASER for automated analysis of neural signals recorded in pulsating magnetic fields

“…The massive size of the subsequent raw voltage traces will make it infeasible to store them for offline processing. Moreover, real-time spike sorting allows for experimental conditions that adapt according to the neural responses that are observed which makes room for wider scientific investigation including but not limited to the neural basis of adaptive sensorimotor computation [78,79], contextual information processing [80][81][82][83] and storage [84][85][86][87], navigation [88][89][90], and information transfer in neural circuits [46,48]. Brain-machine-interfaces (BMI), like limb prosthetics, also necessitate that spike sorting is performed in real-time on a time-scale of hundreds of milliseconds [65], as these are usually controlled by direct neuronal signalling that is measured invasively by an array of electrodes [91].…”

“…Spike sorting plays a central role in the data processing pipeline, because accurate identification of response properties of single neurons is essential for understanding the principles of neural coding and activity dependent expression of plasticity in networks [46][47][48][49][50]. A reason why response properties of single neurons cannot be predicted by the local population dynamics is that individual neurons can display significantly different firing patterns compared to adjacent neurons.…”

Serotonergic development of active sensing

“…Considering that lack of adaptive whisking reduces mechanical forces traveling along whiskers upon whisker touch (Fig.3) A computational circuit that could perform adaptive sensorimotor control necessarily requires information from sensory circuits about the stimulus availability as well as motor control circuits that perform phase to motor signal transformation given the current state of the sensory information. Based on the known coding properties of the neurons along sensorimotor circuits, and the connectivity between them (see Discussion), the graph network consists of the following nodes ( Fig.4A): 1) primary somatosensory cortex (S1; barrel cortex) where stimulus properties are encoded (Brecht and Sakmann, 2002;Ganguly and Kleinfeld, 2004;Crochet and Petersen, 2006;Curtis and Kleinfeld, 2009;de Kock and Sakmann, 2009;Lundstrom et al, 2010;Azarfar et al, 2018a); 2) primary motor cortex (M1) which provides adaptive motor control for whisker protraction (Berg and Kleinfeld, 2003a;Brecht et al, 2004;Diamond et al, 2008;Petersen, 2014;Sreenivasan et al, 2016), through recursively adjusting the amplitude and midpoint of whisking envelope (Hill et al, 2011); 3) central pattern generators (CPGs) that control phasic motion of whiskers (Gao et al, 2001;Cramer and Keller, 2006;Kleinfeld et al, 2015); 4) superior colliculus (SC) which translates phase and amplitude information to motor control commands for facial motor nucleus (FMN) to drive whisking (Hemelt and Keller, 2008); 5) dorsal raphe nucleus (DRN) that regulates excitability in cortical and subcortical (sensorimotor) nuclei (Schubert et al, 2015); and 6) a control circuit, plausibly the barrel cortex (Matyas et al, 2010), that triggers whisker retraction upon stimulation to maintain touch duration (Azarfar and Celikel, 2019). In this model output of each node is a transfer function rather than a time and/or rate varying action potentials.…”