Toward Functional Classification of Neuronal Types

Abstract

How many types of neurons are there in the brain? This basic neuroscience question remains unsettled despite many decades of research. Classification schemes have been proposed based on anatomical, electrophysiological or molecular properties. However, different schemes do not always agree with each other. This raises the question of whether one can classify neurons based on their function directly. For example, among sensory neurons, can a classification scheme be devised that is based on their role in encoding sensory stimuli? Here I outline theoretical arguments for how this can be achieved using information theory by looking at optimal numbers of cell types and paying attention to two key properties: correlations between inputs and noise in neural responses. This theoretical framework could help to map the hierarchical tree relating different neuronal classes within and across species.

RNA-binding proteins in neurodegeneration: mechanisms in aggregate

“…Neurons, the targets of neurodegeneration, come in countless varieties, differing in position ( Fig. 1), size, neurotransmission, and electrophysiological properties yet are united by several key features such as their polarization, ability to form networks with other neurons, and enormous energy demands (Herculano-Houzel 2012;Sharpee 2014).…”

“…Establishing the significance of such correlations will depend on the development of simpler, more accurate methods for measuring repeat length (Yum et al 2017). Along with these endeavors, ongoing DM research has a broad curative focus, from developing DM gene silencing therapies (Thornton et al 2017) to understanding the structure and role of extended hairpins and loops formed by the repeat sequences (Dere et al 2004;Yuan et al 2007;deLorimier et al 2017) and how their modular structure may be therapeutically targeted (Garcia-Lopez et al 2011;Childs-Disney et al 2012, 2014.…”

Electrosensory neural responses to natural electro-communication stimuli are distributed along a continuum

“…However, mappings between neural classifications made using each category have proven difficult to obtain [13], which is in part due to differences that are not taken into account (e.g., morphological, intrinsic firing, or synaptic connections) and the fact that neurons with vastly different molecular attributes can display similar electrophysiological properties [9, 14, 15]. It has been proposed that classifications based on neuronal function [16, 17] could help explain neural heterogeneities and provide critical insight into the neural code [18]. Here we tested whether the responses of electrosensory pyramidal neurons to natural electrosensory stimuli could be functionally classified based on their responses to stimuli alone.…”

Optimizing Neural Information Capacity through Discretization

“…Several theoretical groups have analyzed precisely this problem over the past 20 years (Brinkman et al, 2016; Brunel and Nadal, 1998; Ganguli and Simoncelli, 2014; Gjorgjieva et al, 2014; Harper and McAlpine, 2004; Kastner et al, 2015; McDonnell et al, 2006; Nikitin et al, 2009; Pitkow and Meister, 2012; Sharpee, 2014; Wei and Stocker, 2016). For the sake of specificity, our initial discussion will be for the case where discretization occurs at the level of single neurons, with neurons acting as threshold-like devices processing the same analogue inputs.…”

“…The answer depends on the reliability of individual neurons. When this reliability is low, redundant coding based on a single neuronal type provides more information about the stimulus compared to distributed coding using staggered thresholds (Kastner et al, 2015; McDonnell et al, 2006; Nikitin et al, 2009; Sharpee, 2014). When the reliability increases beyond a certain threshold, a distributed coding based on multiple thresholds, and therefore multiple neuronal types, transmits more information (Figure 1D).…”