Neuronal On- and Off-type heterogeneities improve population coding of envelope signals in the presence of stimulus-induced noise

Abstract

Understanding the mechanisms by which neuronal population activity gives rise to perception and behavior remains a central question in systems neuroscience. Such understanding is complicated by the fact that natural stimuli often have complex structure. Here we investigated how heterogeneities within a sensory neuron population influence the coding of a noisy stimulus waveform (i.e., the noise) and its behaviorally relevant envelope signal (i.e., the signal). We found that On-and Off-type neurons displayed more heterogeneities in their responses to the noise than in their responses to the signal. These differences in heterogeneities had important consequences when quantifying response similarity between pairs of neurons. Indeed, the larger response heterogeneity displayed by On-and Off-type neurons made their pairwise responses to the noise on average more independent than when instead considering pairs of On-type or Off-type neurons. Such relative independence allowed for better averaging out of the noise response when pooling neural activities in a mixed-type (i.e., On-and Offtype) than for same-type (i.e., only On-type or only Off-type), thereby leading to greater information transmission about the signal. Our results thus reveal a function for the combined activities of On-and Off-type neurons towards improving information transmission of envelope stimuli at the population level. Our results will likely generalize because natural stimuli across modalities are characterized by a stimulus waveform whose envelope varies independently as well as because On-and Off-type neurons are observed across systems and species. Understanding the set of transformations by which sensory input leads to behavioral responses remains a central problem in neuroscience. It has been widely observed that neurons display heterogeneities, even within the same class 1-6. Although much effort has focused on understanding how heterogeneities affect population coding 7-12 , considerably less effort has focused on uncovering their role in determining neural responses to the often-complex features of behaviorally relevant stimuli. Here we used the electrosensory system of the weakly electric fish Apteronotus leptorhynchus (Fig. 1A) to demonstrate how neural heterogeneities can improve signal transmission at the population level of a behaviorally relevant signal that is embedded in stimulus-induced noise. Weakly electric fish rely on perturbations of an electric field self-generated around their body through the quasi-sinusoidal electric organ discharge (EOD) in order to sense their surroundings and communicate with conspecifics. Interactions between the EODs of two or more moving conspecifics creates complex electrosensory stimuli consisting of a relatively fast time-varying amplitude modulation of the EOD whose amplitude (i.e., the envelope) varies more slowly 13,14. The time-varying envelope carries behaviorally relevant information about the distance between conspecifics 13 and behavioral studies have shown that information about the deta...

Statistical Analysis of Decoding Performances of Diverse Populations of Neurons

“…The use of these 7 cells and encoding models fit to different time periods results in appreciable ranges of heterogeneity. Our use of 7 ELL pyramidal cells is on a similar order or scale with other published works on this system: 12 cells in Mejias et al (2013) across different animals, 15 cells in Ly and Marsat (2018), 5 cells in Bastian (1986), 12 cells (and 9 pairs) in Simmonds & Chacron (2015), and 14 cells in Huang, Zhang, and Chacron (2016) but much smaller than others: 50 cells in Hofmann and Chacron (2020), and 77 cells in Chacron, Maler, and Bastian (2005). With the limited number of distinct neurons and all aforementioned limitations, we still observe effective neural coding at intermediate levels of heterogeneity.…”

“…In the electrosensory system, prior work has shown that neural attribute heterogeneities of P-units that are a prelude to ELL have a strong effect on some of the response properties: with fast frequency stimuli and a slower envelope frequency, only the slower envelope frequency responses were affected by heterogeneities, while the fast frequency P-unit responses are invariant to neural heterogeneity (Metzen & Chacron, 2015). Also, the heterogeneity of ON and OFF cell types in the ELL generally led to improved population coding of envelope signals (Hofmann & Chacron, 2020); here, the data provided consisted only of ON cells. The stimuli we considered consist of random amplitude modulations with gaussian statistics rather than more natural stimuli with faster and slower envelope frequencies.…”

Serotonin increases population coding of behaviorally relevant stimuli by enhancing responses of ON but not OFF-type sensory neurons

“…ELL pyramidal cells also receive descending input from the raphe nuclei (Rn) [ 39 ]. It is important to note that ON and OFF cells respond to the AM stimuli in opposite fashion, with ON cells responding with increased firing activity to increases in EOD amplitude and OFF cells responding with increased firing activity to decreases in EOD amplitude [ 24 , 50 ]. However, this is not the case when one considers the envelope, which varies more slowly than the AM stimulus.…”

“…However, this is not the case when one considers the envelope, which varies more slowly than the AM stimulus. As such, it is possible to have ON and OFF cells both respond to increases in the envelope and at the same time responding to increases and decreases in the AM stimulus, respectively [ 50 ].…”

Artificial Olfactory Neuron for an In‐Sensor Neuromorphic Nose

“…Both ON‐ and OFF‐type sensory neuron responses are realized by the artificial olfactory neuron module in different manners. [ 28 ] The SMO‐based gas sensor can be miniaturized, with low‐cost and high sensitivity appropriate for mobile and personalized gas sensing. [ 29 , 30 , 31 ] The 1T‐neuron has important benefits in terms of hardware cost and power consumption compared to a conventional circuit‐based CMOS neuron.…”