Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. This...
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Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. This study quantifies whether and how the brain selectively encodes stimulus features about scene identity in complex naturalistic environments. While a wealth of previous work has dug into the static and dynamic features of the population code in retinal ganglion cells (RGCs), less is known about how populations form both flexible and reliable encoding in natural moving scenes. We record from the larval salamander retina responding to five different natural movies, over many repeats, and use these data to characterize the population code in terms of single-cell fluctuations in rate and pairwise couplings between cells. Decomposing the population code into independent and cell-cell interactions reveals how broad scene structure is encoded in the retinal output. while the single-cell activity adapts to different stimuli, the population structure captured in the sparse, strong couplings is consistent across natural movies as well as synthetic stimuli. We show that these interactions contribute to encoding scene identity. We also demonstrate that this structure likely arises in part from shared bipolar cell input as well as from gap junctions between RGCs and amacrine cells.
Our memories help us plan for the future. In some cases, we use memories to repeat the choices that led to preferable outcomes in the past. The success of these memory-guided decisions depends on close interactions be...
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Our memories help us plan for the future. In some cases, we use memories to repeat the choices that led to preferable outcomes in the past. The success of these memory-guided decisions depends on close interactions between the hippocampus and medial prefrontal cortex. In other cases, we need to use our memories to deduce hidden connections between the present and past situations to decide the best choice of action based on the expected outcome. Our recent study investigated neural underpinnings of such inferential decisions by monitoring neural activity in the medial prefrontal cortex and hippocampus in rats. We identified several neural activity patterns indicating awake memory trace reactivation and restructuring of functional connectivity among multiple neurons. We also found that these patterns occurred concurrently with the ongoing hippocampal activity when rats recalled past events but not when they planned new adaptive actions. Here, we discussed how these computational properties might contribute to success in inferential decision-making and propose a working model on how the medial prefrontal cortex changes its interaction with the hippocampus depending on whether it reflects on the past or looks into the future.
The function of the nervous system in conveying and processing information necessary to interact with the environment confers unique aspects on how the expression of genes in neurons is regulated. Three salient factor...
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The function of the nervous system in conveying and processing information necessary to interact with the environment confers unique aspects on how the expression of genes in neurons is regulated. Three salient factors are that (1) neurons are the largest and among the most morphologically complex of all cells, with strict polarity, subcellular compartmentation, and long-distant transport of gene products, signaling molecules, and other materials;(2) information is coded in the temporal firing pattern of membrane depolarization;and (3) neurons must maintain a stable homeostatic level of activation to function so stimuli do not normally drive intracellular signaling to steady state. Each of these factors can require special methods of analysis differing from approaches used in non-neuronal cells. This review considers these three aspects of neuronal gene expression and the current approaches being used to analyze these special features of how the neuronal transcriptome is modulated by action potential firing.
Animal models of noise-induced hearing loss (NIHL) show a dramatic mismatch between cochlear characteristic frequency (CF, based on place of innervation) and the dominant response frequency in single auditory-nerve-fi...
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Animal models of noise-induced hearing loss (NIHL) show a dramatic mismatch between cochlear characteristic frequency (CF, based on place of innervation) and the dominant response frequency in single auditory-nerve-fiber responses to broadband sounds (i.e., distorted tonotopy, DT). This noise trauma effect is associated with decreased frequency-tuning-curve (FTC) tip-to-tail ratio, which results from decreased tip sensitivity and enhanced tail sensitivity. Notably, DT is more severe for noise trauma than for metabolic (e.g., age-related) losses of comparable degree, suggesting that individual differences in DT may contribute to speech intelligibility differences in patients with similar audiograms. Although DT has implications for many neural-coding theories for real-world sounds, it has primarily been explored in single-neuron studies that are not viable with humans. Thus, there are no noninvasive measures to detect DT. Here, frequency following responses (FFRs) to a conversational speech sentence were recorded in anesthetized male chinchillas with either normal hearing or NIHL. Tonotopic sources of FFR envelope and temporal fine structure (TFS) were evaluated in normal-hearing chinchillas. Results suggest that FFR envelope primarily reflects activity from high-frequency neurons, whereas FFR-TFS receives broad tonotopic contributions. Representation of low- and high-frequency speech power in FFRs was also assessed. FFRs in hearing-impaired animals were dominated by low-frequency stimulus power, consistent with oversensitivity of high-frequency neurons to low-frequency power. These results suggest that DT can be diagnosed noninvasively. A normalized DT metric computed from speech FFRs provides a potential diagnostic tool to test for DT in humans. A sensitive noninvasive DT metric could be used to evaluate perceptual consequences of DT and to optimize hearing-aid amplification strategies to improve tonotopic coding for hearing-impaired listeners.
Pain and itch coding mechanisms in polymodal sensory neurons remain elusive. MrgprD+ neurons represent a major polymodal population and mediate both mechanical pain and nonhistaminergic itch. Here, we show that chemog...
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Pain and itch coding mechanisms in polymodal sensory neurons remain elusive. MrgprD+ neurons represent a major polymodal population and mediate both mechanical pain and nonhistaminergic itch. Here, we show that chemogenetic activation of MrgprD+ neurons elicited both pain-and itch-related behavior in a dose -dependent manner, revealing an unanticipated compatibility between pain and itch in polymodal neurons. While VGlut2-dependent glutamate release is required for both pain and itch transmission from MrgprD+ neu-rons, the neuropeptide neuromedin B (NMB) is selectively required for itch signaling. Electrophysiological re-cordings further demonstrated that glutamate synergizes with NMB to excite NMB-sensitive postsynaptic neurons. Ablation of these spinal neurons selectively abolished itch signals from MrgprD+ neurons, without affecting pain signals, suggesting a dedicated itch-processing central circuit. These findings reveal distinct neurotransmitters and neural circuit requirements for pain and itch signaling from MrgprD+ polymodal sen-sory neurons, providing new insights on coding and processing of pain and itch.
Cells in the primary visual cortex (V1) generally respond weakly to large uniform luminance stimuli. Only a subset of V1 cells is thought to encode uniform luminance information. In natural scenes, local luminance is ...
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Cells in the primary visual cortex (V1) generally respond weakly to large uniform luminance stimuli. Only a subset of V1 cells is thought to encode uniform luminance information. In natural scenes, local luminance is an important feature for defining an object that varies and coexists with local spatial contrast. However, the strategies used by V1 cells to encode local mean luminance for spatial contrast stimuli remain largely unclear. Here, using extracellular recordings in anesthetized cats, we investigated the responses of V1 cells by comparing with those of retinal ganglion (RG) cells and lateral geniculate nucleus (LGN) cells to simultaneous and rapid changes in luminance and spatial contrast. Almost all V1 cells exhibited a strong monotonic increasing luminance tuning when they were exposed to high spatial contrast. Thus, V1 cells encode the luminance carried by spatial contrast stimuli with the monotonically increasing response function. Moreover, high contrast decreased luminance tuning of OFF cells but increased that of in ON cells in RG and LGN. The luminance and contrast tunings of LGN ON cells were highly separable as V1 cells, whereas those of LGN OFF cells were lowly separable. These asymmetrical effects of spatial contrast on ON/OFF channels might underlie the robust ability of V1 cells to perform luminance tuning when exposed to spatial contrast stimuli.
Fractional calculus and fractional-order calculus are arranged in lineage as regards the mathematical models with complexity-theoretical tenets capable of capturing the subtle molecular dynamics by the integration of ...
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Fractional calculus and fractional-order calculus are arranged in lineage as regards the mathematical models with complexity-theoretical tenets capable of capturing the subtle molecular dynamics by the integration of power-law convolution kernels into time- and space-related derivatives emerging in equations concerning the Magnetic Resonance Imaging (MRI) phenomena to which the fractional models of diffusion and relaxation are applied. Endowed with an intricate level of complexity and a unique physical and structural scaffolding at molecular and cellular levels with numerous synapses forming elaborate neural networks which entail in-depth probing and computing of patterns and signatures in individual cells and neurons, human brain as a heterogeneous medium is constituted of tissues with cells of different sizes and shapes, distributed across an extra-cellular space. Characterization of the unique brain cells is sought after to unravel the connections between different cells and tissues for accurate, reliable, robust and optimal models and computing. Accordingly, Diffusion Magnetic Resonance Imaging (DMRI), as a noninvasive and experimental imaging technique with clinical and research applications, provides a measure related to the diffusion characteristics of water in biological tissues, particularly in the brain tissues. Compatible with these aspects and beyond the diffusion coefficients' measurement, DMRI technique aims to exceed the spatial resolution of the MRI images and draw inferences from the microstructural properties of the related medium. Thus, novel tools become essential for the description of the biological (organelles, membranes, macromolecules and so on) and neurological (axons, dendrites, neurons and so forth) tissues' complexity. Mathematical model-based computational analyses with multifaceted methods to extract information from the DMRI with SpinDoctor into neuronal dynamics can provide quantitative parametric instruments in order to reflect the
IntroductionInformation transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic...
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IntroductionInformation transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic membrane properties, but how these relate to information transmission in a population of neurons is still poorly understood. A recent study investigated local learning rules and showed how a spiking neural network can learn to represent continuous signals. Our study builds on their model to explore how basic membrane properties and synaptic delays affect information transfer. MethodsThe system consisted of three input and output units and a hidden layer of 300 excitatory and 75 inhibitory leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units. After optimizing the connectivity to accurately replicate the input patterns in the output units, we transformed the model to more biologically accurate units and included synaptic delay and concurrent action potential generation in distinct neurons. We examined three different parameter regimes which comprised either identical physiological values for both excitatory and inhibitory units (Comrade), more biologically accurate values (Bacon), or the Comrade regime whose output units were optimized for low reconstruction error (HiFi). We evaluated information transmission and classification accuracy of the network with four distinct metrics: coherence, Granger causality, transfer entropy, and reconstruction error. ResultsBiophysical parameters showed a major impact on information transfer metrics. The classification was surprisingly robust, surviving very low firing and information rates, whereas information transmission overall and particularly low reconstruction error were more dependent on higher firing rates in LIF units. In AdEx units, the firing rates were lower and less information was transferred, but interestingly the highest information transmission rates were no longer overlapping wit
The brain can combine auditory and visual information to localize objects. However, the cortical substrates underlying audiovisual integration remain uncertain. Here, we show that mouse frontal cortex combines audi-to...
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The brain can combine auditory and visual information to localize objects. However, the cortical substrates underlying audiovisual integration remain uncertain. Here, we show that mouse frontal cortex combines audi-tory and visual evidence;that this combination is additive, mirroring behavior;and that it evolves with learning. We trained mice in an audiovisual localization task. Inactivating frontal cortex impaired responses to either sensory modality, while inactivating visual or parietal cortex affected only visual stimuli. Recordings from >14,000 neurons indicated that after task learning, activity in the anterior part of frontal area MOs (sec-ondary motor cortex) additively encodes visual and auditory signals, consistent with the mice's behavioral strategy. An accumulator model applied to these sensory representations reproduced the observed choices and reaction times. These results suggest that frontal cortex adapts through learning to combine evidence across sensory cortices, providing a signal that is transformed into a binary decision by a downstream accumulator.
Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neur...
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Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent nondeterministic, noisy nature of neural computations. This study introduces the noisy SNN (NSNN) and the noise-driven learning (NDL) rule by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. The NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation and learning. We demonstrate that this framework leads to spiking neural models with competitive performance, improved robustness against challenging perturbations compared with deterministic SNNs, and better reproducing probabilistic computation in neural coding. Generally, this study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.
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