Tuning curves, neuronal variability, and sensory coding

Tuning curves are widely used to characterize the responses of sensory neurons to external stimuli, but there is an ongoing debate as to their role in sensory processing. Commonly, it is assumed that a neuron's role is to encode the stimulus at the tuning curve peak, because high firing rates are the neuron's most distinct responses. In contrast, many theoretical and empirical studies have noted that nearby stimuli are most easily discriminated in high-slope regions of the tuning curve. Here, we demonstrate that both intuitions are correct, but that their relative importance depends on the experimental context and the level of variability in the neuronal response. Using three different information-based measures of encoding applied to experimentally measured sensory neurons, we show how the best-encoded stimulus can transition from high-slope to high-firing-rate regions of the tuning curve with increasing noise level. We further show that our results are consistent with recent experimental findings that correlate neuronal sensitivities with perception and behavior. This study illustrates the importance of the noise level in determining the encoding properties of sensory neurons and provides a unified framework for interpreting how the tuning curve and neuronal variability relate to the overall role of the neuron in sensory encoding.     

Functional clustering drives encoding improvement in a developing brain network during awake visual learning

Sensory experience drives dramatic structural and functional plasticity in developing neurons. However, for single-neuron plasticity to optimally improve whole-network encoding of sensory information, changes must be coordinated between neurons to ensure a full range of stimuli is efficiently represented. Using two-photon calcium imaging to monitor evoked activity in over 100 neurons simultaneously, we investigate network-level changes in the developing Xenopus laevis tectum during visual training with motion stimuli. Training causes stimulus-specific changes in neuronal responses and interactions, resulting in improved population encoding. This plasticity is spatially structured, increasing tuning curve similarity and interactions among nearby neurons, and decreasing interactions among distant neurons. Training does not improve encoding by single clusters of similarly responding neurons, but improves encoding across clusters, indicating coordinated plasticity across the network. NMDA receptor blockade prevents coordinated plasticity, reduces clustering, and abolishes whole-network encoding improvement. We conclude that NMDA receptors support experience-dependent network self-organization, allowing efficient population coding of a diverse range of stimuli.     

Efficient sparse coding in early sensory processing: lessons from signal recovery

Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as well as for using the large and sparse representations in an efficient way. We argue that higher level overcompleteness becomes computationally tractable by imposing sparsity on synaptic activity and we also show that such structural sparsity can be facilitated by statistics based decomposition of the stimuli into typical and atypical parts prior to sparse coding. Typical parts represent large-scale correlations, thus they can be significantly compressed. Atypical parts, on the other hand, represent local features and are the subjects of actual sparse coding. When applied on natural images, our decomposition based sparse coding model can efficiently form overcomplete codes and both center-surround and oriented filters are obtained similar to those observed in the retina and the primary visual cortex, respectively. Therefore we hypothesize that the proposed computational architecture can be seen as a coherent functional model of the first stages of sensory coding in early vision.     

Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons

Recordings of local field potentials (LFPs) reveal that the sensory cortex displays rhythmic activity and fluctuations over a wide range of frequencies and amplitudes. Yet, the role of this kind of activity in encoding sensory information remains largely unknown. To understand the rules of translation between the structure of sensory stimuli and the fluctuations of cortical responses, we simulated a sparsely connected network of excitatory and inhibitory neurons modeling a local cortical population, and we determined how the LFPs generated by the network encode information about input stimuli. We first considered simple static and periodic stimuli and then naturalistic input stimuli based on electrophysiological recordings from the thalamus of anesthetized monkeys watching natural movie scenes. We found that the simulated network produced stimulus-related LFP changes that were in striking agreement with the LFPs obtained from the primary visual cortex. Moreover, our results demonstrate that the network encoded static input spike rates into gamma-range oscillations generated by inhibitory–excitatory neural interactions and encoded slow dynamic features of the input into slow LFP fluctuations mediated by stimulus–neural interactions. The model cortical network processed dynamic stimuli with naturalistic temporal structure by using low and high response frequencies as independent communication channels, again in agreement with recent reports from visual cortex responses to naturalistic movies. One potential function of this frequency decomposition into independent information channels operated by the cortical network may be that of enhancing the capacity of the cortical column to encode our complex sensory environment.     

Neuronal Adaptation Translates Stimulus Gaps into a Population Code

Neurons in sensory pathways exhibit a vast multitude of adaptation behaviors, which are assumed to aid the encoding of temporal stimulus features and provide the basis for a population code in higher brain areas. Here we study the transition to a population code for auditory gap stimuli both in neurophysiological recordings and in a computational network model. Independent component analysis (ICA) of experimental data from the inferior colliculus of Mongolian gerbils reveals that the network encodes different gap sizes primarily with its population firing rate within 30 ms after the presentation of the gap, where longer gap size evokes higher network activity. We then developed a computational model to investigate possible mechanisms of how to generate the population code for gaps. Phenomenological (ICA) and functional (discrimination performance) analyses of our simulated networks show that the experimentally observed patterns may result from heterogeneous adaptation, where adaptation provides gap detection at the single neuron level and neuronal heterogeneity ensures discriminable population codes for the whole range of gap sizes in the input. Furthermore, our work suggests that network recurrence additionally enhances the network''s ability to provide discriminable population patterns.     

Sensorimotor pathways processing vibratory signals from the femoral chordotonal organ of the stick insect

The femoral chordotonal organ of stick insects senses position and velocity of movements in the femur-tibia joint, as well as tibial vibration. While sensory information about large-scale tibial movements is processed by a well-known neuronal network and elicits resistance reflexes in extensor and flexor tibiae motoneurons, it is not yet known how sensory information about vibration of the tibia is processed. We investigated the transmission of vibration stimuli to tibial extensor motoneurons and their premotor interneurons. Vibration stimuli applied to the femoral chordotonal organ evoked responses in tibial extensor and flexor muscles. During ongoing vibration this response adapted rapidly. This adaptation had no effect on the motoneuronal response to large-scale tibial movements. Recording from premotor interneurons revealed that vibratory signals were processed in part by the same interneuronal pathways as (large-scale) velocity and position information. While only certain parts of the interneuronal reflex pathways showed little or no response during vibration stimuli, most neurons responded to both position or velocity stimuli and vibration at the femoral chordotonal organ. We conclude that sensory information about vibration of the tibia shares part of the interneuronal pathways that transmit sensory information about large-scale tibial movements to the motoneurons. Accepted: 25 April 1999     

Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways

Most neurons in peripheral sensory pathways initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. It is unclear how this phenomenon affects stimulus coding in the later stages of sensory processing. Here, we show that a temporally sparse and reliable stimulus representation develops naturally in sequential stages of a sensory network with adapting neurons. As a modeling framework we employ a mean-field approach together with an adaptive population density treatment, accompanied by numerical simulations of spiking neural networks. We find that cellular adaptation plays a critical role in the dynamic reduction of the trial-by-trial variability of cortical spike responses by transiently suppressing self-generated fast fluctuations in the cortical balanced network. This provides an explanation for a widespread cortical phenomenon by a simple mechanism. We further show that in the insect olfactory system cellular adaptation is sufficient to explain the emergence of the temporally sparse and reliable stimulus representation in the mushroom body. Our results reveal a generic, biophysically plausible mechanism that can explain the emergence of a temporally sparse and reliable stimulus representation within a sequential processing architecture.     

The vestibular system implements a linear-nonlinear transformation in order to encode self-motion

Although it is well established that the neural code representing the world changes at each stage of a sensory pathway, the transformations that mediate these changes are not well understood. Here we show that self-motion (i.e. vestibular) sensory information encoded by VIIIth nerve afferents is integrated nonlinearly by post-synaptic central vestibular neurons. This response nonlinearity was characterized by a strong (～50%) attenuation in neuronal sensitivity to low frequency stimuli when presented concurrently with high frequency stimuli. Using computational methods, we further demonstrate that a static boosting nonlinearity in the input-output relationship of central vestibular neurons accounts for this unexpected result. Specifically, when low and high frequency stimuli are presented concurrently, this boosting nonlinearity causes an intensity-dependent bias in the output firing rate, thereby attenuating neuronal sensitivities. We suggest that nonlinear integration of afferent input extends the coding range of central vestibular neurons and enables them to better extract the high frequency features of self-motion when embedded with low frequency motion during natural movements. These findings challenge the traditional notion that the vestibular system uses a linear rate code to transmit information and have important consequences for understanding how the representation of sensory information changes across sensory pathways.     

Receptor guanylyl cyclases in mammalian olfactory function

The contributions of guanylyl cyclases to sensory signaling in the olfactory system have been unclear. Recently, studies of a specialized subpopulation of olfactory sensory neurons (OSNs) located in the main olfactory epithelium have provided important insights into the neuronal function of one receptor guanylyl cyclase, GC-D. Mice expressing reporters such as β-galactosidase and green fluorescent protein in OSNs that normally express GC-D have allowed investigators to identify these neurons in situ, facilitating anatomical and physiological studies of this sparse neuronal population. The specific perturbation of GC-D function in vivo has helped to resolve the role of this guanylyl cyclase in the transduction of olfactory stimuli. Similar approaches could be useful for the study of the orphan receptor GC-G, which is expressed in another distinct subpopulation of sensory neurons located in the Grueneberg ganglion. In this review, we discuss key findings that have reinvigorated the study of guanylyl cyclase function in the olfactory system.     

An efficient coding hypothesis links sparsity and selectivity of neural responses

To what extent are sensory responses in the brain compatible with first-order principles? The efficient coding hypothesis projects that neurons use as few spikes as possible to faithfully represent natural stimuli. However, many sparsely firing neurons in higher brain areas seem to violate this hypothesis in that they respond more to familiar stimuli than to nonfamiliar stimuli. We reconcile this discrepancy by showing that efficient sensory responses give rise to stimulus selectivity that depends on the stimulus-independent firing threshold and the balance between excitatory and inhibitory inputs. We construct a cost function that enforces minimal firing rates in model neurons by linearly punishing suprathreshold synaptic currents. By contrast, subthreshold currents are punished quadratically, which allows us to optimally reconstruct sensory inputs from elicited responses. We train synaptic currents on many renditions of a particular bird''s own song (BOS) and few renditions of conspecific birds'' songs (CONs). During training, model neurons develop a response selectivity with complex dependence on the firing threshold. At low thresholds, they fire densely and prefer CON and the reverse BOS (REV) over BOS. However, at high thresholds or when hyperpolarized, they fire sparsely and prefer BOS over REV and over CON. Based on this selectivity reversal, our model suggests that preference for a highly familiar stimulus corresponds to a high-threshold or strong-inhibition regime of an efficient coding strategy. Our findings apply to songbird mirror neurons, and in general, they suggest that the brain may be endowed with simple mechanisms to rapidly change selectivity of neural responses to focus sensory processing on either familiar or nonfamiliar stimuli. In summary, we find support for the efficient coding hypothesis and provide new insights into the interplay between the sparsity and selectivity of neural responses.     

AMPA受体介导的神经元和星形胶质细胞网络编程小鼠胡须感觉适应

动物感觉输入的适应性影响了它们对外界环境改变的意识和反应.感觉通路各层次,诸如感受器、传入神经和中枢系统等,反应活性的降低可能与感觉适应性相关联.在感觉适应过程中,皮层局部网络中神经元和星形胶质细胞对信号的编程机制仍有待进一步研究.利用活体双光子成像、电生理记录即药理学方法,我们分析了小鼠barrel皮层神经元和星形胶质应答重复的胡须感觉输入动力学.相同特征的胡须感觉刺激诱发了神经元和星形胶质细胞反应活性的降低,并且它们的活动在空间上和时间上去同步化,神经元和星形胶质细胞之间的缺少协调性.这种神经元和星形胶质细胞功能在空间和时间性质上的下调被局部施加AMPA受体脱敏感抑制剂所逆转.因此,在胡须感觉适应过程中,barrel皮层神经元和星形胶质细胞反应活性的下降和去同步化是由AMPA受体脱敏感参与介导完成的.     

A large-scale model of the locust antennal lobe

The antennal lobe (AL) is the primary structure within the locust’s brain that receives information from olfactory receptor neurons (ORNs) within the antennae. Different odors activate distinct subsets of ORNs, implying that neuronal signals at the level of the antennae encode odors combinatorially. Within the AL, however, different odors produce signals with long-lasting dynamic transients carried by overlapping neural ensembles, suggesting a more complex coding scheme. In this work we use a large-scale point neuron model of the locust AL to investigate this shift in stimulus encoding and potential consequences for odor discrimination. Consistent with experiment, our model produces stimulus-sensitive, dynamically evolving populations of active AL neurons. Our model relies critically on the persistence time-scale associated with ORN input to the AL, sparse connectivity among projection neurons, and a synaptic slow inhibitory mechanism. Collectively, these architectural features can generate network odor representations of considerably higher dimension than would be generated by a direct feed-forward representation of stimulus space.     

From stimulus estimation to combination sensitivity: encoding and processing of amplitude and timing information in parallel,convergent sensory pathways

Information theoretical approaches to sensory processing in electric fish have focused on the encoding of amplitude modulations in a single sensory pathway in the South American gymnotiforms. To assess the generality of these studies, we investigated the encoding of amplitude and phase modulations in the distantly related African fish Gymnarchus. In both the amplitude- and time-coding pathways, primary afferents accurately estimated the time course of random modulations whereas hindbrain neurons extracted information about specific stimulus features. Despite exhibiting a clear preference for encoding amplitude or phase, afferents and hindbrain neurons could encode significant amounts of modulation of their nonpreferred attribute. Although no increase in feature extraction performance occurred where the two pathways converge in the midbrain, neurons there were increasingly sensitive to simultaneous modulation of both attributes. A shift from accurate stimulus estimation in the periphery to increasingly sparse representations of specific features appears to be a general strategy in electrosensory processing.     

Shaping embodied neural networks for adaptive goal-directed behavior

The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves.     

In vitro classical conditioning of a gill withdrawal reflex in Aplysia: neural correlates and possible neural mechanisms

An in vitro preparation consisting of the siphon, mantle, gill, and abdominal ganglion undergoes classical conditioning when a weak tactile stimulus (CS) applied to the siphon is paired with a strong tactile stimulus to the gill (UCS). When the stimuli are paired, the CS comes to evoke a gill withdrawal reflex (GWR) which increases in amplitude with training. Only when the stimuli are paired in a classical conditioning paradigm does the CS come to evoke a GWR. With classical conditioning training there is an alteration in the synaptic efficacy between central sensory neurons and central gill motor neurons. Moreover, these changes can be observed in sensory neurons not activated by the CS. The changes observed, as evidence by the number of action potentials evoked in the gill motor neuron do not completely parallel the observed behavioral changes. It is suggested that in addition to changes in the synaptic efficacy at the sensory-motor neuron synapse, other changes in neuronal activity occur at other loci which lead to the observed behavioral changes.     

What we have learned from the study of learning in the leech

The use of invertebrate preparations has contributed greatly to our understanding of the neural basis of learning. The leech is especially useful for studying behavioral changes and their underlying neuronal mechanisms. Learning in the leech is essentially identical to that found in other animals, both vertebrate and invertebrate. Using anatomical and physiological techniques on leeches as they learn, we have begun to characterize the properties of individual neurons and neuronal networks that play a role in learning. We have been able to show two neuronal mechanisms that have not been previously associated with associative conditioning. The first has to do with the importance of contingency: one stimulus [the conditional stimulus (CS)] becomes associated with a second stimulus [the unconditional stimulus, (US)] in proportion to the ability of the CS to predict the US. We have found that important properties for encoding predictability, such as circuit reconfiguration, may lie in the US pathway. The firing of the serotonergic Retzius cells is taken as the US; consistent CS prediction of a US prevents “dropout” of a critical component of one US pathway. Throughout training, predicted USs continue to elicit a barrage of action potentials in these cells. Recurring unpredicted USs degrade both the learning and the response of the Retzius cell to the US. A second insight is that at least two US pathways contribute to learning, the Retzius cell pathway and the nociceptive (N) cell pathway. This second pathway persists after the elimination of the Retzius cell pathway. The observation of multiple US pathways raises a host of issues concerning CS–US convergence and the functional significance of distinct US pathways, and our results are discussed in terms of implications to current models of learning. © 1995 John Wiley & Sons, Inc.     

Sparse representation of sounds in the unanesthetized auditory cortex

How do neuronal populations in the auditory cortex represent acoustic stimuli? Although sound-evoked neural responses in the anesthetized auditory cortex are mainly transient, recent experiments in the unanesthetized preparation have emphasized subpopulations with other response properties. To quantify the relative contributions of these different subpopulations in the awake preparation, we have estimated the representation of sounds across the neuronal population using a representative ensemble of stimuli. We used cell-attached recording with a glass electrode, a method for which single-unit isolation does not depend on neuronal activity, to quantify the fraction of neurons engaged by acoustic stimuli (tones, frequency modulated sweeps, white-noise bursts, and natural stimuli) in the primary auditory cortex of awake head-fixed rats. We find that the population response is sparse, with stimuli typically eliciting high firing rates (>20 spikes/second) in less than 5% of neurons at any instant. Some neurons had very low spontaneous firing rates (<0.01 spikes/second). At the other extreme, some neurons had driven rates in excess of 50 spikes/second. Interestingly, the overall population response was well described by a lognormal distribution, rather than the exponential distribution that is often reported. Our results represent, to our knowledge, the first quantitative evidence for sparse representations of sounds in the unanesthetized auditory cortex. Our results are compatible with a model in which most neurons are silent much of the time, and in which representations are composed of small dynamic subsets of highly active neurons.