Nonlinear visuoauditory integration in the mouse superior colliculus

Sensory information from different modalities is processed in parallel, and then integrated in associative brain areas to improve object identification and the interpretation of sensory experiences. The Superior Colliculus (SC) is a midbrain structure that plays a critical role in integrating visual, auditory, and somatosensory input to assess saliency and promote action. Although the response properties of the individual SC neurons to visuoauditory stimuli have been characterized, little is known about the spatial and temporal dynamics of the integration at the population level. Here we recorded the response properties of SC neurons to spatially restricted visual and auditory stimuli using large-scale electrophysiology. We then created a general, population-level model that explains the spatial, temporal, and intensity requirements of stimuli needed for sensory integration. We found that the mouse SC contains topographically organized visual and auditory neurons that exhibit nonlinear multisensory integration. We show that nonlinear integration depends on properties of auditory but not visual stimuli. We also find that a heuristically derived nonlinear modulation function reveals conditions required for sensory integration that are consistent with previously proposed models of sensory integration such as spatial matching and the principle of inverse effectiveness.     

The Y cell visual pathway implements a demodulating nonlinearity

Neural encoding of sensory signals involves both linear and nonlinear processes. Determining which nonlinear operations are implemented by neural systems is crucial to understanding sensory processing. Here, we ask if demodulation, the process used to decode AM radio signals, describes how Y cells in the cat LGN nonlinearly encode the visual scene. In response to visual AM signals across?a wide range of carrier frequencies, Y cells were found to transmit a demodulated signal, with the firing rate of single-units fluctuating at the envelope frequency but not the carrier frequency. A comparison of temporal frequency tuning properties between LGN Y cells and neurons in two primary cortical areas suggests that Y cells initiate a distinct pathway that carries a demodulated representation of the visual scene to cortex. The nonlinear signal processing carried out by the Y cell pathway simplifies the neural representation of complex visual features and allows high spatiotemporal frequencies to drive cortical responses.     

Diverse processing underlying frequency integration in midbrain neurons of barn owls

Emergent response properties of sensory neurons depend on circuit connectivity and somatodendritic processing. Neurons of the barn owl’s external nucleus of the inferior colliculus (ICx) display emergence of spatial selectivity. These neurons use interaural time difference (ITD) as a cue for the horizontal direction of sound sources. ITD is detected by upstream brainstem neurons with narrow frequency tuning, resulting in spatially ambiguous responses. This spatial ambiguity is resolved by ICx neurons integrating inputs over frequency, a relevant processing in sound localization across species. Previous models have predicted that ICx neurons function as point neurons that linearly integrate inputs across frequency. However, the complex dendritic trees and spines of ICx neurons raises the question of whether this prediction is accurate. Data from in vivo intracellular recordings of ICx neurons were used to address this question. Results revealed diverse frequency integration properties, where some ICx neurons showed responses consistent with the point neuron hypothesis and others with nonlinear dendritic integration. Modeling showed that varied connectivity patterns and forms of dendritic processing may underlie observed ICx neurons’ frequency integration processing. These results corroborate the ability of neurons with complex dendritic trees to implement diverse linear and nonlinear integration of synaptic inputs, of relevance for adaptive coding and learning, and supporting a fundamental mechanism in sound localization.     

Manipulating neural activity in physiologically classified neurons: triumphs and challenges

Understanding brain function requires knowing both how neural activity encodes information and how this activity generates appropriate responses. Electrophysiological, imaging and immediate early gene immunostaining studies have been instrumental in identifying and characterizing neurons that respond to different sensory stimuli, events and motor actions. Here we highlight approaches that have manipulated the activity of physiologically classified neurons to determine their role in the generation of behavioural responses. Previous experiments have often exploited the functional architecture observed in many cortical areas, where clusters of neurons share response properties. However, many brain structures do not exhibit such functional architecture. Instead, neurons with different response properties are anatomically intermingled. Emerging genetic approaches have enabled the identification and manipulation of neurons that respond to specific stimuli despite the lack of discernable anatomical organization. These approaches have advanced understanding of the circuits mediating sensory perception, learning and memory, and the generation of behavioural responses by providing causal evidence linking neural response properties to appropriate behavioural output. However, significant challenges remain for understanding cognitive processes that are probably mediated by neurons with more complex physiological response properties. Currently available strategies may prove inadequate for determining how activity in these neurons is causally related to cognitive behaviour.     

Dynamic properties of the action potential encoder in an insect mechanosensory neuron

A variety of sensory receptors show adaptation to dynamic stimuli that can be well characterized as fractional differentiation of the input signal. The cause of this behavior is unknown, but because it can be represented by linear systems theory, it has been assumed to arise during early linear processes of transduction or adaptation, rather than during the nonlinear process of action potential encoding. I measured the action potential encoding properties of an insect mechanoreceptor by direct electrical stimulation of the sensory cell axon and found a dynamic response that is identical to the response given by mechanical stimulation. This indicates that the fractional differentiation is a property of the encoder rather than the transducer.     

The systems analysis approach to mechanosensory coding

An important problem in neuroscience is to obtain quantitative knowledge of how information is represented, or encoded, in the signals that nerve cells process and transmit. Sensory receptors have provided important models for the study of neural coding because their inputs can often be relatively easily controlled and measured, while the resultant activity is recorded. A variety of engineering concepts have been successfully applied to physiological sciences, particularly those related to control of dynamic systems. Linear systems analysis was one of the earliest methods used to probe sensory coding, and measurements such as step responses and frequency responses have become standard tools for describing sensory functions. Modern systems analysis has evolved to provide accurate and efficient linear identification of encoding in sensory receptors that use either graded potentials or action potentials. It has also led to nonlinear systems analysis, the creation of parametric nonlinear models, and measures of information coding by sensory neurons. These methods promise to provide important new knowledge about sensory systems in the future, especially when complemented with parallel biophysical and molecular studies of sensory neurons. Mechanoreceptors provided some of the earliest preparations for the investigation of neural coding, and both the linear and nonlinear properties of wide variety of vertebrate and invertebrate mechanoreceptors continue to be explored. This article is part of a special issue on Neuronal Dynamics of Sensory Coding.     

Sensory transduction in an insect mechanoreceptor: extended bandwidth measurements and sensitivity to simulus strenght

The dynamic properties of sensory transduction in an insect mechanoreceptor, the femoral tactile spine of the cockroach, Periplaneta americana, have been studied by measurement of the frequency response function between randomly varying movement of the tactile spine and afferent action potentials from the sensory neuron which innervates it. The frequency response function of the mechanoreceptor has been characterized over a frequency range which is more than ten times larger than has previously been used for this preparation. Also the effects of varying the amplitude of the stimulating signal have been studied by the use of a range of input signal strengths from about 0.5 to 10 m R.M.S. displacement. The measured frequency response functions can all be well fitted by a theoretical relationship which is a fractional exponent of complex frequency, provided that the time delay caused by conduction of the action potentials from the sensory dendrite to the recording electrodes is taken into account. Under small signal conditions the exponent of complex frequency is close to 0.5 but with larger displacements its value decreases to about half this value. The overall sensitivity of the receptor, as measured by the gain of the frequency response function at a natural frequency of 1 radian/s, is not significantly altered by changes in the input movement amplitude, so that the receptor behaves linearly in this respect. However, the mean rate of action potential occurrence is not linearly related to input movement amplitude. These results are discussed in terms of current theories of sensory transduction and the possible role of tubular bodies in the dynamic behaviour of insect cuticular mechanoreceptors.     

Why individual thermo sensation and pain perception varies? Clue of disruptive mutations in TRPVs from 2504 human genome data

Every individual varies in character and so do their sensory functions and perceptions. The molecular mechanism and the molecular candidates involved in these processes are assumed to be similar if not same. So far several molecular factors have been identified which are fairly conserved across the phylogenetic tree and are involved in these complex sensory functions. Among all, members belonging to Transient Receptor Potential (TRP) channels have been widely characterized for their involvement in thermo-sensation. These include TRPV1 to TRPV4 channels which reveal complex thermo-gating behavior in response to changes in temperature. The molecular evolution of these channels is highly correlative with the thermal response of different species. However, recent 2504 human genome data suggest that these thermo-sensitive TRPV channels are highly variable and carry possible deleterious mutations in human population. These unexpected findings may explain the individual differences in terms of complex sensory functions.     

Role of type-specific neuron properties in a spinal cord motor network

Recent recordings from spinal neurons in hatchling frog tadpoles allow their type-specific properties to be defined. Seven main types of neuron involved in the control of swimming have been characterized. To investigate the significance of type-specific properties, we build models of each neuron type and assemble them into a network using known connectivity between: sensory neurons, sensory pathway interneurons, central pattern generator (CPG) interneurons and motoneurons. A single stimulus to a sensory neuron initiates swimming where modelled neuronal and network activity parallels physiological activity. Substitution of firing properties between neuron types shows that those of excitatory CPG interneurons are critical for stable swimming. We suggest that type-specific neuronal properties can reflect the requirements for involvement in one particular network response (like swimming), but may also reflect the need to participate in more than one response (like swimming and slower struggling). Action Editor: Eberhard E. Fetz     

Otoacoustic emissions from insect ears: evidence of active hearing?

Sensitive hearing organs often employ nonlinear mechanical sound processing which generates distortion-product otoacoustic emissions (DPOAE). Such emissions are also recordable from tympanal organs of insects. In vertebrates (including humans), otoacoustic emissions are considered by-products of active sound amplification through specialized sensory receptor cells in the inner ear. Force generated by these cells primarily augments the displacement amplitude of the basilar membrane and thus increases auditory sensitivity. As in vertebrates, the emissions from insect ears are based on nonlinear mechanical properties of the sense organ. Apparently, to achieve maximum sensitivity, convergent evolutionary principles have been realized in the micromechanics of these hearing organs-although vertebrates and insects possess quite different types of receptor cells in their ears. Just as in vertebrates, otoacoustic emissions from insects ears are vulnerable and depend on an intact metabolism, but so far in tympanal organs, it is not clear if auditory nonlinearity is achieved by active motility of the sensory neurons or if passive cellular characteristics cause the nonlinear behavior. In the antennal ears of flies and mosquitoes, however, active vibrations of the flagellum have been demonstrated. Our review concentrates on experiments studying the tympanal organs of grasshoppers and moths; we show that their otoacoustic emissions are produced in a frequency-specific way and can be modified by electrical stimulation of the sensory cells. Even the simple ears of notodontid moths produce distinct emissions, although they have just one auditory neuron. At present it is still uncertain, both in vertebrates and in insects, if the nonlinear amplification so essential for sensitive sound processing is primarily due to motility of the somata of specialized sensory cells or to active movement of their (stereo-)cilia. We anticipate that further experiments with the relatively simple ears of insects will help answer these questions.     

Modeling convergent ON and OFF pathways in the early visual system

For understanding the computation and function of single neurons in sensory systems, one needs to investigate how sensory stimuli are related to a neuron’s response and which biological mechanisms underlie this relationship. Mathematical models of the stimulus–response relationship have proved very useful in approaching these issues in a systematic, quantitative way. A starting point for many such analyses has been provided by phenomenological “linear–nonlinear” (LN) models, which comprise a linear filter followed by a static nonlinear transformation. The linear filter is often associated with the neuron’s receptive field. However, the structure of the receptive field is generally a result of inputs from many presynaptic neurons, which may form parallel signal processing pathways. In the retina, for example, certain ganglion cells receive excitatory inputs from ON-type as well as OFF-type bipolar cells. Recent experiments have shown that the convergence of these pathways leads to intriguing response characteristics that cannot be captured by a single linear filter. One approach to adjust the LN model to the biological circuit structure is to use multiple parallel filters that capture ON and OFF bipolar inputs. Here, we review these new developments in modeling neuronal responses in the early visual system and provide details about one particular technique for obtaining the required sets of parallel filters from experimental data.     

System analysis of Phycomyces light-growth response with sum-of-sinusoids test stimuli.

The light-growth response of Phycomyces has been studied with the sum-of-sinusoids method of nonlinear system identification (Victor, J.D., and R.M. Shapley, 1980, Biophys. J., 29:459). This transient response of the sporangiophore has been treated as a black-box system with one input (logarithm of the light intensity, I) and one output (elongation rate). The light intensity was modulated so that log I, as a function of time, was a sum of sinusoids. The log-mean intensity was 10(-4) W m-2 and the wavelength was 477 nm. The first- and second-order frequency kernels, which represent the linear and nonlinear behavior of the system, were obtained from the Fourier transform of the response at the appropriate component and combination frequencies. Although the first-order kernel accounts for most of the response, there remains a significant nonlinearity beyond the logarithmic transducer presumed to occur at the input of the sensory transduction chain. From the analysis of the frequency kernels, we have derived a dynamic nonlinear model of the light-growth response system. The model consists of a nonlinear subsystem followed by a linear subsystem. The model parameters were estimated from a combined nonlinear least-squares fit to the first- and second-order frequency kernels.     

The role of spike timing for thalamocortical processing

Although the response properties of sensory neurons in the thalamus and cerebral cortex have been studied for decades, relatively few studies have examined how sensory information is processed at thalamocortical synapses. Recent studies now show that the strength of thalamocortical connections is very dynamic and spike timing plays an important role in determining whether action potentials will be transferred from thalamus to cortex.     

The Essential Complexity of Auditory Receptive Fields

Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.     

Learning to hear: plasticity of auditory cortical processing

Sensory experience and auditory cortex plasticity are intimately related. This relationship is most striking during infancy when changes in sensory input can have profound effects on the functional organization of the developing cortex. But a considerable degree of plasticity is retained throughout life, as demonstrated by the cortical reorganization that follows damage to the sensory periphery or by the more controversial changes in response properties that are thought to accompany perceptual learning. Recent studies in the auditory system have revealed the remarkably adaptive nature of sensory processing and provided important insights into the way in which cortical circuits are shaped by experience and learning.     

Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs.     

Simple molecular model for sensing and adaptation based on receptor modification with application to bacterial chemotaxis

A series of simple models to explain adaptation in a sensory system based on reversible covalent modification is developed. The models are applied to the reversible methylation of chemoreceptors in bacteria and by analogy to other sensory transduction systems. The receptor modification system exhibits sensing and adaptation, i.e. raising the stimulus to a new level generates a transient response followed by a return to prestimulus behavior. By means of an analytical solution of the kinetic equation that governs the evolution of the receptor system. an exact expression is obtained for the time required for adaptation. The results account for the most conspicuous properties of the bacterial sensory system; namely, the response times in relation to stimulus changes, the proportionality of receptor modification to receptor occupancy, and the additivity of response times. The analysis indicates how these properties depend upon the parameters of the system, e.g. the rates of covalent modification and demodification, the accuracy of the detector, and the molecular nature of the response regulator. The theory developed for analysis of the bacterial system revealed properties that will be applicable to any system processing sensory information.