A biologically motivated neural network for phase extraction from complex sounds

We demonstrate that natural acoustic signals like speech or music contain synchronous phase information across multiple frequency bands and show how to extract this information using a spiking neural network. This network model is motivated by common neurophysiological findings in the auditory brainstem and midbrain of several species. A computer simulation of the model was tested by applying spoken vowels and organ pipe tones. As expected, spikes occurred synchronously in the activated frequency bands. This phase information may be used for sound separation with one microphone or sound localization with two microphones.     

Study of sound localization by owls and its relevance to humans

Human psychoacoustical studies have been the main sources of information from which the brain mechanisms of sound localization are inferred. The value of animal models would be limited, if humans and the animals did not share the same perceptual experience and the neural mechanisms for it. Barn owls and humans use the same method of computing interaural time differences for localization in the horizontal plane. The behavioral performance of owls and its neural bases are consistent with some of the theories developed for human sound localization. Neural theories of sound localization largely owe their origin to the study of sound localization by humans, even though little is known about the physiological properties of the human auditory system. One of these ideas is binaural cross-correlation which assumes that the human brain performs a process similar to mathematical cross-correlation to measure the interaural time difference for localization in the horizontal plane. The most complete set of neural evidence for this theory comes from the study of sound localization and its brain mechanisms in barn owls, although partial support is also available from studies on laboratory mammals. Animal models of human sensory perception make two implicit assumptions; animals and humans experience the same percept and the same neural mechanism underlies the creation of the percept. These assumptions are hard to prove for obvious reason. This article reviews several lines of evidence that similar neural mechanisms must underlie the perception of sound locations in humans and owls.     

Optimal nonlinear cue integration for sound localization

Integration of multiple sensory cues can improve performance in detection and estimation tasks. There is an open theoretical question of the conditions under which linear or nonlinear cue combination is Bayes-optimal. We demonstrate that a neural population decoded by a population vector requires nonlinear cue combination to approximate Bayesian inference. Specifically, if cues are conditionally independent, multiplicative cue combination is optimal for the population vector. The model was tested on neural and behavioral responses in the barn owl’s sound localization system where space-specific neurons owe their selectivity to multiplicative tuning to sound localization cues interaural phase (IPD) and level (ILD) differences. We found that IPD and ILD cues are approximately conditionally independent. As a result, the multiplicative combination selectivity to IPD and ILD of midbrain space-specific neurons permits a population vector to perform Bayesian cue combination. We further show that this model describes the owl’s localization behavior in azimuth and elevation. This work provides theoretical justification and experimental evidence supporting the optimality of nonlinear cue combination.     

A functional neuroimaging study of sound localization: visual cortex activity predicts performance in early-blind individuals

Blind individuals often demonstrate enhanced nonvisual perceptual abilities. However, the neural substrate that underlies this improved performance remains to be fully understood. An earlier behavioral study demonstrated that some early-blind people localize sounds more accurately than sighted controls using monaural cues. In order to investigate the neural basis of these behavioral differences in humans, we carried out functional imaging studies using positron emission tomography and a speaker array that permitted pseudo-free-field presentations within the scanner. During binaural sound localization, a sighted control group showed decreased cerebral blood flow in the occipital lobe, which was not seen in early-blind individuals. During monaural sound localization (one ear plugged), the subgroup of early-blind subjects who were behaviorally superior at sound localization displayed two activation foci in the occipital cortex. This effect was not seen in blind persons who did not have superior monaural sound localization abilities, nor in sighted individuals. The degree of activation of one of these foci was strongly correlated with sound localization accuracy across the entire group of blind subjects. The results show that those blind persons who perform better than sighted persons recruit occipital areas to carry out auditory localization under monaural conditions. We therefore conclude that computations carried out in the occipital cortex specifically underlie the enhanced capacity to use monaural cues. Our findings shed light not only on intermodal compensatory mechanisms, but also on individual differences in these mechanisms and on inhibitory patterns that differ between sighted individuals and those deprived of vision early in life.     

Optimal Prediction of Moving Sound Source Direction in the Owl

Capturing nature’s statistical structure in behavioral responses is at the core of the ability to function adaptively in the environment. Bayesian statistical inference describes how sensory and prior information can be combined optimally to guide behavior. An outstanding open question of how neural coding supports Bayesian inference includes how sensory cues are optimally integrated over time. Here we address what neural response properties allow a neural system to perform Bayesian prediction, i.e., predicting where a source will be in the near future given sensory information and prior assumptions. The work here shows that the population vector decoder will perform Bayesian prediction when the receptive fields of the neurons encode the target dynamics with shifting receptive fields. We test the model using the system that underlies sound localization in barn owls. Neurons in the owl’s midbrain show shifting receptive fields for moving sources that are consistent with the predictions of the model. We predict that neural populations can be specialized to represent the statistics of dynamic stimuli to allow for a vector read-out of Bayes-optimal predictions.     

Modelling the modulation of cortical Up-Down state switching by astrocytes

Up-Down synchronization in neuronal networks refers to spontaneous switches between periods of high collective firing activity (Up state) and periods of silence (Down state). Recent experimental reports have shown that astrocytes can control the emergence of such Up-Down regimes in neural networks, although the molecular or cellular mechanisms that are involved are still uncertain. Here we propose neural network models made of three populations of cells: excitatory neurons, inhibitory neurons and astrocytes, interconnected by synaptic and gliotransmission events, to explore how astrocytes can control this phenomenon. The presence of astrocytes in the models is indeed observed to promote the emergence of Up-Down regimes with realistic characteristics. Our models show that the difference of signalling timescales between astrocytes and neurons (seconds versus milliseconds) can induce a regime where the frequency of gliotransmission events released by the astrocytes does not synchronize with the Up and Down phases of the neurons, but remains essentially stable. However, these gliotransmission events are found to change the localization of the bifurcations in the parameter space so that with the addition of astrocytes, the network enters a bistability region of the dynamics that corresponds to Up-Down synchronization. Taken together, our work provides a theoretical framework to test scenarios and hypotheses on the modulation of Up-Down dynamics by gliotransmission from astrocytes.     

Precognitive and cognitive elements in sound localization

Sound localization behavior is of great importance for an animal's survival. To localize a sound, animals have to detect a sound source and assign a location to it. In this review we discuss recent results on the underlying mechanisms and on modulatory influences in the barn owl, an auditory specialist with very well developed capabilities to localize sound. Information processing in the barn owl auditory pathway underlying the computations of detection and localization is well understood. This analysis of the sensory information primarily determines the following orienting behavior towards the sound source. However, orienting behavior may be modulated by cognitive (top-down) influences such as attention. We show how advanced stimulation techniques can be used to determine the importance of different cues for sound localization in quasi-realistic stimulation situations, how attentional influences can improve the response to behaviorally relevant stimuli, and how attention can modulate related neural responses. Taken together, these data indicate how sound localization might function in the usually complex natural environment.     

Stochastic model shows how cochlear implants process azimuth in real auditory space

Interaural time difference (ITD) is a major cue for sound azimuth localization at lower sound frequencies. We review two theories of how the sound localization neural circuit works. One of them proposes labeling of sound direction in the array of delay lines by maximal response of the tuning curve (Jeffress model). The other proposes detection of the direction by calculating the maximum slope of tuning curves. We formulate a simple hypothesis from this that stochastic neural response infers sound direction from this maximum slope, which supports the second theory. We calculate the output spike time density used in the readout of sound direction analytically. We show that the numerical implementation of the model yields results similar to those observed in experiments in mammals. We then go one step further and show that our model also gives similar results when a detailed implementation of the cochlear implant processor and simulation of implant to auditory nerve transduction are used, instead of the simplified model of auditory nerve input. Our results are useful in explaining some recent puzzling observations on the binaural cochlear implantees.     

The Rich Get Richer: Brain Injury Elicits Hyperconnectivity in Core Subnetworks

There remains much unknown about how large-scale neural networks accommodate neurological disruption, such as moderate and severe traumatic brain injury (TBI). A primary goal in this study was to examine the alterations in network topology occurring during the first year of recovery following TBI. To do so we examined 21 individuals with moderate and severe TBI at 3 and 6 months after resolution of posttraumatic amnesia and 15 age- and education-matched healthy adults using functional MRI and graph theoretical analyses. There were two central hypotheses in this study: 1) physical disruption results in increased functional connectivity, or hyperconnectivity, and 2) hyperconnectivity occurs in regions typically observed to be the most highly connected cortical hubs, or the “rich club”. The current findings generally support the hyperconnectivity hypothesis showing that during the first year of recovery after TBI, neural networks show increased connectivity, and this change is disproportionately represented in brain regions belonging to the brain''s core subnetworks. The selective increases in connectivity observed here are consistent with the preferential attachment model underlying scale-free network development. This study is the largest of its kind and provides the unique opportunity to examine how neural systems adapt to significant neurological disruption during the first year after injury.     

Quantitative imaging of lymphocyte membrane protein reorganization and signaling

Changes in membrane protein localization are critical to establishing cell polarity and regulating cell signaling. Fluorescence microscopy of labeled proteins allows visualization of these changes, but quantitative analysis is needed to study this aspect of cell signaling in full mechanistic detail. We have developed a novel approach for quantitative assessment of membrane protein redistribution based on four-dimensional video microscopy of fluorescently labeled proteins. Our analytic system provides robust automated methods for cell surface reconstruction, cell shape tracking, cell-surface distance measurement, and cluster formation analysis. These methods permit statistical analyses and testing of mechanistic hypotheses regarding cell signaling. We have used this approach to measure antigen-dependent clustering of signaling molecules in CD4+ T lymphocytes, obtaining clustering velocities consistent with single-particle tracking data. Our system captures quantitative differences in clustering between signaling proteins with distinct biological functions. Our methods can be generalized to a range of cell-signaling phenomena and enable novel applications not feasible with single-particle studies, such as analysis of subcellular protein localization in live organ culture.     

Visual Input Modulates Audiomotor Function via Hypothalamic Dopaminergic Neurons through a Cooperative Mechanism

Visual cues often modulate auditory signal processing, leading to improved sound detection. However, the synaptic and circuit mechanism underlying this cross-modal modulation remains poorly understood. Using larval zebrafish, we first established a cross-modal behavioral paradigm in which a preceding flash enhances sound-evoked escape behavior, which is known to be executed through auditory afferents (VIII(th) nerves) and command-like neurons (Mauthner cells). In?vivo recording revealed that the visual enhancement of auditory escape is achieved by increasing sound-evoked Mauthner cell responses. This increase in Mauthner cell responses is accounted for by the increase in the signal-to-noise ratio of sound-evoked VIII(th) nerve spiking and efficacy of VIII(th) nerve-Mauthner cell synapses. Furthermore, the visual enhancement of Mauthner cell response and escape behavior requires light-responsive dopaminergic neurons in the caudal hypothalamus and D1 dopamine receptor activation. Our findings illustrate a cooperative neural mechanism for visual modulation of audiomotor processing that involves dopaminergic neuromodulation.     

Slow manifolds within network dynamics encode working memory efficiently and robustly

Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge. In particular, we show the prevalence of a mechanism in which memories are encoded along slow stable manifolds in the network state space, leading to a phasic neuronal activation profile during memory periods. In contrast to mechanisms in which memories are directly encoded at stable attractors, these networks naturally forget stimuli over time. Despite this seeming functional disadvantage, they are more efficient in terms of how they leverage their attractor landscape and paradoxically, are considerably more robust to noise. Our results provide new hypotheses regarding how working memory function may be encoded within the dynamics of neural circuits.     

Intracellular Recording,Sensory Field Mapping,and Culturing Identified Neurons in the Leech,Hirudo medicinalis

The freshwater leech, Hirudo medicinalis, is a versatile model organism that has been used to address scientific questions in the fields of neurophysiology, neuroethology, and developmental biology. The goal of this report is to consolidate experimental techniques from the leech system into a single article that will be of use to physiologists with expertise in other nervous system preparations, or to biology students with little or no electrophysiology experience. We demonstrate how to dissect the leech for recording intracellularly from identified neural circuits in the ganglion. Next we show how individual cells of known function can be removed from the ganglion to be cultured in a Petri dish, and how to record from those neurons in culture. Then we demonstrate how to prepare a patch of innervated skin to be used for mapping sensory or motor fields. These leech preparations are still widely used to address basic electrical properties of neural networks, behavior, synaptogenesis, and development. They are also an appropriate training module for neuroscience or physiology teaching laboratories.     

History influences signal recognition: neural network models of túngara frogs

Animals often attend to only a few of the cues provided by the complex displays of conspecifics. We suggest that these perceptual biases are influenced by mechanisms of signal recognition inherited from antecedent species. We tested this hypothesis by manipulating the evolutionary history of artificial neural networks, observing how the resulting networks respond to many novel stimuli and comparing these responses to the behaviour of females in phonotaxis experiments. Networks with different evolutionary histories proved equally capable of evolving to recognize the call of the túngara frog, Physalaemus pustulosus, but exhibited distinct responses to novel stimuli. History influenced the ability of networks to predict known responses of túngara frogs; network accuracy was determined by how closely the network history approximated the hypothesized history of the túngara frog. Our findings emphasize the influence of past selection pressures on current perceptual mechanisms, and demonstrate how neural network models can be used to address behavioural questions that are intractable through traditional methods.     

Constraints on adaptation: explaining deviation from optimal sex ratio using artificial neural networks

Determining processes constraining adaptation is a major challenge facing evolutionary biology, and sex allocation has proved a useful model system for exploring different constraints. We investigate the evolution of suboptimal sex allocation in a solitary parasitoid wasp system by modelling information acquisition and processing using artificial neural networks (ANNs) evolving according to a genetic algorithm. Theory predicts an instantaneous switch from the production of male to female offspring with increasing host size, whereas data show gradual changes. We found that simple ANNs evolved towards producing sharp switches in sex ratio, but additional biologically reasonable assumptions of costs of synapse maintenance, and simplification of the ANNs, led to more gradual adjustment. Switch sharpness was robust to uncertainty in fitness consequences of host size, challenging interpretations of previous empirical findings. Our results also question some intuitive hypotheses concerning the evolution of threshold traits and confirm how neural processing may constrain adaptive behaviour.     

Spiking behavior and epileptiform oscillations in a discrete model of cortical neural networks

Summary We investigate the phenomenon of epileptiform activity using a discrete model of cortical neural networks. Our model is reduced to the elementary features of neurons and assumes simplified dynamics of action potentials and postsynaptic potentials. The discrete model provides a comparably high simulation speed which allows the rendering of phase diagrams and simulations of large neural networks in reasonable time. Further the reduction to the basic features of neurons provides insight into the essentials of a possible mechanism of epilepsy. Our computer simulations suggest that the detailed dynamics of postsynaptic and action potentials are not indispensable for obtaining epileptiform behavior on the system level. The simulation results of autonomously evolving networks exhibit a regime in which the network dynamics spontaneously switch between fluctuating and oscillating behavior and produce isolated network spikes without external stimulation. Inhibitory neurons have been found to play an important part in the synchronization of neural firing: an increased number of synapses established by inhibitory neurons onto other neurons induces a transition to the spiking regime. A decreased frequency accompanying the hypersynchronous population activity has only occurred with slow inhibitory postsynaptic potentials.     

Reticulospinal Control of Rapid Escape Turning Maneuvers in Fishes

SYNOPSIS. In teleost fishes, the C-start is a rapid avoidancemaneuver that steers the animal away from the strike trajectoryof a predator. We review how the axial motor pattern underlyingthis movement is organized by the reticulospinal system. Thissystem includes the prominent Mauthner cells and other identifiableneurons. Typical of reticulospinal cells, the Mauthner neuronhas inputs from auditory, vestibular, and visual areas and hasoutputs to the trunk motoneurons by means of mono- and disynapticcontacts. Because reticulospinal organization and function isconserved among the vertebrates, the Mauthner network is advantageousas a model for understanding control of movement.We developthree major points. First, despite its seeming anatomical simplicity,the escape network is not a simple hierarchial system with onecell that directs the entire movement. We present evidence thatthe escape response is mediated by an array of neurons whichinclude the Mauthner cells. Second, though the firing of thiscell is adequate to result in a major component of the movement,the Mauthner cell is not indispensible for its execution. Thenetwork producing this movement is capable of regulating itsoutput so that the animal produces the normal range of responsesdespite the absence of one of its major contributors. Thesefindings can be accounted for by one of two models, though themost likely one involves lateral interactions, or corollarydischarges, within the array. Third, our recent work also showsthat the movement pattern is generated by a ballistic commandwhich does not rely on movement-induced sensory feedback. Thesesystem properties allow the animal to produce a highly reliableand orchestrated motor command in only 15 msec from sensorystimulus to EMG.     

Active head rolls enhance sonar-based auditory localization performance

Animals utilize a variety of active sensing mechanisms to perceive the world around them. Echolocating bats are an excellent model for the study of active auditory localization. The big brown bat (Eptesicus fuscus), for instance, employs active head roll movements during sonar prey tracking. The function of head rolls in sound source localization is not well understood. Here, we propose an echolocation model with multi-axis head rotation to investigate the effect of active head roll movements on sound localization performance. The model autonomously learns to align the bat’s head direction towards the target. We show that a model with active head roll movements better localizes targets than a model without head rolls. Furthermore, we demonstrate that active head rolls also reduce the time required for localization in elevation. Finally, our model offers key insights to sound localization cues used by echolocating bats employing active head movements during echolocation.