Information and discriminability as measures of reliability of sensory coding

Optic flow based spatial vision in insects

The optic flow, i.e., the displacement of retinal images of objects in the environment induced by self-motion, is an important source of spatial information, especially for fast-flying insects. Spatial information over a wide range of distances, from the animal's immediate surroundings over several hundred metres to kilometres, is necessary for mediating behaviours, such as landing manoeuvres, collision avoidance in spatially complex environments, learning environmental object constellations and path integration in spatial navigation. To facilitate the processing of spatial information, the complexity of the optic flow is often reduced by active vision strategies. These result in translations and rotations being largely separated by a saccadic flight and gaze mode. Only the translational components of the optic flow contain spatial information. In the first step of optic flow processing, an array of local motion detectors provides a retinotopic spatial proximity map of the environment. This local motion information is then processed in parallel neural pathways in a task-specific manner and used to control the different components of spatial behaviour. A particular challenge here is that the distance information extracted from the optic flow does not represent the distances unambiguously, but these are scaled by the animal's speed of locomotion. Possible ways of coping with this ambiguity are discussed.

Visualization of a Distributed Synaptic Memory Code in the Drosophila Brain

During associative conditioning, animals learn which sensory cues are predictive for positive or negative conditions. Because sensory cues are encoded by distributed neurons, one has to monitor plasticity across many synapses to capture how learned information is encoded. We analyzed synaptic boutons of Kenyon cells of the Drosophila mushroom body γ lobe, a brain structure that mediates olfactory learning. A fluorescent Ca²⁺ sensor was expressed in single Kenyon cells so that axonal boutons could be assigned to distinct cells and Ca²⁺ could be measured across many animals. Learning induced directed synaptic plasticity in specific compartments along the axons. Moreover, we show that odor-evoked Ca²⁺ dynamics across boutons decorrelate as a result of associative learning. Information theory indicates that learning renders the stimulus representation more distinct compared with naive stimuli. These data reveal that synaptic boutons rather than cells act as individually modifiable units, and coherence among them is a memory-encoding parameter.

Point process analysis of noise in early invertebrate vision

Noise is a prevalent and sometimes even dominant aspect of many biological processes. While many natural systems have adapted to attenuate or even usefully integrate noise, the variability it introduces often still delimits the achievable precision across biological functions. This is particularly so for visual phototransduction, the process responsible for converting photons of light into usable electrical signals (quantum bumps). Here, randomness of both the photon inputs (regarded as extrinsic noise) and the conversion process (intrinsic noise) are seen as two distinct, independent and significant limitations on visual reliability. Past research has attempted to quantify the relative effects of these noise sources by using approximate methods that do not fully account for the discrete, point process and time ordered nature of the problem. As a result the conclusions drawn from these different approaches have led to inconsistent expositions of phototransduction noise performance.

This paper provides a fresh and complete analysis of the relative impact of intrinsic and extrinsic noise in invertebrate phototransduction using minimum mean squared error reconstruction techniques based on Bayesian point process (Snyder) filters. An integrate-fire based algorithm is developed to reliably estimate photon times from quantum bumps and Snyder filters are then used to causally estimate random light intensities both at the front and back end of the phototransduction cascade. Comparison of these estimates reveals that the dominant noise source transitions from extrinsic to intrinsic as light intensity increases. By extending the filtering techniques to account for delays, it is further found that among the intrinsic noise components, which include bump latency (mean delay and jitter) and shape (amplitude and width) variance, it is the mean delay that is critical to noise performance. As the timeliness of visual information is important for real-time action, this delay could potentially limit the speed at which invertebrates can respond to stimuli. Consequently, if one wants to increase visual fidelity, reducing the photoconversion lag is much more important than improving the regularity of the electrical signal.

The invertebrate phototransduction system captures and converts environmental light inputs into electrical signals for use in later visual processing. Consequently, one would expect it to be optimised in some way to ensure that only a minimal amount of environmental information is lost during conversion. Confirming this requires an understanding and quantification of the performance limiting noise sources. Photons, which are inherently random and discrete, introduce extrinsic noise. The phototransduction cascade, which converts photons into electrical bumps possessing non-deterministic shapes and latencies, contributes intrinsic noise. Previous work on characterising the relative impact of all these sources did not account for the discrete, causal, point process nature of the problem and thus results were often inconclusive. Here we use non-linear Poisson process filtering to show that photon noise is dominant at low light intensity and cascade noise limiting at high intensity. Further, our analysis reveals that mean bump delay is the most deleterious aspect of the intrinsic noise. Our work emphasises a new approach to assessing sensory noise and provides the first complete description and evaluation of the relative impact of noise in phototransduction that does not rely on continuity, linearity or Gaussian approximations.

Visual attention: Linking prefrontal sources to neuronal and behavioral correlates

Attention is a means of flexibly selecting and enhancing a subset of sensory input based on the current behavioral goals. Numerous signatures of attention have been identified throughout the brain, and now experimenters are seeking to determine which of these signatures are causally related to the behavioral benefits of attention, and the source of these modulations within the brain. Here, we review the neural signatures of attention throughout the brain, their theoretical benefits for visual processing, and their experimental correlations with behavioral performance. We discuss the importance of measuring cue benefits as a way to distinguish between impairments on an attention task, which may instead be visual or motor impairments, and true attentional deficits. We examine evidence for various areas proposed as sources of attentional modulation within the brain, with a focus on the prefrontal cortex. Lastly, we look at studies that aim to link sources of attention to its neuronal signatures elsewhere in the brain.

Detection of acoustic temporal fine structure by cochlear implant listeners: behavioral results and computational modeling

A test of within-channel detection of acoustic temporal fine structure (aTFS) cues is presented. Eight cochlear implant listeners (CI) were asked to discriminate between two Schroeder-phase (SP) complexes using a two-alternative, forced-choice task. Because differences between the acoustic stimuli are primarily constrained to their aTFS, successful discrimination reflects a combination of the subjects' perception of and the strategy's ability to deliver aTFS cues. Subjects were mapped with single-channel Continuous Interleaved Sampling (CIS) and Simultaneous Analog Stimulation (SAS) strategies. To compare within- and across- channel delivery of aTFS cues, a 16-channel clinical HiRes strategy was also fitted. Throughout testing, SAS consistently outperformed the CIS strategy (p ≤ 0.002). For SP stimuli with F0 = 50 Hz, the highest discrimination scores were achieved with the HiRes encoding, followed by scores with the SAS and the CIS strategies, respectively. At 200 Hz, single-channel SAS performed better than HiRes (p = 0.022), demonstrating that under a more challenging testing condition, discrimination performance with a single-channel analog encoding can exceed that of a 16-channel pulsatile strategy. To better understand the intermediate steps of discrimination, a biophysical model was used to examine the neural discharges evoked by the SP stimuli. Discrimination estimates calculated from simulated neural responses successfully tracked the behavioral performance trends of single-channel CI listeners.

Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action

Insects such as flies or bees, with their miniature brains, are able to control highly aerobatic flight maneuvres and to solve spatial vision tasks, such as avoiding collisions with obstacles, landing on objects, or even localizing a previously learnt inconspicuous goal on the basis of environmental cues. With regard to solving such spatial tasks, these insects still outperform man-made autonomous flying systems. To accomplish their extraordinary performance, flies and bees have been shown by their characteristic behavioral actions to actively shape the dynamics of the image flow on their eyes ("optic flow"). The neural processing of information about the spatial layout of the environment is greatly facilitated by segregating the rotational from the translational optic flow component through a saccadic flight and gaze strategy. This active vision strategy thus enables the nervous system to solve apparently complex spatial vision tasks in a particularly efficient and parsimonious way. The key idea of this review is that biological agents, such as flies or bees, acquire at least part of their strength as autonomous systems through active interactions with their environment and not by simply processing passively gained information about the world. These agent-environment interactions lead to adaptive behavior in surroundings of a wide range of complexity. Animals with even tiny brains, such as insects, are capable of performing extraordinarily well in their behavioral contexts by making optimal use of the closed action-perception loop. Model simulations and robotic implementations show that the smart biological mechanisms of motion computation and visually-guided flight control might be helpful to find technical solutions, for example, when designing micro air vehicles carrying a miniaturized, low-weight on-board processor.

Impact and sources of neuronal variability in the fly's motion vision pathway

Nervous systems encode information about dynamically changing sensory input by changes in neuronal activity. Neuronal activity changes, however, also arise from noise sources within and outside the nervous system or from changes of the animal's behavioral state. The resulting variability of neuronal responses in representing sensory stimuli limits the reliability with which animals can respond to stimuli and may thus even affect the chances for survival in certain situations. Relevant sources of noise arising at different stages along the motion vision pathway have been investigated from the sensory input to the initiation of behavioral reactions. Here, we concentrate on the reliability of processing visual motion information in flies. Flies rely on visual motion information to guide their locomotion. They are among the best established model systems for the processing of visual motion information allowing us to bridge the gap between behavioral performance and underlying neuronal computations. It has been possible to directly assess the consequences of noise at major stages of the fly's visual motion processing system on the reliability of neuronal signals. Responses of motion sensitive neurons and their variability have been related to optomotor movements as indicators for the overall performance of visual motion computation. We address whether and how noise already inherent in the stimulus, e.g. photon noise for the visual system, influences later processing stages and to what extent variability at the output level of the sensory system limits behavioral performance. Recent advances in circuit analysis and the progress in monitoring neuronal activity in behaving animals should now be applied to understand how the animal meets the requirements of fast and reliable manoeuvres in naturalistic situations.

Variability of blowfly head optomotor responses

Behavioural responses of an animal are variable even when the animal experiences the same sensory input several times. This variability can arise from stochastic processes inherent to the nervous system. Also, the internal state of an animal may influence a particular behavioural response. In the present study, we analyse the variability of visually induced head pitch responses of tethered blowflies by high-speed cinematography. We found these optomotor responses to be highly variable in amplitude. Most of the variability can be attributed to two different internal states of the flies with high and low optomotor gain, respectively. Even within a given activity state, there is some variability of head optomotor responses. The amount of this variability differs for the two optomotor gain states. Moreover, these two activity states can be distinguished on a fine timescale and without visual stimulation, on the basis of the occurrence of peculiar head jitter movements. Head jitter goes along with high gain optomotor responses and haltere oscillations. Halteres are evolutionary transformed hindwings that oscillate when blowflies walk or fly. Their main function is to serve as equilibrium organs by detecting Coriolis forces and to mediate gaze stabilisation. However, their basic oscillating activity was also suggested to provide a gain-modulating signal. Our experiments demonstrate that halteres are not necessary for high gain head pitch to occur. Nevertheless, we find the halteres to be responsible for one component of head jitter movements. This component may be the inevitable consequence of their function as equilibrium and gaze-stabilising organs.