Reliability of signal transfer at a tonically transmitting, graded potential synapse of the locust ocellar pathway

Electrophysiology and the magnetic sense: a guide to best practice

Magnetoreception, sensing the Earth’s magnetic field, is used by many species in orientation and navigation. While this is established on the behavioural level, there is a severe lack in knowledge on the underlying neuronal mechanisms of this sense. A powerful technique to study the neuronal processing of magnetic cues is electrophysiology but, thus far, few studies have adopted this technique. Why is this the case? A fundamental problem is the introduction of electromagnetic noise (induction) caused by the magnetic stimuli, within electrophysiological recordings which, if too large, prevents feasible separation of neuronal signals from the induction artefacts. Here, we address the concerns surrounding the use of electromagnetic coils within electrophysiology experiments and assess whether these would prevent viable electrophysiological recordings within a generated magnetic field. We present calculations of the induced voltages in typical experimental situations and compare them against the neuronal signals measured with different electrophysiological techniques. Finally, we provide guidelines that should help limit and account for possible induction artefacts. In conclusion, if great care is taken, viable electrophysiological recordings from magnetoreceptive cells are achievable and promise to provide new insights on the neuronal basis of the magnetic sense.

Sensory Processing at Ribbon Synapses in the Retina and the Cochlea

In recent years, sensory neuroscientists have made major efforts to dissect the structure and function of ribbon synapses which process sensory information in the eye and ear. This review aims to summarize our current understanding of two key aspects of ribbon synapses: 1) their mechanisms of exocytosis and endocytosis and 2) their molecular anatomy and physiology. Our comparison of ribbon synapses in the cochlea and the retina reveals convergent signaling mechanisms, as well as divergent strategies in different sensory systems.

Elements of qualitative cognition: An information topology perspective

Elementary quantitative and qualitative aspects of consciousness are investigated conjointly from the biology, neuroscience, physic and mathematic point of view, by the mean of a theory written with Bennequin that derives and extends information theory within algebraic topology. Information structures, that accounts for statistical dependencies within n-body interacting systems are interpreted a la Leibniz as a monadic-panpsychic framework where consciousness is information and physical, and arise from collective interactions. The electrodynamic intrinsic nature of consciousness, sustained by an analogical code, is illustrated by standard neuroscience and psychophysic results. It accounts for the diversity of the learning mechanisms, including adaptive and homeostatic processes on multiple scales, and details their expression within information theory. The axiomatization and logic of cognition are rooted in measure theory expressed within a topos intrinsic probabilistic constructive logic. Information topology provides a synthesis of the main models of consciousness (Neural Assemblies, Integrated Information, Global Neuronal Workspace, Free Energy Principle) within a formal Gestalt theory, an expression of information structures and patterns in correspondence with Galois cohomology and discrete symmetries. The methods provide new formalization of deep neural network with homologicaly imposed architecture applied to challenges in AI-machine learning.

Axonal noise as a source of synaptic variability

Post-synaptic potential (PSP) variability is typically attributed to mechanisms inside synapses, yet recent advances in experimental methods and biophysical understanding have led us to reconsider the role of axons as highly reliable transmission channels. We show that in many thin axons of our brain, the action potential (AP) waveform and thus the Ca++ signal controlling vesicle release at synapses will be significantly affected by the inherent variability of ion channel gating. We investigate how and to what extent fluctuations in the AP waveform explain observed PSP variability. Using both biophysical theory and stochastic simulations of central and peripheral nervous system axons from vertebrates and invertebrates, we show that channel noise in thin axons (<1 µm diameter) causes random fluctuations in AP waveforms. AP height and width, both experimentally characterised parameters of post-synaptic response amplitude, vary e.g. by up to 20 mV and 0.5 ms while a single AP propagates in C-fibre axons. We show how AP height and width variabilities increase with a ¾ power-law as diameter decreases and translate these fluctuations into post-synaptic response variability using biophysical data and models of synaptic transmission. We find for example that for mammalian unmyelinated axons with 0.2 µm diameter (matching cerebellar parallel fibres) axonal noise alone can explain half of the PSP variability in cerebellar synapses. We conclude that axonal variability may have considerable impact on synaptic response variability. Thus, in many experimental frameworks investigating synaptic transmission through paired-cell recordings or extracellular stimulation of presynaptic neurons, causes of variability may have been confounded. We thereby show how bottom-up aggregation of molecular noise sources contributes to our understanding of variability observed at higher levels of biological organisation.

A theory for how sensorimotor skills are learned and retained in noisy and nonstationary neural circuits

During the process of skill learning, synaptic connections in our brains are modified to form motor memories of learned sensorimotor acts. The more plastic the adult brain is, the easier it is to learn new skills or adapt to neurological injury. However, if the brain is too plastic and the pattern of synaptic connectivity is constantly changing, new memories will overwrite old memories, and learning becomes unstable. This trade-off is known as the stability-plasticity dilemma. Here a theory of sensorimotor learning and memory is developed whereby synaptic strengths are perpetually fluctuating without causing instability in motor memory recall, as long as the underlying neural networks are sufficiently noisy and massively redundant. The theory implies two distinct stages of learning--preasymptotic and postasymptotic--because once the error drops to a level comparable to that of the noise-induced error, further error reduction requires altered network dynamics. A key behavioral prediction derived from this analysis is tested in a visuomotor adaptation experiment, and the resultant learning curves are modeled with a nonstationary neural network. Next, the theory is used to model two-photon microscopy data that show, in animals, high rates of dendritic spine turnover, even in the absence of overt behavioral learning. Finally, the theory predicts enhanced task selectivity in the responses of individual motor cortical neurons as the level of task expertise increases. From these considerations, a unique interpretation of sensorimotor memory is proposed--memories are defined not by fixed patterns of synaptic weights but, rather, by nonstationary synaptic patterns that fluctuate coherently.

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.

Structural organization of the presynaptic density at identified synapses in the locust central nervous system

In a synaptic active zone, vesicles aggregate around a densely staining structure called the presynaptic density. We focus on its three-dimensional architecture and a major molecular component in the locust. We used electron tomography to study the presynaptic density in synapses made in the brain by identified second-order neuron of the ocelli. Here, vesicles close to the active zone are organized in two rows on either side of the presynaptic density, a level of organization not previously reported in insect central synapses. The row of vesicles that is closest to the density's base includes vesicles docked with the presynaptic membrane and thus presumably ready for release, whereas the outer row of vesicles does not include any that are docked. We show that a locust ortholog of the Drosophila protein Bruchpilot is localized to the presynaptic density, both in the ocellar pathway and compound eye visual neurons. An antibody recognizing the C-terminus of the Bruchpilot ortholog selectively labels filamentous extensions of the presynaptic density that reach out toward vesicles. Previous studies on Bruchpilot have focused on its role in neuromuscular junctions in Drosophila, and our study shows it is also a major functional component of presynaptic densities in the central nervous system of an evolutionarily distant insect. Our study thus reveals Bruchpilot executes similar functions in synapses that can sustain transmission of small graded potentials as well as those relaying large, spike-evoked signals.

Sparse but specific temporal coding by spikes in an insect sensory-motor ocellar pathway

We investigate coding in a locust brain neuron, DNI, which transforms graded synaptic input from ocellar L-neurons into axonal spikes that travel to excite particular thoracic flight neurons. Ocellar neurons are naturally stimulated by fluctuations in light collected from a wide field of view, for example when the visual horizon moves up and down. We used two types of stimuli: fluctuating light from a light-emitting diode (LED), and a visual horizon displayed on an electrostatic monitor. In response to randomly fluctuating light stimuli delivered from the LED, individual spikes in DNI occur sparsely but are timed to sub-millisecond precision, carrying substantial information: 4.5–7 bits per spike in our experiments. In response to these light stimuli, the graded potential signal in DNI carries considerably less information than in presynaptic L-neurons. DNI is excited in phase with either sinusoidal light from an LED or a visual horizon oscillating up and down at 20 Hz, and changes in mean light level or mean horizon level alter the timing of excitation for each cycle. DNI is a multimodal interneuron, but its ability to time spikes precisely in response to ocellar stimulation is not degraded by additional excitation. We suggest that DNI is part of an optical proprioceptor system, responding to the optical signal induced in the ocelli by nodding movements of the locust head during each wing-beat.

Reliability and frequency response of excitatory signals transmitted to different types of retinal ganglion cell

The same visual stimulus evokes a different pattern of neural signals each time the stimulus is presented. Because this unreliability reduces visual performance, it is important to understand how it arises from neural circuitry. We asked whether different types of ganglion cell receive excitatory signals with different reliability and frequency content and, if so, how retinal circuitry contributes to these differences. If transmitter release is governed by Poisson statistics, the SNR of the postsynaptic currents (ratio of signal power to noise power) should grow linearly with quantal rate (qr), a prediction that we confirmed experimentally. Yet ganglion cells of the same type receive quanta at different rates. Thus to obtain a measure of reliability independent of quantal rate, we calculated the ratio SNR/qr, and found this measure to be type-specific. We also found type-specific differences in the frequency content of postsynaptic currents, although types whose dendrites branched at nearby levels of the inner plexiform layer (IPL) had similar frequency content. As a result, there was an orderly distribution of frequency response through the depth of the IPL, with alternating layers of broadband and high-pass signals. Different types of bipolar cell end at different depths of the IPL and provide excitatory synapses to ganglion cell dendrites there. Thus these findings indicate that a bipolar cell synapse conveys signals whose temporal message and reliability (SNR/qr) are determined by neuronal type. The final SNR of postsynaptic currents is set by the dendritic membrane area of a ganglion cell, which sets the numbers of bipolar cell synapses and thus the rate at which it receives quanta [SNR = qr x (SNR/qr)].

Visual coding in locust photoreceptors

Information capture by photoreceptors ultimately limits the quality of visual processing in the brain. Using conventional sharp microelectrodes, we studied how locust photoreceptors encode random (white-noise, WN) and naturalistic (1/f stimuli, NS) light patterns in vivo and how this coding changes with mean illumination and ambient temperature. We also examined the role of their plasma membrane in shaping voltage responses. We found that brightening or warming increase and accelerate voltage responses, but reduce noise, enabling photoreceptors to encode more information. For WN stimuli, this was accompanied by broadening of the linear frequency range. On the contrary, with NS the signaling took place within a constant bandwidth, possibly revealing a ‘preference’ for inputs with 1/f statistics. The faster signaling was caused by acceleration of the elementary phototransduction current - leading to bumps - and their distribution. The membrane linearly translated phototransduction currents into voltage responses without limiting the throughput of these messages. As the bumps reflected fast changes in membrane resistance, the data suggest that their shape is predominantly driven by fast changes in the light-gated conductance. On the other hand, the slower bump latency distribution is likely to represent slower enzymatic intracellular reactions. Furthermore, the Q₁₀s of bump duration and latency distribution depended on light intensity. Altogether, this study suggests that biochemical constraints imposed upon signaling change continuously as locust photoreceptors adapt to environmental light and temperature conditions.

Information and discriminability as measures of reliability of sensory coding

Response variability is a fundamental issue in neural coding because it limits all information processing. The reliability of neuronal coding is quantified by various approaches in different studies. In most cases it is largely unclear to what extent the conclusions depend on the applied reliability measure, making a comparison across studies almost impossible. We demonstrate that different reliability measures can lead to very different conclusions even if applied to the same set of data: in particular, we applied information theoretical measures (Shannon information capacity and Kullback-Leibler divergence) as well as a discrimination measure derived from signal-detection theory to the responses of blowfly photoreceptors which represent a well established model system for sensory information processing. We stimulated the photoreceptors with white noise modulated light intensity fluctuations of different contrasts. Surprisingly, the signal-detection approach leads to a safe discrimination of the photoreceptor response even when the response signal-to-noise ratio (SNR) is well below unity whereas Shannon information capacity and also Kullback-Leibler divergence indicate a very low performance. Applying different measures, can, therefore, lead to very different interpretations concerning the system's coding performance. As a consequence of the lower sensitivity compared to the signal-detection approach, the information theoretical measures overestimate internal noise sources and underestimate the importance of photon shot noise. We stress that none of the used measures and, most likely no other measure alone, allows for an unbiased estimation of a neuron's coding properties. Therefore the applied measure needs to be selected with respect to the scientific question and the analyzed neuron's functional context.

Synapses in the fly motion-vision pathway: evidence for a broad range of signal amplitudes and dynamics

Synapses are generally considered to operate efficiently only when their signaling range matches the spectrum of prevailing presynaptic signals in terms of both amplitudes and dynamics. However, the prerequisites for optimally matching the signaling ranges may differ between spike-mediated and graded synaptic transmission. This poses a problem for synapses that convey both graded and spike signals at the same time. We addressed this issue by tracing transmission systematically in vivo in the blowfly's visual-motion pathway by recording from single neurons that receive mixed potential signals consisting of rather slow graded fluctuations superimposed with highly variable spikes from a small number of presynaptic elements. Both pre- and postsynaptic neurons were previously shown to represent preferred-direction motion velocity reliably and linearly at low fluctuation frequencies. To selectively assess the performance of individual synapses and to precisely control presynaptic signals, we voltage clamped one of the presynaptic neurons. Results showed that synapses can effectively convey signals over a much larger amplitude and frequency range than is normally used during graded transmission of visual signals. An explanation for this unexpected finding might lie in the transmission of the spike component that reaches larger amplitudes and contains higher frequencies than graded signals.

Not by spikes alone: responses of coordinating neurons and the swimmeret system to local differences in excitation

Swimmeret coordinating neurons in the crayfish CNS collectively encode a detailed cycle-by-cycle report on features of the motor output to each swimmeret. This information coordinates the motor output that drives swimmeret movements. To see how coordinating neurons responded to forced changes in intersegmental phase, we used a split-bath, repeated-measures experimental design to expose different regions of isolated abdominal nerve cords to different levels of excitation. We present a quantitative description of the firing of power-stroke (PS) motor units and two kinds of coordinating interneurons, ASC(E) and DSC, recorded simultaneously from each swimmeret ganglion under uniform and nonuniform excitation. When anterior and posterior ganglia were excited differently, several parameters of the swimmeret motor pattern were affected. Strengths of PS bursts in each ganglion were determined by local excitation. The phase of PS bursts in neighboring ganglia changed at the excitation boundary. Coordinating neurons from the two ganglia closest to the excitation boundary were most affected by nonuniform excitation. ASC(E) neurons tracked the timing and duration of each PS burst in their home ganglion, but did not follow changes in PS burst strength. DSC neurons changed the duration, phase, and number of spikes per burst. We propose two models to explain these results. First, the period expressed under nonuniform conditions is the sum of local intersegmental latencies and these latencies are determined by local excitation. Second, the phase change at the excitation boundary is determined by local modulation of the targets of the intersegmental coordinating neurons, not by modulation of the coordinating neurons themselves.