Fronto-Central Theta Oscillations Are Related to Oscillations in Saccadic Response Times (SRT): An EEG and Behavioral Data Analysis

The phase reset hypothesis states that the phase of an ongoing neural oscillation, reflecting periodic fluctuations in neural activity between states of high and low excitability, can be shifted by the occurrence of a sensory stimulus so that the phase value become highly constant across trials (Schroeder et al., 2008). From EEG/MEG studies it has been hypothesized that coupled oscillatory activity in primary sensory cortices regulates multi sensory processing (Senkowski et al. 2008). We follow up on a study in which evidence of phase reset was found using a purely behavioral paradigm by including also EEG measures. In this paradigm, presentation of an auditory accessory stimulus was followed by a visual target with a stimulus-onset asynchrony (SOA) across a range from 0 to 404 ms in steps of 4 ms. This fine-grained stimulus presentation allowed us to do a spectral analysis on the mean SRT as a function of the SOA, which revealed distinct peak spectral components within a frequency range of 6 to 11 Hz with a modus of 7 Hz. The EEG analysis showed that the auditory stimulus caused a phase reset in 7-Hz brain oscillations in a widespread set of channels. Moreover, there was a significant difference in the average phase at which the visual target stimulus appeared between slow and fast SRT trials. This effect was evident in three different analyses, and occurred primarily in frontal and central electrodes.     

Pickpocket1 Is an Ionotropic Molecular Sensory Transducer

The molecular transformation of an external stimulus into changes in sensory neuron activity is incompletely described. Although a number of molecules have been identified that can respond to stimuli, evidence that these molecules can transduce stimulation into useful neural activity is lacking. Here we demonstrate that pickpocket1 (ppk1), a Drosophila homolog of mammalian Degenerin/epithelial sodium channels, encodes an acid-sensing sodium channel that conducts a transient depolarizing current in multidendritic sensory neurons of Drosophila melanogaster. Stimulation of Ppk1 is sufficient to bring these sensory neurons to threshold, eliciting a burst of action potentials. The transient nature of the neural activity produced by Ppk1 activation is the result of Ppk1 channel gating properties. This model is supported by the observation of enhanced bursting activity in neurons expressing a gain of function ppk1 mutant harboring the degenerin mutation. These findings demonstrate that Ppk1 can function as an ionotropic molecular sensory transducer capable of transforming the perception of a stimulus into phasic neuronal activity in sensory neurons.     

Influence of TRPV1 on diabetes-induced alterations in thermal pain sensitivity

Animals detect environmental changes through sensory neural mechanisms that enable them to differentiate the quality, intensity and temporal characteristics of stimuli. The 'doctrine of specific nervous energies' postulates that the different sensory modalities experienced by humans result of the activation of specific nervous pathways. Identification of functional classes of sensory receptors provided scientific support to the concept that somatosensory modalities (touch, pain, temperature, kinesthesis) are subserved by separate populations of sensory receptor neurons specialized in detecting innocuous and injurious stimuli of different quality (mechanical forces, temperature, chemical compounds). The identification of receptor proteins activated by different physicochemical stimuli, in particular ion channels of the Transient Receptor Potential (TRP) superfamily, has put forward the concept that specificity of peripheral sensory receptor neurons is determined by their expression of a particular "molecular sensor" that confers to each functional type its selectivity to respond with a discharge of nerve impulses to stimuli of a given quality. Nonetheless, recent experimental data suggest that the various molecular sensors proposed as specific transducer molecules for stimuli of different quality are not as neatly associated with the distinct functional types of sensory receptors as originally proposed. First, many ion channel molecules initially associated to the transduction of only one particular form of energy are also activated by stimuli of different quality, implying a limited degree of specificity in their transducing capacities. Second, molecular sensors associated with a stimulus quality and hence to a sensory receptor type and ultimately to a sensory modality may be concomitantly expressed in sensory receptor neurons functionally defined as specific for another stimulus quality. Finally, activation of voltage gated channels involved primarily in nerve impulse generation can also influence the gating of transducing channels, dramatically modifying their activation profile. Thus, we propose that the capacity exhibited by the different functional types of somatosensory receptor neurons to preferentially detect and encode specific stimuli into a discharge of nerve impulses, appears to result of a characteristic combinatorial expression of different ion channels in each neuronal type that finally determines their transduction and impulse firing properties. Transduction channels don't operate in isolation and their cellular context should also be taken into consideration to fully understand their function. Moreover, the inhomogeneous distribution of transduction and voltage-gated channels at soma, axonal branches and peripheral endings of primary sensory neurons influences the characteristics of the propagated impulse discharge that encodes the properties of the stimulus. Alteration of this concerted operation of ion channels in pathological conditions may underlie the changes in excitability accompanying peripheral sensory neuron injuries.     

A Functional Hypothesis for LGN-V1-TRN Connectivities Suggested by Computer Simulation

We employ computer simulation to investigate the function of neural circuitries between thalamic sensory relay nuclei, primary sensory cortices, and the thalamic reticular nucleus (TRN). Computational similarities exist between these circuits and the architecture of a simple artificial neural network. We impose processing parameters on this network architecture in keeping with anatomical and physiological details of the mammalian geniculo-cortical visual pathway, and then run the simulation on a task involving multiple simultaneous inputs from the simulated visual field. After two to three loops through the simulation, activity in cortical and thalamic units whose receptive fields include the stronger stimulus remains constant, while activity in other cortical and thalamic units activated by weaker stimuli declines toward resting values. These results suggest that the modeled neural circuitry functions to prime selective attentional mechanisms further up the visual streams toward specific portions of the total visual stimulus. Besides extending existing models and evidence about the function of these neural circuits, our results also provide physiologists with predicted activity profiles of thalamic and cortical elements of the modeled neural system for a task not yet studied experimentally.     

Different effects of blocked potassium channels on action potentials, accommodation, adaptation and anode break excitation in human motor and sensory myelinated nerve fibres: computer simulations

 Action potentials and electrotonic responses to 300-ms depolarizing and hyperpolarizing currents for human motor and sensory myelinated nerve fibres have been simulated on the basis of double cable models. The effects of blocked nodal or internodal potassium (fast or slow) channels on the fibre action potentials, early and late adaptations to 30-ms suprathreshold slowly increasing depolarizing stimuli have been examined. The effects of the same channels on accommodation after the termination of a prolonged (100 ms) hyperpolarizing current pulse have also been investigated. By removing the nodal fast potassium conductance the action potentials of the sensory fibres are considerably broader than those of the motor neurons. For both types of fibres, the blocked nodal slow potassium channels have a substantially smaller effect on the action potential repolarization. When the suprathreshold depolarizing current intensity is increased, the onset of the spike burst occurs sooner, which is common in the behaviour of the fibres. The most striking differences in the burst activity during early adaptation have been found between the fibres when the nodal fast potassium channels are blocked. The results obtained confirm the fact that the motor fibres adapt more quickly to sustained depolarizing current pulses than the sensory ones. The results also show that normal human motor and sensory fibres cannot be excited by a 100-ms hyperpolarizing current pulse, even at the threshold level. When removing the potassium channels in the nodal or internodal axolemma, the posthyperpolarization increase in excitability is small, which is common in the behaviour of the fibres. However, anode break excitation can be simulated in the fibres with simultaneous removal of the potassium channels under the myelin sheath, and this is more pronounced in the human sensory fibres than in motor fibres. This phenomenon can also be found when the internodal and some of the nodal (fast or slow) potassium channels are simultaneously blocked. Received: 8 November 1999 / Accepted in revised form: 29 February 2000     

Boring's formulation: a scheme for identifying functional neuron groups in a sensory system

In 1935 Edwin Boring proposed that each attribute of sensation reflects the activity of a different neural circuit. If this idea is valid, it could facilitate both psychophysical and neurophysiological research on sensory systems. We think it likely that Boring's formulation is correct for three reasons: 1) Different sensory attributes reflect conscious information about different parameters of a stimulus. To be measured by any device, each of these parameters must be individually computed. Different neural circuits would appear to be necessary for the nervous system to carry out these different computations. 2) Perceived information about different sensory attributes can be made to diverge by appropriate manipulations of the stimuli. If there is a rigorous relationship between conscious sensory experience and neural activity, such a divergence implies that different sensory attributes are served by different neural circuits. 3) Accurate information about a sensory attribute requires that a human observer's attention be focused on that attribute. Changes in direction of attention are thought to involve a process of switching from one neural circuit to another, and provide another way to cause perceived information about different sensory attributes to diverge.     

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.     

Recovering the contents of consciousness in the noise of neuroimaging

A classical approach in the neurosciences is to study neural activity modulations induced by a stimulus, a task, etc. This approach is anchored in a behaviourist culture and has proven informative within certain limits. The present paper shows that this approach nonetheless neglects aspects of neural activity that can also contribute important information about brain function. Over the last years, the contributions with the strongest impact on progress in cognitive neuroscience have used other approaches that exploit a spatial or temporal variability of neural activity that standard analyses consider as noise and hence do not take into account. By applying multi-variate analyses, spatial variability of evoked responses has permitted decoding sensory and cognitive representations in the brain. Temporal variability of ongoing neural activity influences how stimuli are perceived trial by trial as well as the associated evoked responses which points out the importance of spontaneous brain activity for cognition. We describe these two kind of approaches based on experiments using functional neuroimaging but the conclusions generalize to other techniques applied in the neurosciences.     

Stronger Neural Modulation by Visual Motion Intensity in Autism Spectrum Disorders

Theories of autism spectrum disorders (ASD) have focused on altered perceptual integration of sensory features as a possible core deficit. Yet, there is little understanding of the neuronal processing of elementary sensory features in ASD. For typically developed individuals, we previously established a direct link between frequency-specific neural activity and the intensity of a specific sensory feature: Gamma-band activity in the visual cortex increased approximately linearly with the strength of visual motion. Using magnetoencephalography (MEG), we investigated whether in individuals with ASD neural activity reflect the coherence, and thus intensity, of visual motion in a similar fashion. Thirteen adult participants with ASD and 14 control participants performed a motion direction discrimination task with increasing levels of motion coherence. A polynomial regression analysis revealed that gamma-band power increased significantly stronger with motion coherence in ASD compared to controls, suggesting excessive visual activation with increasing stimulus intensity originating from motion-responsive visual areas V3, V6 and hMT/V5. Enhanced neural responses with increasing stimulus intensity suggest an enhanced response gain in ASD. Response gain is controlled by excitatory-inhibitory interactions, which also drive high-frequency oscillations in the gamma-band. Thus, our data suggest that a disturbed excitatory-inhibitory balance underlies enhanced neural responses to coherent motion in ASD.     

Power and phase properties of oscillatory neural responses in the presence of background activity

Natural sensory inputs, such as speech and music, are often rhythmic. Recent studies have consistently demonstrated that these rhythmic stimuli cause the phase of oscillatory, i.e. rhythmic, neural activity, recorded as local field potential (LFP), electroencephalography (EEG) or magnetoencephalography (MEG), to synchronize with the stimulus. This phase synchronization, when not accompanied by any increase of response power, has been hypothesized to be the result of phase resetting of ongoing, spontaneous, neural oscillations measurable by LFP, EEG, or MEG. In this article, however, we argue that this same phenomenon can be easily explained without any phase resetting, and where the stimulus-synchronized activity is generated independently of background neural oscillations. It is demonstrated with a simple (but general) stochastic model that, purely due to statistical properties, phase synchronization, as measured by ‘inter-trial phase coherence’, is much more sensitive to stimulus-synchronized neural activity than is power. These results question the usefulness of analyzing the power and phase of stimulus-synchronized activity as separate and complementary measures; particularly in the case of attempting to demonstrate whether stimulus-synchronized neural activity is generated by phase resetting of ongoing neural oscillations.     

Shifting baselines in attention research

Psychophysical and physiological studies have shown that attending to a stimulus can enhance its sensory processing. Functional imaging studies now reveal that attention can also modulate activity in sensory brain areas before stimulus onset, when the observer prepares to attend to an anticipated stimulus. These preparatory 'baseline shifts' in brain activity pose many new questions, and potentially offer new insights into the neural basis of perceptual awareness.     

Saccadic Reaction Times to Audiovisual Stimuli Show Effects of Oscillatory Phase Reset

Initiating an eye movement towards a suddenly appearing visual target is faster when an accessory auditory stimulus occurs in close spatiotemporal vicinity. Such facilitation of saccadic reaction time (SRT) is well-documented, but the exact neural mechanisms underlying the crossmodal effect remain to be elucidated. From EEG/MEG studies it has been hypothesized that coupled oscillatory activity in primary sensory cortices regulates multisensory processing. Specifically, it is assumed that the phase of an ongoing neural oscillation is shifted due to the occurrence of a sensory stimulus so that, across trials, phase values become highly consistent (phase reset). If one can identify the phase an oscillation is reset to, it is possible to predict when temporal windows of high and low excitability will occur. However, in behavioral experiments the pre-stimulus phase will be different on successive repetitions of the experimental trial, and average performance over many trials will show no signs of the modulation. Here we circumvent this problem by repeatedly presenting an auditory accessory stimulus followed by a visual target stimulus with a temporal delay varied in steps of 2 ms. Performing a discrete time series analysis on SRT as a function of the delay, we provide statistical evidence for the existence of distinct peak spectral components in the power spectrum. These frequencies, although varying across participants, fall within the beta and gamma range (20 to 40 Hz) of neural oscillatory activity observed in neurophysiological studies of multisensory integration. Some evidence for high-theta/alpha activity was found as well. Our results are consistent with the phase reset hypothesis and demonstrate that it is amenable to testing by purely psychophysical methods. Thus, any theory of multisensory processes that connects specific brain states with patterns of saccadic responses should be able to account for traces of oscillatory activity in observable behavior.     

Density dependent neurodynamics

The dynamics of a neural network depends on density parameters at (at least) two different levels: the subcellular density of ion channels in single neurons, and the density of cells and synapses at a network level. For the Frankenhaeuser-Huxley (FH) neural model, the density of sodium (Na) and potassium (K) channels determines the behaviour of a single neuron when exposed to an external stimulus. The features of the onset of single neuron oscillations vary qualitatively among different regions in the channel density plane. At a network level, the density of neurons is reflected in the global connectivity. We study the relation between the two density levels in a network of oscillatory FH neurons, by qualitatively distinguishing between three regions, where the mean network activity is (1) spiking, (2) oscillating with enveloped frequencies, and (3) bursting, respectively. We demonstrate that the global activity can be shifted between regions by changing either the density of ion channels at the subcellular level, or the connectivity at the network level, suggesting that different underlying mechanisms can explain similar global phenomena. Finally, we model a possible effect of anaesthesia by blocking specific inhibitory ion channels.     

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.     

Fitting of dynamic recurrent neural network models to sensory stimulus-response data

We present a theoretical study aiming at model fitting for sensory neurons. Conventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a smooth time-dependent variable, the associated response will be a set of neural spike timings (roughly the instants of successive action potential peaks) that have no amplitude information. A recurrent neural network model can be fitted to such a stimulus-response data pair by using the maximum likelihood estimation method where the likelihood function is derived from Poisson statistics of neural spiking. The universal approximation feature of the recurrent dynamical neuron network models allows us to describe excitatory-inhibitory characteristics of an actual sensory neural network with any desired number of neurons. The stimulus data are generated by a phased cosine Fourier series having a fixed amplitude and frequency but a randomly shot phase. Various values of amplitude, stimulus component size, and sample size are applied in order to examine the effect of the stimulus to the identification process. Results are presented in tabular and graphical forms at the end of this text. In addition, to demonstrate the success of this research, a study involving the same model, nominal parameters and stimulus structure, and another study that works on different models are compared to that of this research.     

Some aspects of the physiological role of ion channels in the nervous system

Recent analyses of the genomes of several animal species, including man, have revealed that a large number of ion channels are present in the nervous system. Our understanding of the physiological role of these channels in the nervous system has followed the evolution of biophysical techniques during the last century. The observation and the quantification of the electrical events associated with the operation of the ionic channels has been, and still is, one of the best tools to analyse the various aspects of their contribution to nerve function. For this reason, we have chosen to use electrophysiological recordings to illustrate some of the main functions of these channels. The properties and the roles of Na+ and K+ channels in neuronal resting and action potentials are illustrated in the case of the giant axons of the squid and the cockroach. The nature and role of the calcium currents in the bursting behaviour of the neurons are illustrated for Aplysia giant neurons. The relationship between presynaptic calcium currents and synaptic transmission is shown for the squid giant synapse. The involvement of calcium channels in survival and neurite outgrowth of cultured neurons is exemplified using embryonic cockroach brain neurons. This same neuronal preparation is used to illustrate ion channel noise and single-channel events associated with the binding of agonists to nicotinic receptors. Some features of the synaptic activity in the central nervous system are shown, with examples from the cercal nerve giant-axon preparation of the cockroach. The interplay of different ion conductances involved in the oscillatory behaviour of the Xenopus spinal motoneurons is illustrated and discussed. The last part of this review deals with ionic homeostasis in the brain and the function of glial cells, with examples from Necturus and squids.     

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain-body- environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour.     

Nonmonotonic noise tuning of BOLD fMRI signal to natural images in the visual cortex of the anesthetized monkey

BACKGROUND: The perceptual ability of humans and monkeys to identify objects in the presence of noise varies systematically and monotonically as a function of how much noise is introduced to the visual display. That is, it becomes more and more difficult to identify an object with increasing noise. Here we examine whether the blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) signal in anesthetized monkeys also shows such monotonic tuning. We employed parametric stimulus sets containing natural images and noise patterns matched for spatial frequency and intensity as well as intermediate images generated by interpolation between natural images and noise patterns. Anesthetized monkeys provide us with the unique opportunity to examine visual processing largely in the absence of top-down cognitive modulations and can thus provide an important baseline against which work with awake monkeys and humans can be compared. RESULTS: We measured BOLD activity in occipital visual cortical areas as natural images and noise patterns, as well as intermediate interpolated patterns at three interpolation levels (25%, 50%, and 75%) were presented to anesthetized monkeys in a block paradigm. We observed reliable visual activity in occipital visual areas including V1, V2, V3, V3A, and V4 as well as the fundus and anterior bank of the superior temporal sulcus (STS). Natural images consistently elicited higher BOLD levels than noise patterns. For intermediate images, however, we did not observe monotonic tuning. Instead, we observed a characteristic V-shaped noise-tuning function in primary and extrastriate visual areas. BOLD signals initially decreased as noise was added to the stimulus but then increased again as the pure noise pattern was approached. We present a simple model based on the number of activated neurons and the strength of activation per neuron that can account for these results. CONCLUSIONS: We show that, for our parametric stimulus set, BOLD activity varied nonmonotonically as a function of how much noise was added to the visual stimuli, unlike the perceptual ability of humans and monkeys to identify such stimuli. This raises important caveats for interpreting fMRI data and demonstrates the importance of assessing not only which neural populations are activated by contrasting conditions during an fMRI study, but also the strength of this activation. This becomes particularly important when using the BOLD signal to make inferences about the relationship between neural activity and behavior.     

Encoding of Time-varying Stimuli in Populations of Cultured Neurons

We wondered whether random populations of dissociated cultured cortical neurons, despite of their lack of structure and/or regional specialization, are capable of modulating their neural activity as the effect of a time-varying stimulation – a simulated ‘sensory’ afference. More specifically, we used localized low-frequency, non-periodic trains of stimuli to simulate sensory afferences, and asked how much information about the original trains of stimuli could be extracted from the neural activity recorded at the different sites. Furthermore, motivated by the results of studies performed both in vivo and in vitro on different preparations, which suggested that isolated spikes and bursts may play different roles in coding time-varying signals, we explored the amount of such ‘sensory’ information that could be associated to these different firing modes. Finally, we asked whether and how such ‘sensory’ information is transferred from the sites of stimulation (i.e., the ‘sensory’ areas), to the other regions of the neural populations. To do this we applied stimulus reconstruction techniques and information theoretic concepts that are typically used to investigate neural coding in sensory systems. Our main results are that (1) slow variations of the rate of stimulation are coded into isolated spikes and in the time of occurrence of bursts (but not in the bursts’ temporal structure); (2) increasing the rate of stimulation has the effect of increasing the proportion of isolated spikes in the average evoked response and their importance in coding for the stimuli; and, (3) the ability to recover the time course of the pattern of stimulation is strongly related to the degree of functional connectivity between stimulation and recording sites. These observations parallel similar findings in intact nervous systems regarding the complementary roles of bursts and tonic spikes in encoding sensory information. Our results also have interesting implications in the field of neuro-robotic interfaces. In fact, the ability of populations of neurons to code information is a prerequisite for obtaining hybrid systems, in which neuronal populations are used to control external devices.     

A computer model for describing the effect of urethral afferents on simulated lower urinary tract function

A computer model of mechanical properties of the bladder, the urethra and the rhabdosphincter, as well as their neural control is presented in this paper. The model has a rather simple design and processes sensory information from both the bladder wall tension and urethral stretch. It is assumed that afferent signals from the urethra are involved in a sacral excitatory reflex and a supraspinal inhibitory reflex. Pressure and flow signals that resemble experimentally measured normal human behaviour could be simulated with this model. From these simulations the relation between the neural control mechanisms used in the model and the neural control mechanism in vivo cannot be judged entirely because similar behaviour could be simulated with models that are bas ed on different neural control mechanisms. Also behaviour that resembles detrusor overactivity was simulated with our model after an externally induced rise in detrusor pressure was added. Detrusor overactivity, sometimes in combination with urethral relaxation, can occur during a urodynamic investigation. A possible explanation for this detrusor overactivity might be that the micturition reflex is triggered by unknown disturbances and is inhibited immediately after by the same mechanism that normally ceases voiding. The described model provides such a mechanism. Based on these simulations, therefore, it is concluded that urethral afferent signals might be important in lower urinary tract control.