Axon shape as a basis for multinode functional units in a hierarchical neural model

The ability of animals to perform fixed action patterns and to access information by categories suggests that there are several types of hierarchical organization in the nervous system. This paper employs data about axon shape and neurotransmitter effect to demonstrate the emergence of hierarchical structure in a neural model. Two dimensions of neural classification, axon shape and neurotransmitter effect, are used to generate a five-node-type neural model. Neurons are classified as interneurons, relay cells, and monoamine transmitters on the basis of axon shape; the transmitter classifications include excitatory, inhibitory, and parameter-changing. The five types of nodes in the model correspond to all the biologically observed combinations: excitatory and inhibitory short-range, excitatory and inhibitory long-range-directional, and long-lasting long-range-diffuse nodes. The emergence of multinode functional units (MFUs) from the five-node-type model is mathematically demonstrated. These units correspond to cortical columns anatomically defined by the axon fields of relay cells, and are called columnar multinode functional units (CMFUs). CMFUs may, in turn, be part of larger functional groups designated coherent populations, which consist of widely distributed CMFUs in retinotopically equivalent locations. The existence of coherent populations imposes a three-level hierarchical structure on the model. To represent this hierarchical structure, a new type of CMFU node, which has a set of vector-valued inputs and outputs, is introduced. Each CMFU node contains a system of short-range nodes which supplies it with vector-valued inputs. Sets of long-range-diffuse nodes are also treated as vector-valued nodes whose outputs control the size and number of coherent populations. The role of coherent populations and hierarchical organization in the nervous system is discussed for such cognitive tasks as visual perception, attention and learning. Physiological and behavioral evidence are cited which support the existence of a similar three-level hierarchy in vertebrate brains.     

Influence of glutamate and GABA transport on brain excitatory/inhibitory balance

An optimally functional brain requires both excitatory and inhibitory inputs that are regulated and balanced. A perturbation in the excitatory/inhibitory balance—as is the case in some neurological disorders/diseases (e.g. traumatic brain injury Alzheimer’s disease, stroke, epilepsy and substance abuse) and disorders of development (e.g. schizophrenia, Rhett syndrome and autism spectrum disorder)—leads to dysfunctional signaling, which can result in impaired cognitive and motor function, if not frank neuronal injury. At the cellular level, transmission of glutamate and GABA, the principle excitatory and inhibitory neurotransmitters in the central nervous system control excitatory/inhibitory balance. Herein, we review the synthesis, release, and signaling of GABA and glutamate followed by a focused discussion on the importance of their transport systems to the maintenance of excitatory/inhibitory balance.     

Inhibitory neurons exhibit high controlling ability in the cortical microconnectome

The brain is a network system in which excitatory and inhibitory neurons keep activity balanced in the highly non-random connectivity pattern of the microconnectome. It is well known that the relative percentage of inhibitory neurons is much smaller than excitatory neurons in the cortex. So, in general, how inhibitory neurons can keep the balance with the surrounding excitatory neurons is an important question. There is much accumulated knowledge about this fundamental question. This study quantitatively evaluated the relatively higher functional contribution of inhibitory neurons in terms of not only properties of individual neurons, such as firing rate, but also in terms of topological mechanisms and controlling ability on other excitatory neurons. We combined simultaneous electrical recording (~2.5 hours) of ~1000 neurons in vitro, and quantitative evaluation of neuronal interactions including excitatory-inhibitory categorization. This study accurately defined recording brain anatomical targets, such as brain regions and cortical layers, by inter-referring MRI and immunostaining recordings. The interaction networks enabled us to quantify topological influence of individual neurons, in terms of controlling ability to other neurons. Especially, the result indicated that highly influential inhibitory neurons show higher controlling ability of other neurons than excitatory neurons, and are relatively often distributed in deeper layers of the cortex. Furthermore, the neurons having high controlling ability are more effectively limited in number than central nodes of k-cores, and these neurons also participate in more clustered motifs. In summary, this study suggested that the high controlling ability of inhibitory neurons is a key mechanism to keep balance with a large number of other excitatory neurons beyond simple higher firing rate. Application of the selection method of limited important neurons would be also applicable for the ability to effectively and selectively stimulate E/I imbalanced disease states.     

Dynamics of the seizure states caused by the action of ligands of the inhibitory transmitter system, as a consequence of evolution of stochastic neuronal populations

A probabilistic model describing functioning of the elements of a neuronal population is suggested. The model includes subsystems: synaptically interconnected inhibitory and excitatory neurons. The probability of the occurrence of the intercellular signal at varying levels of the endogenous transmitter and the effects of exogenous ligands (agonists, antagonists, etc.) of the inhibitory (GABA_A) transmitter system is considered. Comparison of the computer simulation with the experimental data showed that their paroxysmal EEG activity recorded after applications of strychnine, corazol, bicuculline, or picrotoxin to the neocortical surface, as well as during seizures following intravenous administrations of these drugs, is characterized by a consequent mild and rigid loss of stability, followed by a decrease in the share of active neurons in the population. The reason for these phenomena is a periodical asynchronous decrease in the efficiency of excitatory and inhibitory intercellular signals in the neuronal population.     

Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.     

Population rate coding in recurrent neuronal networks with unreliable synapses

Neuron transmits spikes to postsynaptic neurons through synapses. Experimental observations indicated that the communication between neurons is unreliable. However most modelling and computational studies considered deterministic synaptic interaction model. In this paper, we investigate the population rate coding in an all-to-all coupled recurrent neuronal network consisting of both excitatory and inhibitory neurons connected with unreliable synapses. We use a stochastic on-off process to model the unreliable synaptic transmission. We find that synapses with suitable successful transmission probability can enhance the encoding performance in the case of weak noise; while in the case of strong noise, the synaptic interactions reduce the encoding performance. We also show that several important synaptic parameters, such as the excitatory synaptic strength, the relative strength of inhibitory and excitatory synapses, as well as the synaptic time constant, have significant effects on the performance of the population rate coding. Further simulations indicate that the encoding dynamics of our considered network cannot be simply determined by the average amount of received neurotransmitter for each neuron in a time instant. Moreover, we compare our results with those obtained in the corresponding random neuronal networks. Our numerical results demonstrate that the network randomness has the similar qualitative effect as the synaptic unreliability but not completely equivalent in quantity.     

Amino acid pharmacology in a mature rat spinal cord preparation in vitro

1. It has been shown that a relatively large intact piece of mature mammalian spinal cord can be maintained in vitro if suitable experimental conditions are employed. 2. The preparation as described gives robust and reproduceable reflex responses in both dorsal and ventral horns (ventral horn activity has been maintained for over 36 hr in vitro). 3. The action of antagonists at both excitatory and inhibitory amino acid receptors give qualitatively predictable results from known in vivo experiments with the added advantage of fine quantitative control. 4. A significant advantage of such an adult preparation over more common immature spinal cord preparation is the ability to clearly separate low threshold reflexes, such as those described in this article, from reflexes evoked by small diameter non-myelinated afferents.     

Effects of phase on homeostatic spike rates

Recent experimental results by Talathi et al. (Neurosci Lett 455:145–149, 2009) showed a divergence in the spike rates of two types of population spike events, representing the putative activity of the excitatory and inhibitory neurons in the CA1 area of an animal model for temporal lobe epilepsy. The divergence in the spike rate was accompanied by a shift in the phase of oscillations between these spike rates leading to a spontaneous epileptic seizure. In this study, we propose a model of homeostatic synaptic plasticity which assumes that the target spike rate of populations of excitatory and inhibitory neurons in the brain is a function of the phase difference between the excitatory and inhibitory spike rates. With this model of homeostatic synaptic plasticity, we are able to simulate the spike rate dynamics seen experimentally by Talathi et al. in a large network of interacting excitatory and inhibitory neurons using two different spiking neuron models. A drift analysis of the spike rates resulting from the homeostatic synaptic plasticity update rule allowed us to determine the type of synapse that may be primarily involved in the spike rate imbalance in the experimental observation by Talathi et al. We find excitatory neurons, particularly those in which the excitatory neuron is presynaptic, have the most influence in producing the diverging spike rates and causing the spike rates to be anti-phase. Our analysis suggests that the excitatory neuronal population, more specifically the excitatory to excitatory synaptic connections, could be implicated in a methodology designed to control epileptic seizures.     

Synchronization and sensitivity enhancement of the Hodgkin-Huxley neurons due to inhibitory inputs

Recent experimental results imply that inhibitory postsynaptic potentials can play a functional role in realizing synchronization of neuronal firing in the brain. In order to examine the relation between inhibition and synchronous firing of neurons theoretically, we analyze possible effects of synchronization and sensitivity enhancement caused by inhibitory inputs to neurons with a biologically realistic model of the Hodgkin-Huxley equations. The result shows that, after an inhibitory spike, the firing probability of a single postsynaptic neuron exposed to random excitatory background activity oscillates with time. The oscillation of the firing probability can be related to synchronous firing of neurons receiving an inhibitory spike simultaneously. Further, we show that when an inhibitory spike input precedes an excitatory spike input, the presence of such preceding inhibition raises the firing probability peak of the neuron after the excitatory input. The result indicates that an inhibitory spike input can enhance the sensitivity of the postsynaptic neuron to the following excitatory spike input. Two neural network models based on these effects on postsynaptic neurons caused by inhibitory inputs are proposed to demonstrate possible mechanisms of detecting particular spatiotemporal spike patterns. Received: 15 April 1999 /Accepted in revised form: 25 November 1999     

Propriospinal neurones as a relay system for transmission of cortico-spinal influences.

1. Synaptic organization and transmission have been studied in the lateral group of short propriospinal neurones of the lumbar and cervical regions of the cat spinal cord. Special attention was paid to their role in the transmission of cortico-spinal volleys. 2. The majority of these neurones are mono- or oligosynaptically excited after pyramidal tract stimulation. Convergence of excitatory actions from rubrospinal and lateral reticulospinal tracts was typical for these cells. Neurones with relatively low-level and delayed effects from segmental afferents are frequent in this population. 3. Temporal summation is important for the transmission of descending vlleys through these neurones. Mutual excitatory and recurrent inhibitory connections are supposed to play a substantial role in their function. 4. Possible participation of the short lateral propriospinal system in the transmission, transformation and re-distribution of corticofugal signals to the segmental spinal mechanisms is discussed.     

Computer simulation of shared input among projection neurons in the dorsal cochlear nucleus

Computer simulations of a network model of an isofrequency patch of the dorsal cochlear nucleus (DCN) were run to explore possible mechanisms for the level-dependent features observed in the cross-correlograms of pairs of type IV units in the cat and nominal type IV units in the gerbil DCN. The computer model is based on the conceptual model (of a cat) that suggests two sources of shared input to DCN's projection neurons (type IV units): excitatory input from auditory nerves and inhibitory input from interneurons (type II units). Use of tonal stimuli is thought to cause competition between these sources resulting in the decorrelation of type IV unit activities at low levels. In the model, P-cells (projection neurons), representing type IV units, receive inhibitory input from I-cells (interneurons), representing type II units. Both sets of model neurons receive a simulated excitatory auditory nerve (AN) input from same-CF AN fibers, where the AN input is modeled as a dead-time modified Poisson process whose intensity is given by a computationally tractable discharge rate versus sound pressure level function. Subthreshold behavior of each model neuron is governed by a set of normalized state equations. The computer model has previously been shown to reproduce the major response properties of both type IV and type II units (e.g., rate-level curves and peri-stimulus time histograms) and the level-dependence of the functional type II-type IV inhibitory interaction. This model is adapted for the gerbil by simulating a reduced population of I-cells. Simulations were carried out for several auditory nerve input levels, and cross-correlograms were computed from the activities of pairs of P-cells for a complete (cat model) and reduced (gerbil model) population of I-cells. The resultant correlograms show central mounds (CMs), indicative of either shared excitatory or inhibitory input, for both spontaneous and tone-evoked driven activities. Similar to experimental results, CM amplitudes are a non-monotonic function of level and CM widths decrease as a function of level. These results are consistent with the hypothesis that shared excitatory input correlates the spontaneous activities of type IV units and shared inhibitory input correlates their driven activities. The results also suggest that the decorrelation of the activities of type IV units can result from a reduced effectiveness of the AN input as a function of increasing level. Thus, competition between the excitatory and inhibitory inputs is not required.     

Coupled Flip-Flop Model for REM Sleep Regulation in the Rat

Recent experimental studies investigating the neuronal regulation of rapid eye movement (REM) sleep have identified mutually inhibitory synaptic projections among REM sleep-promoting (REM-on) and REM sleep-inhibiting (REM-off) neuronal populations that act to maintain the REM sleep state and control its onset and offset. The control mechanism of mutually inhibitory synaptic interactions mirrors the proposed flip-flop switch for sleep-wake regulation consisting of mutually inhibitory synaptic projections between wake- and sleep-promoting neuronal populations. While a number of synaptic projections have been identified between these REM-on/REM-off populations and wake/sleep-promoting populations, the specific interactions that govern behavioral state transitions have not been completely determined. Using a minimal mathematical model, we investigated behavioral state transition dynamics dictated by a system of coupled flip-flops, one to control transitions between wake and sleep states, and another to control transitions into and out of REM sleep. The model describes the neurotransmitter-mediated inhibitory interactions between a wake- and sleep-promoting population, and between a REM-on and REM-off population. We proposed interactions between the wake/sleep and REM-on/REM-off flip-flops to replicate the behavioral state statistics and probabilities of behavioral state transitions measured from experimental recordings of rat sleep under ad libitum conditions and after 24 h of REM sleep deprivation. Reliable transitions from REM sleep to wake, as dictated by the data, indicated the necessity of an excitatory projection from the REM-on population to the wake-promoting population. To replicate the increase in REM-wake-REM transitions observed after 24 h REM sleep deprivation required that this excitatory projection promote transient activation of the wake-promoting population. Obtaining the reliable wake-nonREM sleep transitions observed in the data required that activity of the wake-promoting population modulated the interaction between the REM-on and REM-off populations. This analysis suggests neuronal processes to be targeted in further experimental studies of the regulatory mechanisms of REM sleep.     

Numerical bifurcation analysis of distance-dependent on-center off-surround shunting neural networks

 On-center off-surround shunting neural networks are often applied as models for content-addressable memory (CAM), the equilibria being the stored memories. One important demand of biological plausible CAMs is that they function under a broad range of parameters, since several parameters vary due to postnatal maturation or learning. Ellias, Cohen and Grossberg have put much effort into showing the stability properties of several configurations of on-center off-surround shunting neural networks. In this article we present numerical bifurcation analysis of distance-dependent on-center off-surround shunting neural networks with fixed external input. We varied four parameters that may be subject to postnatal maturation: the range of both excitatory and inhibitory connections and the strength of both inhibitory and excitatory connections. These analyses show that fold bifurcations occur in the equilibrium behavior of the network by variation of all four parameters. The most important result is that the number of activation peaks in the equilibrium behavior varies from one to many if the range of inhibitory connections is decreased. Moreover, under a broad range of the parameters the stability of the network is maintained. The examined network is implemented in an ART network, Exact ART, where it functions as the classification layer F2. The stability of the ART network with the F2-field in different dynamic regimes is maintained and the behavior is functional in Exact ART. Through a bifurcation the learning behavior of Exact ART may even change from forming local representations to forming distributed representations. Received: 23 January 1996 / Accepted in revised form: 1 July 1996     

Computational Modeling of Seizure Dynamics Using Coupled Neuronal Networks: Factors Shaping Epileptiform Activity

Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology.     

Inhibitory systems of the spinal cord in the control of interactions of functionally coupled muscles

Studies on muscle functional coupling mechanisms have detected excitatory and inhibitory reflex systems differing in their organization at the spinal level. The classic notions on inhibitory interactions between antagonists at the motoneuronal and premotoneuronal levels are supplemented with data on excitatory interactions. Mutual excitation of synergist muscles is also not the only possible type of interaction: inhibitory relationships are found in the system of synergists under certain physiological conditions. This organization of the motor centers of antagonists and synergists enhances the possibilities for fine coordination of their activity, inhibition and disinhibition mechanisms playing an important role in this system. Data on the inhibitory systems of the spinal cord, in particular, the inhibitory interactions between functionally coupled muscles, are reviewed.     

Combined eco-morphological functional groups are reliable indicators of colonisation processes of benthic diatom assemblages in a lowland stream

Classifying benthic diatom taxa based on ecological and morphological features became increasingly important in recent years due to the demand of understanding the dynamics and functioning of diatom assemblages. The great potential in using these functional classifications in diatom ecology involves further refinement of current classification. In our experimental study, colonisation processes of diatom assemblages were studied in a typical small lowland stream, using both diatom guilds and cell size categories. We also tested newly proposed combined eco-morphological functional groups (ecological guilds combined with cell size categories) in the study of the colonisation process in benthic diatom assemblages. We hypothesised that (i) there is a decrease in the proportion of low profile guild, while an increase in that of high profile and motile guilds in time with the decreasing rate of physical disturbance; (ii) the presence of small size categories will be pronounced at the beginning of the colonisation processes, while proportion of larger size categories will be higher in the latter phases of colonisation; and (iii) the relationship between taxa and environmental factors are better reflected by the use of combined eco-morphological functional groups than by the sole analyses of rough guilds or cell size categories. The first hypothesis was not confirmed, and our second hypothesis was only partially confirmed by the results. We found that the relationship between environmental factors and guilds, as well as cell size categories was not appropriate to reveal the relationship between abiotic factors and taxa composition. In contrast we found that compositional changes in colonisation were appropriately reflected by the newly defined combined eco-morphological functional groups. In the combined eco-morphological functional groups, such kind of taxonomical and ecological features can be prevailed which are hidden in guilds or cell size categories separately. Thus these combined eco-morphological functional groups could help to come one step closer to develop a widely used ecological classification in diatom researches.     

Learning-Dependent Plasticity of the Barrel Cortex Is Impaired by Restricting GABA-Ergic Transmission

Experience-induced plastic changes in the cerebral cortex are accompanied by alterations in excitatory and inhibitory transmission. Increased excitatory drive, necessary for plasticity, precedes the occurrence of plastic change, while decreased inhibitory signaling often facilitates plasticity. However, an increase of inhibitory interactions was noted in some instances of experience-dependent changes. We previously reported an increase in the number of inhibitory markers in the barrel cortex of mice after fear conditioning engaging vibrissae, observed concurrently with enlargement of the cortical representational area of the row of vibrissae receiving conditioned stimulus (CS). We also observed that an increase of GABA level accompanied the conditioning. Here, to find whether unaltered GABAergic signaling is necessary for learning-dependent rewiring in the murine barrel cortex, we locally decreased GABA production in the barrel cortex or reduced transmission through GABAA receptors (GABAARs) at the time of the conditioning. Injections of 3-mercaptopropionic acid (3-MPA), an inhibitor of glutamic acid decarboxylase (GAD), into the barrel cortex prevented learning-induced enlargement of the conditioned vibrissae representation. A similar effect was observed after injection of gabazine, an antagonist of GABAARs. At the behavioral level, consistent conditioned response (cessation of head movements in response to CS) was impaired. These results show that appropriate functioning of the GABAergic system is required for both manifestation of functional cortical representation plasticity and for the development of a conditioned response.     

An extended first approximation model for the amygdaloid kindling phenomenon

A first approximation model, which accounts for the strongest phenomena defining kindling is suggested. It is based on an excitatory-inhibitory coupling of neural aggregates, to which a self-stimulation element for the excitatory aggregate was added. The functional linking hypothesis views the representation of kindling as a process of gradual transition through structural changes from a stable system to a system showing stability for small perturbations and an oscillatory orbit for larger perturbations, to a purely oscillatory system. The anatomical linking hypothesis views the excitatory aggregate as representing the hypothalamus, the inhibitory aggregate as representing the hippocampal-septal-preoptic complex, and the selfstimulating element of the excitatory aggregate as representing the amygdaloid-pyriform complex. The model was realized on a digital computer with graphic capabilities and showed good qualitative agreement with the experimental data related to kindling. In addition, the use of the model for generating new experiments is discussed.     

Effects of exposure to 30 kV/m, 60-Hz electric fields on the social behavior of baboons

We tested the hypothesis that exposure to a 30-kV/m, 60-Hz electric field produces significant change (stress) in the social behavior of adult male baboons (Papio cynocephalus anubis). One group of eight baboons was exposed to an electric field (12 hours per day, 7 days per week for 6 weeks) while a second group of eight baboons was maintained in a sham-exposure (control) condition. Exposed subjects and control subjects were compared over three, six-week experimental periods (pre-exposure, exposure, and post-exposure). Performance rates of six categories of social behaviors (passive affinity, active affinity, approach, tension, threat, and attack) and four categories of nonsocial behaviors (forage, manipulate, posture, and stereotypy) were used to compare the two groups. The results of our study indicate that 1) there were no significant differences between the two groups during the pre-exposure or post-exposure periods; 2) during the exposure period, experimental and control groups exhibited statistically significant differences in the mean performance rates of three behavior categories; 3) within-group comparisons across periods indicate that the experimentally exposed group exhibited statistically significant changes in passive affinity, tension, and stereotypy; and 4) changes in behavior performance among the exposed subjects reflect a stress response to the electric field.     

Modeling inhibition of type II units in the dorsal cochlear nucleus

 Type II units in the dorsal cochlear nucleus (DCN) are characterized by vigorous but nonmonotonic responses to best frequency tones as a function of sound pressure level, and relatively weak responses to noise. A model of DCN neural circuitry was used to explore two hypothetical mechanisms by which neurons may be endowed with type II unit response properties. Both mechanisms assume that type II units receive excitatory input from auditory nerve (AN) fibers and inhibitory input from an unspecified class of cochlear nucleus interneurons that also receive excitatory AN input. The first mechanism, a lateral inhibition (LI) model, supposes that type II units receive inhibitory input from a number of narrowly tuned interneurons whose best frequencies (BFs) flank the BF of the type II unit. Tonal stimuli near BF result in only weak inhibitory input, but broadband stimuli recruit enough lateral inhibitors to greatly weaken the type II unit response. The second mechanism, a wideband inhibition (WBI) model, supposes that type II units receive inhibitory input from interneurons that are broadly tuned so that they respond more vigorously to broadband stimuli than to tones. Physiological and anatomical evidence points to the possible existence of such a class of neurons in the cochlear nucleus. The model extends an earlier computer model of an iso-frequency DCN patch to multiple frequency slices and adds a population of interneurons to provide the inhibition to model type II units (called I2-cells). The results show that both mechanisms accurately simulate responses of type II units to tones and noise. An experimental paradigm for distinguishing the two mechanisms is proposed. Received: 30 December 1996/Accepted in revised form: 13 March 1997