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Developing technologies for coupling neural activity and artificial neural components, is key for advancing neural interfaces and neuroprosthetics. We present a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network. The implementation of the hardware network (denoted NeuroSoC) exhibits complex dynamics with a multiplicity of time-scales, emulating 2880 neurons and 12.7M synapses, designed on a VLSI chip. This network is coupled to a neural network in-vitro, where the activities of both the biological and the hardware networks can be recorded, processed and integrated bidirectionally in real-time. This experimental setup enables an adjustable and well-monitored coupling, while at the same time providing access to the key functional features of neural networks. We demonstrate the feasibility to functionally couple the two networks and of implementing control circuits to modify the biohybrid activity. Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology

Much of what is known about the contribution of inhibition to stimulus discrimination is due to extensively studied sensory systems, which are highly structured neural circuits. The effect of inhibition on stimulus representation in less structured networks is not as clear. Here we exercise a biosynthetic approach in order to study the impacts of inhibition on stimulus representation in non-specialized network anatomy. Combining pharmacological manipulation, multisite electrical stimulation and recording from ex-vivo randomly rewired networks of cortical neurons, we quantified the effects of inhibition on response variability and stimulus discrimination at the population and single unit levels. We find that blocking inhibition quenches variability of responses evoked by repeated stimuli and enhances discrimination between stimuli that invade the network from different spatial loci. Enhanced stimulus discrimination is reserved for representation schemes that are based on temporal relation between spikes emitted in groups of neurons. Our data indicate that – under intact inhibition – the response to a given stimulus is a noisy version of the response evoked in the absence of inhibition. Spatial analysis suggests that the dispersion effect of inhibition is due to disruption of an otherwise coherent, wave-like propagation of activity.

Human visual perception is a complex, dynamic and fluctuating process. In addition to the incoming visual stimulus, it is affected by many other factors including temporal context, both external and internal to the observer. In this study we investigate the dynamic properties of psychophysical responses to a continuous stream of visual near-threshold detection tasks. We manipulate the incoming signals to have temporal structures with various characteristic timescales. Responses of human observers to these signals are analyzed using tools that highlight their dynamical features as well. We find that two opposing biases shape perception, and operate over distinct timescales. Positive recency appears over short times, e.g. consecutive trials. Adaptation, entailing an increased probability of changed response, reflects trends over longer times. Analysis of psychometric curves conditioned on various temporal events reveals that the balance between the two biases can shift depending on their interplay with the temporal properties of the input signal. A simple mathematical model reproduces the experimental data in all stimulus regimes. Taken together, our results support the view that visual response fluctuations reflect complex internal dynamics, possibly related to higher cognitive processes., Number of pages: 31 Number of figures: 20

Neuronal dynamics is intrinsically unstable, producing activity fluctuations that are essentially scale free. Here we study single cortical neurons of newborn ratsin vitro, and show that while these scale-free fluctuations are independent of temporal input statistics, they can be entrained by input variation. Joint input–output statistics and spike train reproducibility in synaptically isolated cortical neurons were measured in response to various input regimes over extended timescales (many minutes). Response entrainment was found to be maximal when the input itself possesses natural-like, scale-free statistics. We conclude that preference for natural stimuli, often observed at the system level, exists already at the elementary, single neuron level.

Emergence and maintenance of excitability is often phrased in terms of arriving at and remaining about a manifold of 'solutions' embedded in a high dimensional parameter space. Alongside studies that extend traditional focus on control-based regulation of structural parameters (channel densities), there is a budding interest in self-organization of kinetic parameters. In this picture, ionic channels are continually forced by activity in-and-out of a large pool of states not available for the mechanism of excitability. The process, acting on expressed structure, provides a bed for generation of a spectrum of excitability modes. Driven by microscopic fluctuations over a broad range of temporal scales, self-organization of kinetic parameters extends the metaphors and tools used in the study of development of excitability.

The involvement of dopamine in the process of learning, at the cellular and behavioral levels, has been studied extensively. Evidently, dopamine is released from midbrain nuclei neurons on exposure to salient unpredicted stimuli and binds to neurons of cortical and subcortical structures, where its neuromodulatory effects are exerted. The neuromodulatory effects of dopamine at the synaptic and cellular levels are very rich, but it is difficult to extrapolate from these elementary levels what their effect might be at the behaviorally relevant level of neuronal ensembles. Using multi-site recordings from networks of cortical neurons developing ex vivo, we studied the effects of dopamine on connectivity within neuronal ensembles. We found that dopamine disperses correlations between individual neuronal activities while preserving the global distribution of correlations at the network level. Using selective D1 and D2 modulators, we show that both receptor types are contributing to dopamine-induced dispersion. Our results indicate that, at the neuronal ensemble level, dopamine acts to enhance changes in network connectivity rather than stabilize such connections.

Memory is a property of diverse biological systems, including brain and heart. Studies in cortical neuronal networks have identified an increased sensitivity to infrequent (rare) stimulation patterns that can result in their achieving dominance over network firing. This adaptive behavior is applied to the heart in an attempt to explain the ability of pulmonary venous and other ectopic foci to achieve dominance over cardiac rhythm. Developmental changes in determinants of cardiac rhythm are explored as possible determinants of the range of rhythms expressed by the heart. By understanding the mechanisms for these behavior patterns, we may obtain new means for manipulating memory to return dysrhythmic hearts to normal sinus rhythm.

Identifying a system entails determining effective nonlinear dynamics capable of quantitatively predict its behaviour in time and the repertoire of states under a suited choice of few key parameters [1]. System identification has been used to devise effective ways in controlling and improving the comprehension of both neuronal network and single cell dynamics [2,3]. Here, we extend a recently introduced theory-driven method for the identification of integrate-and-fire (IF) neuron networks [4], to characterize the spontaneous and evoked activity in large-scale networks of cortical neurons in-vitro [5]. The approach assumes that population bursts (PBs) observed in these biological preparations, result from the competitive interplay between a net excitatory synaptic reverberation driving the firing growth, and an activity-dependent inhibition akin to processes underlying spike-frequency adaptation (SFA) phenomena [2]. Mean-field approximation provides a theoretical framework [6] in which these two ingredients are quantitatively described by nonlinearly coupled differential equations for population firing rate and a fatigue variable, not experimentally accessible, determining the excitability level of the network. The fatigue state variable is reconstructed looking for an optimal anti-correlation between firing rates and fatigue level following electrical stimulations, which are periodically delivered to the network to elicit PBs. Inferred fatigue dynamics largely correlates both with failure rate in evoking PBs and with response delay between stimulation times and PB onsets. Nevertheless, spontaneously PBs, those that occur without exogenous stimulations, show a variability which is not fully captured by the identified two dimensional dynamics. Resorting to a principal component analysis, we further refined our system identification, and uncovered an additional hidden degree of freedom acting as an inhibitory force at the population level. This inhibition rapidly reacts to firing changes and expresses characteristic time scales of few tens of milliseconds, much shorter than the few seconds estimated for fatigue dynamics [7]. The composition of these slow and fast activity-dependent inhibitions finally allowed us to infer other dynamical features of the system such as the current-to-rate gain function of neurons involved in PBs.

Psychological and physiological considerations entail that formation and functionality of neuronal cell assemblies depend upon synchronized repeated activation such as zero-lag synchronization. Several mechanisms for the emergence of this phenomenon have been suggested, including the global network quantity, the greatest common divisor of neuronal circuit delay loops. However, they require strict biological prerequisites such as precisely matched delays and connectivity, and synchronization is represented as a stationary mode of activity instead of a transient phenomenon. Here we show that the unavoidable increase in neuronal response latency to ongoing stimulation serves as a nonuniform gradual stretching of neuronal circuit delay loops. This apparent nuisance is revealed to be an essential mechanism in various types of neuronal time controllers, where synchronization emerges as a transient phenomenon and without predefined precisely matched synaptic delays. These findings are described in an experimental procedure where conditioned stimulations were enforced on a circuit of neurons embedded within a large-scale network of cortical cells in vitro, and are corroborated and extended by simulations of circuits composed of Hodgkin-Huxley neurons with time-dependent latencies. These findings announce a cortical time scale for time controllers based on tens of microseconds stretching of neuronal circuit delay loops per spike. They call for a reexamination of the role of the temporal periodic mode in brain functionality using advanced in vitro and in vivo experiments.

We present experimental and theoretical arguments, at the single neuron level, suggesting that neuronal response fluctuations reflect a process that positions the neuron near a transition point that separates excitable and unexcitable phases. This view is supported by the dynamical properties of the system as observed in experiments on isolated cultured cortical neurons, as well as by a theoretical mapping between the constructs of self organized criticality and membrane excitability biophysics., Comment: 5 pages, 2 figures

Synchronous activity impacts on a range of functional brain capacities in health and disease. To address the interrelations between cellular level activity and network-wide synchronous events, we implemented in vitro a recently introduced technique, the response clamp, which enables online monitoring of single neuron threshold dynamics while ongoing network synchronous activity continues uninterrupted. We show that the occurrence of a synchronous network event causes a significant biphasic change in the single neuron threshold. These threshold dynamics are correlated across the neurons constituting the network and are entailed by the input to the neurons rather than by their own spiking (i.e., output) activity. The magnitude of network activity during a synchronous event is correlated with the threshold state of individual neurons at the event's onset. Recovery from the impact of a given synchronous event on the neuronal threshold lasts several seconds and seems to be a key determinant of the time to the next spontaneously occurring synchronous event. Moreover, the neuronal threshold is shown to be correlated with the excitability dynamics of the entire network. We conclude that the relations between the two levels (network activity and the single neuron threshold) should be thought of in terms that emphasize their interactive nature.

We show that the unavoidable increase in neuronal response latency to ongoing stimulation serves as a nonuniform gradual stretching of neuronal circuit delay loops and emerges as an essential mechanism in the formation of various types of neuronal timers. Synchronization emerges as a transient phenomenon without predefined precise matched synaptic delays. These findings are described in an experimental procedure where conditioned stimulations were enforced on a circuit of neurons embedded within a large-scale network of cortical cells in-vitro, and are corroborated by neuronal simulations. They evidence a new cortical timescale based on tens of microseconds stretching of neuronal circuit delay loops per spike, and with realistic delays of a few milliseconds, synchronization emerges for a finite fraction of neuronal circuit delays., 12 pages, 4 figures, 13 pages of Supplementary material

Neural network models include at least one synaptic interaction between a pair of neurons so that plasticity can be conferred on the system. The single neuron is usually treated unrealistically in these models, functioning as a simple machine that transforms weighted synaptic input to firing frequency in a sigmoidal fashion. Knowledge gained in the past 15 years about the ways voltage-gated ion channels (the molecules of excitability) work, calls for re-evaluation of the role played by intrinsic neural excitability properties in learning and memory. We show that complex memory processes, ranging from seconds to ‘lifetime’, can be induced in a point neuron by including a realistic gating machinery of a potassium selective voltage-gated ion channel that is known to express in hippocampal cells.

We show that trial-to-trial variability in sensory detection of a weak visual stimulus is dramatically diminished when rather than presenting a fixed stimulus contrast, fluctuations in a subject's judgment are matched by fluctuations in stimulus contrast. This attenuation of fluctuations does not involve a change in the subject's psychometric function. The result is consistent with the interpretation of trial-to-trial variability in this sensory detection task being a high-level meta-cognitive control process that explores for something that our brains are so used to: subject-object relational dynamics., Comment: 11 pages, 3 figures

Responses of individual neurons to ongoing input are highly variable, reflecting complex threshold dynamics. Experimental access to this threshold dynamics is required in order to fully characterize neuronal input–output relationships. The challenge is practically intractable using present day experimental paradigms due to the cumulative, non-linear interactions involved. Here we introduce the Neuronal Response Clamp, a closed-loop technique enabling control over the instantaneous response probability of the neuron. The potential of the technique is demonstrated by showing direct access to threshold dynamics of cortical neuron in vitro using extracellular recording and stimulation, over timescales ranging from seconds to many hours. Moreover, the method allowed us to expose the sensitivity of threshold dynamics to spontaneous input from the network in which the neuron is embedded. The Response-Clamp technique follows the rationale of the voltage-clamp and dynamic-clamp approaches, extending it to the neuron's spiking behavior. The general framework offered here is applicable in the study of other neural systems, beyond the single neuron level.

Although neuronal excitability is well understood and accurately modeled over timescales of up to hundreds of milliseconds, it is currently unclear whether extrapolating from this limited duration to longer behaviorally relevant timescales is appropriate. Here we used an extracellular recording and stimulation paradigm that extends the duration of single-neuron electrophysiological experiments, exposing the dynamics of excitability in individual cultured cortical neurons over timescales hitherto inaccessible. We show that the long-term neuronal excitability dynamics is unstable and dominated by critical fluctuations, intermittency, scale-invariant rate statistics, and long memory. These intrinsic dynamics bound the firing rate over extended timescales, contrasting observed short-term neuronal response to stimulation onset. Furthermore, the activity of a neuron over extended timescales shows transitions between quasi-stable modes, each characterized by a typical response pattern. Like in the case of rate statistics, the short-term onset response pattern that often serves to functionally define a given neuron is not indicative of its long-term ongoing response. These observations question the validity of describing neuronal excitability based on temporally restricted electrophysiological data, calling for in-depth exploration of activity over wider temporal scales. Such extended experiments will probably entail a different kind of neuronal models, accounting for the unbounded range, from milliseconds up.

Distinct modules of the neural circuitry interact with each other and (through the motor-sensory loop) with the environment, forming a complex dynamic system. Neuro-prosthetic devices seeking to modulate or restore CNS function need to interact with the information flow at the level of neural modules electrically, bi-directionally and in real-time. A set of freely available generic tools is presented that allow computationally demanding multi-channel short-latency bi-directional interactions to be realized in in-vivo and in-vitro preparations using standard PC data acquisition and processing hardware and software (Mathworks Matlab and Simulink). A commercially available 60-channel extracellular multi-electrode recording and stimulation set-up connected to an ex-vivo developing cortical neuronal culture is used as a model system to validate the method. We demonstrate how complex high-bandwidth (> 10 megabit per second) neural recording data can be analyzed in real-time while simultaneously generating specific complex electrical stimulation feedback with deterministically timed responses at sub-millisecond resolution.

This article aims at making readers, experimentalists and theorists, more aware of the abstractions made by an observer when measuring and reporting behavioral and neural timescales. These abstractions stow away the fact that, above lower boundaries that reflect fairly well understood physical constraints, observed and reported timescales are often not intrinsic to the biological system; rather, in most cases they reflect conditions that are imposed by the observer through the measuring procedure. This is true at practically every level of organization, from behavior down to molecules. I analyze the impacts of the resulting temporal manifold in behavioral and brain sciences on experimental designs and mathematical modeling.

Long term time-lapse imaging reveals that individual synapses undergo significant structural remodeling not only when driven by activity, but also when network activity is absent, raising questions about how reliably individual synapses maintain connections., Synaptic plasticity is widely believed to constitute a key mechanism for modifying functional properties of neuronal networks. This belief implicitly implies, however, that synapses, when not driven to change their characteristics by physiologically relevant stimuli, will maintain these characteristics over time. How tenacious are synapses over behaviorally relevant time scales? To begin to address this question, we developed a system for continuously imaging the structural dynamics of individual synapses over many days, while recording network activity in the same preparations. We found that in spontaneously active networks, distributions of synaptic sizes were generally stable over days. Following individual synapses revealed, however, that the apparently static distributions were actually steady states of synapses exhibiting continual and extensive remodeling. In active networks, large synapses tended to grow smaller, whereas small synapses tended to grow larger, mainly during periods of particularly synchronous activity. Suppression of network activity only mildly affected the magnitude of synaptic remodeling, but dependence on synaptic size was lost, leading to the broadening of synaptic size distributions and increases in mean synaptic size. From the perspective of individual neurons, activity drove changes in the relative sizes of their excitatory inputs, but such changes continued, albeit at lower rates, even when network activity was blocked. Our findings show that activity strongly drives synaptic remodeling, but they also show that significant remodeling occurs spontaneously. Whereas such spontaneous remodeling provides an explanation for “synaptic homeostasis” like processes, it also raises significant questions concerning the reliability of individual synapses as sites for persistently modifying network function., Author Summary Neurons communicate via synapses, and it is believed that activity-dependent modifications to synaptic connections—synaptic plasticity—is a fundamental mechanism for stably altering the function of neuronal networks. This belief implies that synapses, when not driven to change their properties by physiologically relevant stimuli, should preserve their individual properties over time. Otherwise, physiologically relevant modifications to network function would be gradually lost or become inseparable from stochastically occurring changes in the network. So do synapses actually preserve their properties over behaviorally relevant time scales? To begin to address this question, we examined the structural dynamics of individual postsynaptic densities for several days, while recording and manipulating network activity levels in the same networks. We found that as expected in highly active networks, individual synapses undergo continual and extensive remodeling over time scales of many hours to days. However, we also observed, that synaptic remodeling continues at very significant rates even when network activity is completely blocked. Our findings thus indicate that the capacity of synapses to preserve their specific properties might be more limited than previously thought, raising intriguing questions about the long-term reliability of individual synapses.

In this perspective we provide an example for the limits of reverse engineering in neuroscience. We demonstrate that application of reverse engineering to the study of the design principle of a functional neuro-system with a known mechanism, may result in a perfectly valid but wrong induction of the system's design principle. If in the very simple setup we bring here (static environment, primitive task and practically unlimited access to every piece of relevant information), it is difficult to induce a design principle, what are our chances of exposing biological design principles when more realistic conditions are examined− Implications to the way we do Biology are discussed.

Adaptation of activity in excitable membranes occurs over a wide range of timescales. Standard computational approaches handle this wide temporal range in terms of multiple states and related reaction rates emanating from the complexity of ionic channels. The study described here takes a different (perhaps complementary) approach, by interpreting ion channel kinetics in terms of population dynamics. I show that adaptation in excitable membranes is reducible to a simple Logistic-like equation in which the essential non-linearity is replaced by a feedback loop between the history of activation and an adaptive transition rate that is sensitive to a single dimension of the space of inactive states. This physiologically measurable dimension contributes to the stability of the system and serves as a powerful modulator of input-output relations that depends on the patterns of prior activity; an intrinsic scale free mechanism for cellular adaptation that emerges from the microscopic biophysical properties of ion channels of excitable membranes.

The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen., Author Summary The idea that sensory objects are represented by the order in which neurons are recruited in response to stimulus presentation was put forward over a decade ago, largely based on computational biology considerations. While intensively analyzed in simulation studies, the general biological applicability of this highly compacted and efficient representation scheme, as an ensemble neural code, was never established. In recent years, algorithmic and experimental technologies advanced to a stage that allows for facing the challenge; here we took advantage of this progress. We let a large-scale random network of cortical neurons develop on top of a microfabricated, multielectrode array that enables electronic interrogation of the network, stimulating through various points in space, and simultaneously recorded the resulting activities from a large number of neurons. We applied state-of-the-art classification algorithms and asked how well the rank order representation scheme handles categorization tasks. We show that recruitment order is generally applicable as an ensemble code; it emerges spontaneously in a large “structureless” network of neurons as a functional code that is invariant to significant temporal variance in spike times and spike rates and flawlessly classifies inputs on a trial-to-trial basis.

Eytan and Marom recently showed that the spontaneous burst activity of rat neuron cultures includes `first to fire' cells that consistently fire earlier than others. Here we analyze the behavior of these neurons in long term recordings of spontaneous activity of rat hippocampal and rat cortical neuron cultures from three different laboratories. We identify precursor events that may either subside (`aborted bursts') or can lead to a full-blown burst (`pre-bursts'). We find that the activation in the pre-burst typically has a first neuron (`leader'), followed by a localized response in its neighborhood. Locality is diminished in the bursts themselves. The long term dynamics of the leaders is relatively robust, evolving with a half-life of 23-34 hours. We show that the leaders carry information about the identity of the burst, as measured by the signature of the number of spikes per neuron in a burst. The number of spikes from leaders in the first few spikes of a precursor event is furthermore shown to be predictive with regard to the transition into a burst (pre-burst versus aborted burst). We conclude that the leaders play a role in the development of the bursts.

This contribution describes the use of multi-site interaction with large cortical networks in the study of learning. The general physiological properties of the network are described, and the concept of learning is mapped to the experimental network preparation. Learning is then analyzed in terms of exploration (defined as changes in the configuration of associations within the biological network) and recognition (the stabilization of “worthy” associations).

A key property of neural systems is their ability to adapt selectively to stimuli with different features. Using multisite electrical recordings from networks of cortical neurons developingex vivo, we show that neurons adapt selectively to different stimuli invading the network. We focus on selective adaptation to frequent and rare stimuli; networks were stimulated at two sites with two different stimulus frequencies. When both stimuli were presented within the same period, neurons in the network attenuated their responsiveness to the more frequent input, whereas their responsiveness to the rarely delivered stimuli showed a marked average increase. The amplification of the response to rare stimuli required the presence of the other, more frequent stimulation source. By contrast, the decreased response to the frequent stimuli occurred regardless of the presence of the rare stimuli. Analysis of the response of single units suggests that both of these effects are caused by changes in synaptic transmission. By using synaptic blockers, we find that the increased responsiveness to the rarely stimulated site depends specifically on fast GABAergic transmission. Thus, excitatory synaptic depression, the inhibitory sub-network, and their balance play an active role in generating selective gain control. The observation that selective adaptation arises naturally in a network of cortical neurons developingex vivoindicates that this is an inherent feature of spontaneously organizing cortical networks.

The results presented here demonstrate selective learning in a network of real cortical neurons. We focally stimulate the network at a low frequency (0.3–1 Hz) until a desired predefined response is observed 50 ± 10 msec after a stimulus, at which point the stimulus is stopped for 5 min. Repeated cycles of this procedure ultimately lead to the desired response being directly elicited by the stimulus. By plotting the number of stimuli required to achieve the target response in each cycle, we are able to generate learning curves. Presumably, the repetitive stimulation is driving changes in the circuit, and we are selecting for changes consistent with the predefined desired response. To the best of our knowledge, this is the first time learning of arbitrarily chosen tasks, in networks composed of real cortical neurons, is demonstrated outside of the body.

The input-output relation of a single neuron stands at the basis of every biologically oriented description of the brain. This report shows that the input-output relation of cultured cortical neurons is non-linearly tuned by the input frequency. Increasing the rate of stimulation results in the appearance of ordered temporal firing patterns, which are qualitatively different for different input frequencies. The experimental results of this study lead to the conclusion that frequency tuning of neuronal input-output relation arises from activity-dependent rates at the molecular level underlying the mechanism of excitability itself.

Biological systems often change their responsiveness when subject to persistent stimulation, a phenomenon termed adaptation. In neural systems, this process is often selective, allowing the system to adapt to one stimulus while preserving its sensitivity to another. In some studies, it has been shown that adaptation to a frequent stimulus increases the system's sensitivity to rare stimuli. These phenomena were explained in previous work as a result of complex interactions between the various subpopulations of the network. A formal description and analysis of neuronal systems, however, is hindered by the network's heterogeneity and by the multitude of processes taking place at different time-scales. Viewing neural networks as populations of interacting elements, we develop a framework that facilitates a formal analysis of complex, structured, heterogeneous networks. The formulation developed is based on an analysis of the availability of activity dependent resources, and their effects on network responsiveness. This approach offers a simple mechanistic explanation for selective adaptation, and leads to several predictions that were corroborated in both computer simulations and in cultures of cortical neurons developing in vitro. The framework is sufficiently general to apply to different biological systems, and was demonstrated in two different cases., Author Summary Our mind continuously adapts to background sensory events while preserving and even enhancing its sensitivity to deviant objects. In the visual modality, for instance, a target violating a surrounding pattern is easily detected, a phenomenon termed “pop-out.” Indeed, the automatic attention we pay to the irregular or the surprising, which developed as a valuable aid in our survival, is often used to advantage nowadays in popular culture and advertisement. Such phenomena have been investigated in many systems, from psychophysics in behaving animals to experiments in neural networks developing in vitro. In this work, we develop a mechanistic model that demonstrates how a relatively simple system may express such selective behavior. We apply our model first to the case of transiently responding networks, and compare the results with computer simulations and experimental data collected from neural networks developing in vitro. We also demonstrate the application of the model to other systems. Our approach provides insight as to how complex, behavior-related phenomena may arise from simple dynamic interactions between the system's elementary components.