Background Synaptic Activity Research Articles

Weak signal detection capacity of type-II Morris–Lecar neuron system under presynaptic bombardments

The information encoding mechanism of the neuronal system is quite complex and is sensitive to environmental conditions. Neuronal noise, especially synaptic random inputs, is a natural part of the brain. Theoretical and experimental studies have revealed that noise plays a vital role in signal frequency selectivity and weak sensory input perception of the neuronal system. There is a consensus that sufficiently intense noise can trigger rhythmic brain activity, and these noise-induced oscillations could improve signal processing of the neuronal system, particularly when the external signal frequency aligns with the frequency of the noise-induced rhythm. Such behavior in response to noise in the signal-processing mechanism of biological systems is elucidated by stochastic resonance. In this regard, this paper systematically analyzes the stochastic resonance in a Type-II Morris–Lecar neuron under excitatory and inhibitory background spike bombardments. Our results indicate that Morris–Lecar Type II neurons enhance their signal coding capacity by exploiting background synaptic activity. In addition, it has been revealed that the biological properties of the background synaptic connections of neurons that oscillate at different frequencies, such as delta, theta, alpha, beta, and gamma bands, are different. On the other hand, it has been found that neurons adapt to the properties of pre-synaptic neurons to encode weak signals. This study ensures novel insights into the functional role of background synaptic input in neuronal coding mechanisms by demonstrating a biophysical factor of stochastic resonance via numerical analyses. Furthermore, our results show that the properties of the background synaptic inputs significantly determine what kind of signal to encode or not. These results are an important indicator of the signal frequency selectivity of neurons, such as auditory cells.

A Mathematical Model of Spontaneous Action Potential Based on Stochastics Synaptic Noise Dynamics in Non-Neural Cells

We developed a mathematical model to simulate the dynamics of background synaptic noise in non-neuronal cells. By employing the stochastic Ornstein–Uhlenbeck process, we represented excitatory synaptic conductance and integrated it into a whole-cell model to generate spontaneous and evoke cellular electrical activities. This single-cell model encompasses numerous biophysically detailed ion channels, depicted by a set of ordinary differential equations in Hodgkin–Huxley and Markov formalisms. Consequently, this approach effectively induced irregular spontaneous depolarizations (SDs) and spontaneous action potentials (sAPs), resembling electrical activity observed in vitro. The input resistance decreased significantly, while the firing rate of spontaneous action potentials increased. Moreover, alterations in the ability to reach the action potential threshold were observed. Background synaptic activity can modify the input/output characteristics of non-neuronal excitatory cells. Hence, suppressing these baseline activities could aid in identifying new pharmaceutical targets for various clinical diseases.

Synaptic plasticity of inhibitory synapses onto medial olivocochlear efferent neurons.

The descending auditory system modulates the ascending system at every level. The final descending, or efferent, stage comprises lateral olivocochlear and medial olivocochlear (MOC) neurons. MOC somata in the ventral brainstem project axons to the cochlea to synapse onto outer hair cells (OHC), inhibiting OHC‐mediated cochlear amplification. MOC suppression of OHC function is implicated in cochlear gain control with changing sound intensity, detection of salient stimuli, attention and protection against acoustic trauma. Thus, sound excites MOC neurons to provide negative feedback of the cochlea. Sound also inhibits MOC neurons via medial nucleus of the trapezoid body (MNTB) neurons. However, MNTB–MOC synapses exhibit short‐term depression, suggesting reduced MNTB–MOC inhibition during sustained stimuli. Further, due to high rates of both baseline and sound‐evoked activity in MNTB neurons in vivo, MNTB–MOC synapses may be tonically depressed. To probe this, we characterized short‐term plasticity of MNTB–MOC synapses in mouse brain slices. We mimicked in vivo‐like temperature and extracellular calcium conditions, and in vivo‐like activity patterns of fast synaptic activation rates, sustained activation and prior tonic activity. Synaptic depression was sensitive to extracellular calcium concentration and temperature. During rapid MNTB axon stimulation, postsynaptic currents in MOC neurons summated but with concurrent depression, resulting in smaller, sustained currents, suggesting tonic inhibition of MOC neurons during rapid circuit activity. Low levels of baseline MNTB activity did not significantly reduce responses to subsequent rapid activity that mimics sound stimulation, indicating that, in vivo, MNTB inhibition of MOC neurons persists despite tonic synaptic depression.Key points Inhibitory synapses from the medial nucleus of the trapezoid body (MNTB) onto medial olivocochlear (MOC) neurons exhibit short‐term plasticity that is sensitive to calcium and temperature, with enhanced synaptic depression occurring at higher calcium concentrations and at room temperature.High rates of background synaptic activity that mimic the upper limits of spontaneous MNTB activity cause tonic synaptic depression of MNTB–MOC synapses that limits further synaptic inhibition.High rates of activity at MNTB–MOC synapses cause synaptic summation with concurrent depression to yield a response with an initial large amplitude that decays to a tonic inhibition.

Numerical Simulation: Fluctuation in Background Synaptic Activity Regulates Synaptic Plasticity.

Synaptic plasticity is vital for learning and memory in the brain. It consists of long-term potentiation (LTP) and long-term depression (LTD). Spike frequency is one of the major components of synaptic plasticity in the brain, a noisy environment. Recently, we mathematically analyzed the frequency-dependent synaptic plasticity (FDP) in vivo and found that LTP is more likely to occur with an increase in the frequency of background synaptic activity. Meanwhile, previous studies suggest statistical fluctuation in the amplitude of background synaptic activity. Little is understood, however, about its contribution to synaptic plasticity. To address this issue, we performed numerical simulations of a calcium-based synapse model. Then, we found attenuation of the tendency to become LTD due to an increase in the fluctuation of background synaptic activity, leading to an enhancement of synaptic weight. Our result suggests that the fluctuation affects synaptic plasticity in the brain.

Multicoding in neural information transfer suggested by mathematical analysis of the frequency-dependent synaptic plasticity in vivo

Two elements of neural information processing have primarily been proposed: firing rate and spike timing of neurons. In the case of synaptic plasticity, although spike-timing-dependent plasticity (STDP) depending on presynaptic and postsynaptic spike times had been considered the most common rule, recent studies have shown the inhibitory nature of the brain in vivo for precise spike timing, which is key to the STDP. Thus, the importance of the firing frequency in synaptic plasticity in vivo has been recognized again. However, little is understood about how the frequency-dependent synaptic plasticity (FDP) is regulated in vivo. Here, we focused on the presynaptic input pattern, the intracellular calcium decay time constants, and the background synaptic activity, which vary depending on neuron types and the anatomical and physiological environment in the brain. By analyzing a calcium-based model, we found that the synaptic weight differs depending on these factors characteristic in vivo, even if neurons receive the same input rate. This finding suggests the involvement of multifaceted factors other than input frequency in FDP and even neural coding in vivo.

Membrane Potential Correlates of Network Decorrelation and Improved SNR by Cholinergic Activation in the Somatosensory Cortex.

The nucleus basalis (NB) projects cholinergic axons to the cortex, where they play a major role in arousal, attention, and learning. Cholinergic inputs shift cortical dynamics from synchronous to asynchronous and improve the signal-to-noise ratio (SNR) of sensory responses. However, the underlying mechanisms of these changes remain unclear. Using simultaneous extracellular and whole-cell patch recordings in layer 4 of the mouse barrel cortex, we show that electrical or optogenetic activation of the cholinergic system has a differential effect on ongoing and sensory evoked activities. Cholinergic activation profoundly reduced the large spontaneous fluctuations in membrane potential and decorrelated ongoing activity. However, NB stimulation had no effect on the response to whisker stimulation or on signal correlations. These effects of cholinergic activation provide a unified explanation for the increased SNR of sensory response and for the reduction in noise correlations and explain the shift into the desynchronized cortical state, which are the hallmarks of arousal and attention.SIGNIFICANCE STATEMENT Attention increases the signal-to-noise ratio (SNR) of cortical sensory response, which may reflect either reduction in background firing rate or increased sensory response. Extracellular recordings showed that attention also reduces the correlation in network activity. These effects are partially mediated by cholinergic axons from the nucleus basalis projecting to the entire cortex. To reveal the cellular and synaptic correlates of these cholinergic effects, we performed simultaneous intracellular and LFP recordings in the somatosensory cortex. Global or local cholinergic activation increased the SNR of sensory response mainly by reducing the rate and amplitude of background synaptic activity and also reduced network correlations. Therefore, coding of sensory information is enhanced by the cholinergic system mainly due to a reduction in spontaneous activity.

Dynamical response properties of neocortical neurons to conductance-driven time-varying inputs.

Ensembles of cortical neurons can track fast-varying inputs and relay them in their spike trains, far beyond the cut-off imposed by membrane passive electrical properties and mean firing rates. Initially explored in silico and later demonstrated experimentally, investigating how neurons respond to sinusoidally modulated stimuli provides a deeper insight into spike initiation mechanisms and information processing than conventional F-I curve methodologies. Besides net membrane currents, physiological synaptic inputs can also induce a stimulus-dependent modulation of the total membrane conductance, which is not reproduced by standard current-clamp protocols. Here, we investigated whether rat cortical neurons can track fast temporal modulations over a noisy conductance background. We also determined input-output transfer properties over a range of conditions, including: distinct presynaptic activation rates, postsynaptic firing rates and variability and type of temporal modulations. We found a very broad signal transfer bandwidth across all conditions, similar large cut-off frequencies and power-law attenuations of fast-varying inputs. At slow and intermediate input modulations, the response gain decreased for increasing output mean firing rates. The gain also decreased significantly for increasing intensities of background synaptic activity, thus generalising earlier studies on F-I curves. We also found a direct correlation between the action potentials' onset rapidness and the neuronal bandwidth. Our novel results extend previous investigations of dynamical response properties to non-stationary and conductance-driven conditions, and provide computational neuroscientists with a novel set of observations that models must capture when aiming to replicate cortical cellular excitability.

A Dynamic Clamp on Every Rig.

The dynamic clamp should be a standard part of every cellular electrophysiologist’s toolbox. That it is not, even 25 years after its introduction, comes down to three issues: money, the disruption that adding dynamic clamp to an existing electrophysiology rig entails, and the technical prowess required of experimenters. These have been valid and limiting issues in the past, but no longer. Technological advances associated with the so-called maker movement render them moot. We demonstrate this by implementing a fast (∼100 kHz) dynamic clamp system using an inexpensive microcontroller (Teensy 3.6). The overall cost of the system is less than USD$100, and assembling it requires no prior electronics experience. Modifying it—for example, to add Hodgkin–Huxley-style conductances—requires no prior programming experience. The system works together with existing electrophysiology data acquisition systems (for Macintosh, Windows, and Linux); it does not attempt to supplant them. Moreover, the process of assembling, modifying, and using the system constitutes a useful pedagogical exercise for students and researchers with no background but an interest in electronics and programming. We demonstrate the system’s utility by implementing conductances as fast as a transient sodium conductance and as complex as the Ornstein–Uhlenbeck conductances of the “point conductance” model of synaptic background activity.

Background noise exerts diverse effects on the cortical encoding of foreground sounds.

In natural listening conditions, many sounds must be detected and identified in the context of competing sound sources, which function as background noise. Traditionally, noise is thought to degrade the cortical representation of sounds by suppressing responses and increasing response variability. However, recent studies of neural network models and brain slices have shown that background synaptic noise can improve the detection of signals. Because acoustic noise affects the synaptic background activity of cortical networks, it may improve the cortical responses to signals. We used spike train decoding techniques to determine the functional effects of a continuous white noise background on the responses of clusters of neurons in auditory cortex to foreground signals, specifically frequency-modulated sweeps (FMs) of different velocities, directions, and amplitudes. Whereas the addition of noise progressively suppressed the FM responses of some cortical sites in the core fields with decreasing signal-to-noise ratios (SNRs), the stimulus representation remained robust or was even significantly enhanced at specific SNRs in many others. Even though the background noise level was typically not explicitly encoded in cortical responses, significant information about noise context could be decoded from cortical responses on the basis of how the neural representation of the foreground sweeps was affected. These findings demonstrate significant diversity in signal in noise processing even within the core auditory fields that could support noise-robust hearing across a wide range of listening conditions.NEW & NOTEWORTHY The ability to detect and discriminate sounds in background noise is critical for our ability to communicate. The neural basis of robust perceptual performance in noise is not well understood. We identified neuronal populations in core auditory cortex of squirrel monkeys that differ in how they process foreground signals in background noise and that may contribute to robust signal representation and discrimination in acoustic environments with prominent background noise.

Conditional Spike Transmission Mediated by Electrical Coupling Ensures Millisecond Precision-Correlated Activity among Interneurons In Vivo

SummaryMany GABAergic interneurons are electrically coupled and in vitro can display correlated activity with millisecond precision. However, the mechanisms underlying correlated activity between interneurons in vivo are unknown. Using dual patch-clamp recordings in vivo, we reveal that in the presence of spontaneous background synaptic activity, electrically coupled cerebellar Golgi cells exhibit robust millisecond precision-correlated activity which is enhanced by sensory stimulation. This precisely correlated activity results from the cooperative action of two mechanisms. First, electrical coupling ensures slow subthreshold membrane potential correlations by equalizing membrane potential fluctuations, such that coupled neurons tend to approach action potential threshold together. Second, fast spike-triggered spikelets transmitted through gap junctions conditionally trigger postjunctional spikes, depending on both neurons being close to threshold. Electrical coupling therefore controls the temporal precision and degree of both spontaneous and sensory-evoked correlated activity between interneurons, by the cooperative effects of shared synaptic depolarization and spikelet transmission.

Membrane Potential Dynamics of Spontaneous and Visually Evoked Gamma Activity in V1 of Awake Mice.

Cortical gamma activity (30–80 Hz) is believed to play important functions in neural computation and arises from the interplay of parvalbumin-expressing interneurons (PV) and pyramidal cells (PYRs). However, the subthreshold dynamics underlying its emergence in the cortex of awake animals remain unclear. Here, we characterized the intracellular dynamics of PVs and PYRs during spontaneous and visually evoked gamma activity in layers 2/3 of V1 of awake mice using targeted patch-clamp recordings and synchronous local field potentials (LFPs). Strong gamma activity patterned in short bouts (one to three cycles), occurred when PVs and PYRs were depolarizing and entrained their membrane potential dynamics regardless of the presence of visual stimulation. PV firing phase locked unconditionally to gamma activity. However, PYRs only phase locked to visually evoked gamma bouts. Taken together, our results indicate that gamma activity corresponds to short pulses of correlated background synaptic activity synchronizing the output of cortical neurons depending on external sensory drive.

Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection

The neural correlates of optimal states for signal detection task performance are largely unknown. One hypothesis holds that optimal states exhibit tonically depolarized cortical neurons with enhanced spiking activity, such as occur during movement. Werecorded membrane potentials of auditory cortical neurons in mice trained on a challenging tone-in-noise detection task while assessing arousal with simultaneous pupillometry and hippocampal recordings. Arousal measures accurately predicted multiple modes of membrane potential activity, including rhythmic slow oscillations at low arousal, stable hyperpolarization at intermediate arousal, and depolarization during phasic or tonic periods of hyper-arousal. Walking always occurred during hyper-arousal. Optimal signal detection behavior and sound-evoked responses, at both sub-threshold and spiking levels, occurred at intermediate arousal when pre-decision membrane potentials were stably hyperpolarized. These results reveal a cortical physiological signature of the classically observed inverted-U relationship between task performance and arousal and that optimal detection exhibits enhanced sensory-evoked responses and reduced background synaptic activity.

A neural network model of reliably optimized spike transmission.

We studied the detailed structure of a neuronal network model in which the spontaneous spike activity is correctly optimized to match the experimental data and discuss the reliability of the optimized spike transmission. Two stochastic properties of the spontaneous activity were calculated: the spike-count rate and synchrony size. The synchrony size, expected to be an important factor for optimization of spike transmission in the network, represents a percentage of observed coactive neurons within a time bin, whose probability approximately follows a power-law. We systematically investigated how these stochastic properties could matched to those calculated from the experimental data in terms of the log-normally distributed synaptic weights between excitatory and inhibitory neurons and synaptic background activity induced by the input current noise in the network model. To ensure reliably optimized spike transmission, the synchrony size as well as spike-count rate were simultaneously optimized. This required changeably balanced log-normal distributions of synaptic weights between excitatory and inhibitory neurons and appropriately amplified synaptic background activity. Our results suggested that the inhibitory neurons with a hub-like structure driven by intensive feedback from excitatory neurons were a key factor in the simultaneous optimization of the spike-count rate and synchrony size, regardless of different spiking types between excitatory and inhibitory neurons.

Effect of a Blocker of Nicotine Acetylcholine Receptors on Excitatory Postsynaptic Currents in Ganglion Cells of the Rat Retina

We examined the involvement of nicotine acetylcholine receptors (nAChRs) in the generation of background synaptic activity in rat retinal ganglion cells (RGCs). Experiments were carried out in vitro on RGCs of the isolated retina of 21-day-old rats using the patch-clamp technique in the whole cell configuration (voltage-clamp mode). The neurons did not undergo enzymatic processing in order to provide more adequate preservation of the properties of cells of the native retina. In 15 examined cells, the resting membrane potential (RMP) was –62 ± 2 mV, on average, the input resistance of the membrane was 573 ± 68 MΩ, and the membrane capacitance was 34 ± 5 pF. The effects of blocking of nAChRs were estimated using bеnzohexonium (BH) in a 450 μM concentration. Background excitatory postsynaptic currents (EPSCs) in RGCs appeared, under the conditions of our experiments, with the mean frequency of 3.1 ± 0.8 sec–1 (n = 10). Application of BH resulted in suppression of the currents in six cells. Significant selectivity of the effects of these blockers was observed; it affected mostly high-amplitude EPSCs (40–100 pA). The effect of BH was mostly reversible. Our results show that activation of nAChRs modulate electrical activity in RGCs; probably, these receptors mediate certain types of synaptic transmission. The general RGC population is rather heterogeneous from the aspect of the presence/absence of nAChRs and distribution of separate subtypes of the latter.

Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits.

Layer 5 thick tufted pyramidal cells (TTCs) in the neocortex are particularly electrically complex, owing to their highly excitable dendrites. The interplay between dendritic nonlinearities and recurrent cortical microcircuit activity in shaping network response is largely unknown. We simulated detailed conductance-based models of TTCs forming recurrent microcircuits that were interconnected as found experimentally; the network was embedded in a realistic background synaptic activity. TTCs microcircuits significantly amplified brief thalamocortical inputs; this cortical gain was mediated by back-propagation activated N-methyl-d-aspartate depolarizations and dendritic back-propagation-activated Ca2+ spike firing, ignited by the coincidence of thalamic-activated somatic spike and local dendritic synaptic inputs, originating from the cortical microcircuit. Surprisingly, dendritic nonlinearities in TTCs microcircuits linearly multiplied thalamic inputs—amplifying them while maintaining input selectivity. Our findings indicate that dendritic nonlinearities are pivotal in controlling the gain and the computational functions of TTCs microcircuits, which serve as a dominant output source for the neocortex.

Regional cerebral metabolic patterns demonstrate the role of anterior forebrain mesocircuit dysfunction in the severely injured brain.

Although disorders of consciousness (DOCs) demonstrate widely varying clinical presentations and patterns of structural injury, global down-regulation and bilateral reductions in metabolism of the thalamus and frontoparietal network are consistent findings. We test the hypothesis that global reductions of background synaptic activity in DOCs will associate with changes in the pattern of metabolic activity in the central thalamus and globus pallidus. We compared 32 [(18)F]fluorodeoxyglucose PETs obtained from severely brain-injured patients (BIs) and 10 normal volunteers (NVs). We defined components of the anterior forebrain mesocircuit on high-resolution T1-MRI (ventral, associative, and sensorimotor striatum; globus pallidus; central thalamus and noncentral thalamus). Metabolic profiles for BI and NV demonstrated distinct changes in the pattern of uptake: ventral and association striatum (but not sensorimotor) were significantly reduced relative to global mean uptake after BI; a relative increase in globus pallidus metabolism was evident in BI subjects who also showed a relative reduction of metabolism in the central thalamus. The reversal of globus pallidus and central thalamus profiles across BIs and NVs supports the mesocircuit hypothesis that broad functional (or anatomic) deafferentation may combine to reduce central thalamus activity and release globus pallidus activity in DOCs. In addition, BI subjects showed broad frontoparietal metabolic down-regulation consistent with prior studies supporting the link between central thalamic/pallidal metabolism and down-regulation of the frontoparietal network. Recovery of left hemisphere frontoparietal metabolic activity was further associated with command following.

Background synaptic activity in rat entorhinal cortex shows a progressively greater dominance of inhibition over excitation from deep to superficial layers.

The entorhinal cortex (EC) controls hippocampal input and output, playing major roles in memory and spatial navigation. Different layers of the EC subserve different functions and a number of studies have compared properties of neurones across layers. We have studied synaptic inhibition and excitation in EC neurones, and we have previously compared spontaneous synaptic release of glutamate and GABA using patch clamp recordings of synaptic currents in principal neurones of layers II (L2) and V (L5). Here, we add comparative studies in layer III (L3). Such studies essentially look at neuronal activity from a presynaptic viewpoint. To correlate this with the postsynaptic consequences of spontaneous transmitter release, we have determined global postsynaptic conductances mediated by the two transmitters, using a method to estimate conductances from membrane potential fluctuations. We have previously presented some of this data for L3 and now extend to L2 and L5. Inhibition dominates excitation in all layers but the ratio follows a clear rank order (highest to lowest) of L2>L3>L5. The variance of the background conductances was markedly higher for excitation and inhibition in L2 compared to L3 or L5. We also show that induction of synchronized network epileptiform activity by blockade of GABA inhibition reveals a relative reluctance of L2 to participate in such activity. This was associated with maintenance of a dominant background inhibition in L2, whereas in L3 and L5 the absolute level of inhibition fell below that of excitation, coincident with the appearance of synchronized discharges. Further experiments identified potential roles for competition for bicuculline by ambient GABA at the GABAA receptor, and strychnine-sensitive glycine receptors in residual inhibition in L2. We discuss our results in terms of control of excitability in neuronal subpopulations of EC neurones and what these may suggest for their functional roles.

Electroencephalographic field influence on calcium momentum waves

Macroscopic electroencephalographic (EEG) fields can be an explicit top-down neocortical mechanism that directly drives bottom-up processes that describe memory, attention, and other neuronal processes. The top-down mechanism considered is macrocolumnar EEG firings in neocortex, as described by a statistical mechanics of neocortical interactions (SMNI), developed as a magnetic vector potential A. The bottom-up process considered is Ca2+ waves prominent in synaptic and extracellular processes that are considered to greatly influence neuronal firings. Here, the complimentary effects are considered, i.e., the influence of A on Ca2+ momentum, p. The canonical momentum of a charged particle in an electromagnetic field, Π=p+qA (SI units), is calculated, where the charge of Ca2+ is q=−2e, e is the magnitude of the charge of an electron. Calculations demonstrate that macroscopic EEG A can be quite influential on the momentum p of Ca2+ ions, in both classical and quantum mechanics. Molecular scales of Ca2+ wave dynamics are coupled with A fields developed at macroscopic regional scales measured by coherent neuronal firing activity measured by scalp EEG. The project has three main aspects: fitting A models to EEG data as reported here, building tripartite models to develop A models, and studying long coherence times of Ca2+ waves in the presence of A due to coherent neuronal firings measured by scalp EEG. The SMNI model supports a mechanism wherein the p+qA interaction at tripartite synapses, via a dynamic centering mechanism (DCM) to control background synaptic activity, acts to maintain short-term memory (STM) during states of selective attention.

Inverse stochastic resonance induced by synaptic background activity with unreliable synapses

Inverse stochastic resonance (ISR) is a recently pronounced phenomenon that is the minimum occurrence in mean firing rate of a rhythmically firing neuron as noise level varies. Here, by using a realistic modeling approach for the noise, we investigate the ISR with concrete biophysical mechanisms. It is shown that mean firing rate of a single neuron subjected to synaptic bombardment exhibits a minimum as the spike transmission probability varies. We also demonstrate that the occurrence of ISR strongly depends on the synaptic input regime, where it is most prominent in the balanced state of excitatory and inhibitory inputs.

Interspike intervals under the constraint of linear synaptic integration and background synaptic activity

Previous work [1] using a one-compartment steady-state computer model of a neuron shows how combinations of two active dendritic conductances can produce a linear dendritic response over a voltage range of ca. 15 to 22 mV. Here we extend this linearization issue to a dynamic, non-steady state situation for a neuron model with a soma, dendrite and distal compartment. It is known that synaptically-induced voltage pulses that originate in distal compartments will be both attenuated and broader by the time they reach the soma due to the passive cable properties of neurons. Additionally, neurons in intact brains are not isolated but are under constant synaptic bombardment. While a single synaptic event will not significantly affect the cell properties, thousands of such events will significantly change the integrative properties of neurons [2]. Due to the reduction in membrane resistance caused by synaptic activity, attenuation of individual voltage pulses is even stronger in the presence of background synaptic activity. This study shows that the presence of active conductances distributed along the cell membrane can mitigate some of the effects of electrical filtering on voltage pulses propagating to the soma, even in the presence of background synaptic activity. We consider three uniformly distributed conductances found in pyramidal cells of the forebrain: persistent sodium current (INaP) [3,4], hyperpolarization-activated mixed cation current (Ih) [5], and A-type potassium current (IKA) [6]. Added to an otherwise passive cell, select combinations of these voltage-activated ion channels can generate linear synaptic integration over the entire interspike interval (ISI) membrane potential range, that is from rest (here -72 mV) to threshold (here roughly -45 mV). In the presence of various levels of background synaptic activity, the ratio of these active conductances changes and contributes to the inverse ISI effect. Under the constraint of linear synaptic integration, the length of the ISI is seen to be inversely related to the intensity of the background synaptic activity. This inverse relationship results from a decrease of the effective time constant of the cell by increased levels of membrane conductance induced by the combination of synaptic activity and active conductances.