Biology subjects

Zochowski, M.

Publications and source records attributed to Zochowski, M..

8 recordsLinked to original sources

bioRxiv2026

Impacts of heminode disruption on auditory processing of noisy sound stimuli

Hidden hearing loss (HHL) is an auditory neuropathy characterized by altered auditory nerve responses despite normal hearing thresholds. Recent experimental and computational studies suggest that permanent disruptions to heminode positions in spiral ganglion neuron (SGN) fibers can contribute to these deficits. However, the interaction between heminode disruption and noisy backgrounds ubiquitous in daily listening remains unexplored. This study investigates how background noise affects auditory processing with these peripheral disorders and how deficits propagate to downstream sound localization circuits in the superior olivary complex. We developed computational models of SGN fibers with mild and severe degrees of heminode disruption, subjected to sinusoidal tone stimuli in the presence of background noise with varying spectral characteristics. We analyzed the phase-locking of SGN fiber responses to the stimulus tone and modeled the subsequent effects on interaural time difference (ITD) sensitivity in the medial superior olive (MSO) using a binaural localization network. We found that near-tone-frequency noise disrupted SGN phase locking through cycle-to-cycle variability in spike phases, with effects consistent across tone frequencies. Mild heminode disruption produced frequency-dependent degradation in SGN phase locking, with effects observed only at higher frequencies tested (600-1000 Hz), without reducing overall firing rates. Critically, the effects of noise and heminode disruption were additive, with combined exposure leading to reduced ITD sensitivity and large temporal fluctuations in MSO responses. Severe heminode disruption, which additionally reduced firing rates at the SGN fibers and subsequent stages, produced profound localization deficits across all frequencies tested. Thus, our model results suggest that noisy environments exacerbate auditory deficits from peripheral disorders implicated in HHL and could potentially impair speech intelligibility through degradation in localization ability. This model may be useful for understanding the downstream impacts of SGN neuropathies.

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bioRxiv2025

Modular inhibitory coding in binary networks

We developed and characterized properties of new class of binary models where, as observed in biological networks, excitatory neurons are functionally separated from inhibitory units. New patterns, represented as activation and inactivation of binary units in excitatory layer, are stored in the network through recruitment and training of inhibitory units that are group into individual modules and interact with excitatory layer. We investigate the roles the two populations play in memory storage and show that inhibitory layer plays a critical role in memory storage and management and that capacity of this new type of network scales with number of inhibitory neurons. Further, we show that performance of the network is only gradually diminished when excitatory to excitatory connections are removed, but critically depends on inhibitory to excitatory connections. These results are in line with new experimental work showing that inhibitory interneurons are playing critical role in memory storage and recall in the brain networks and may also address why generally excitatory networks exhibit sparser reciprocal connectivity as compared to connections to/from inhibitory units. We further show advantages of so designed coding shame in terms of memory capacity, its expansion with progressive storage of new memories as well as network behavior for large memory loading.

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bioRxiv2024

Neuromodulatory effects on synchrony and network reorganization in networks of coupled Kuramoto oscillators.

Neuromodulatory processes in the brain can critically change signal processing on a cellular level leading to dramatic changes in network level reorganization. Here, we use coupled non-identical Kuramoto oscillators to investigate how changes in the shape of phase response curves from Type 1 to Type 2, mediated by varying ACh levels, coupled with activity dependent plasticity may alter network reorganization. We first show that when plasticity is absent, the Type 1 networks, as expected, exhibit asynchronous dynamics with oscillators of the highest natural frequency robustly evolving faster in terms of their phase dynamics. At the same time, the Type 2 networks synchronize, with oscillators locked so that the ones with higher natural frequency have a constant phase lead as compared to the ones with lower natural frequency. This relationship establishes a robust mapping between the frequency and oscillators phases in the network, leading to structure/frequency mapping when plasticity is present. Further we show that while connection plasticity can produce stable synchrony (so called splay states) in Type 1 networks, the structure/frequency reorganization observed in Type 2 networks is not present.

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bioRxiv2023

Distinct roles for sequential NREM and REM acetylcholine-regulated states during sleep-dependent memory consolidation

Across vertebrate species, sleep consists of repeating cycles of NREM followed by REM. However, the respective functions of NREM, REM, and their stereotypic cycling pattern are not well understood. Using a simplified biophysical network model, we show that NREM and REM sleep can play differential and critical roles in memory consolidation primarily regulated, based on state-specific changes in cholinergic signaling. Within this network, decreasing and increasing muscarinic acetylcholine (ACh) signaling during bouts of NREM and REM, respectively, differentially alters neuronal excitability and excitatory/inhibitory balance. During NREM, deactivation of inhibitory neurons leads to network-wide disinhibition and bursts of synchronized activity led by firing in engram neurons. These features strengthen connections from the original engram neurons to less-active network neurons. In contrast, during REM, an increase in network inhibition suppresses firing in all but the most-active excitatory neurons, leading to competitive strengthening/pruning of the memory trace. We tested the predictions of the model against in vivo recordings from mouse hippocampus during active sleep-dependent memory storage. Consistent with modeling results, we find that functional connectivity between CA1 neurons changes differentially at transition from NREM to REM sleep during learning. Returning to the model, we find that an iterative sequence of state-specific activations during NREM/REM cycling is essential for memory storage in the network, serving a critical role during simultaneous consolidation of multiple memories. Together these results provide a testable mechanistic hypothesis for the respective roles of NREM and REM sleep, and their universal relative timing, in memory consolidation. Significance statementUsing a simplified computational model and in vivo recordings from mouse hippocampus, we show that NREM and REM sleep can play differential roles in memory consolidation. The specific neurophysiological features of the two sleep states allow for expansion of memory traces (during NREM) and prevention of overlap between different memory traces (during REM). These features are likely essential in the context of storing more than one new memory simultaneously within a brain network.

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bioRxiv2021

Modeling Synaptic Effects of Anesthesia and its Cortical Cholinergic Reversal

General anesthetics work through a variety of molecular mechanisms while resulting in the common end point of sedation and loss of consciousness. Generally, the administration of common inhalation anesthetics induces decreases in synaptic excitation while promoting synaptic inhibition. Animal studies have shown that, during anesthesia, exogenously induced increases in acetylcholine-mediated effects in the brain can elicit wakeful-like behavior despite the continued presence of the anesthetic. Less investigated, however, is the question of whether the brains electrophysiological activity is also restored to pre-anesthetic levels and quality by such interventions. Here we apply a computational model of a network composed of excitatory and inhibitory neurons to simulate the network effects of changes in synaptic inhibition and excitation due to anesthesia and its reversal by muscarinic receptor-mediated cholinergic effects. We use a differential evolution algorithm to fit model parameters to match measures of spiking activity, neuronal connectivity, and network dynamics recorded in the visual cortex of rodents during anesthesia with desflurane in vivo. We find that facilitating muscarinic receptor effects of acetylcholine on top of anesthetic-induced synaptic changes predicts reversal of the neurons spiking activity, functional connectivity, as well as pairwise and population interactions. Thus, our model results predict a possible neuronal mechanism for the induced reversal of the effects of anesthesia on post synaptic potentials, consistent with experimental behavioral observations. Author SummaryHere, we apply a computational model of a network composed of excitatory and inhibitory neurons to simulate the network effects of changes in synaptic inhibition and excitation due to anesthesia and we investigate the possibility of its reversal by muscarinic receptor-mediated cholinergic effects. Specifically, we use a differential evolution algorithm to fit model parameters to match dynamics recorded in the visual cortex of rodents during anesthesia with desflurane in vivo. We find that changes of the fitted synaptic parameters in response to the increasing desflurane concentration matched those established by neurophysiology. Further, our results demonstrate that the cellular effects induced by anesthesia can be mitigated by the changes in cellular excitability due to acetylcholine.

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bioRxiv2020

Contrasting mechanisms for hidden hearing loss: synaptopathy vs myelin defects

Hidden hearing loss (HHL) is an auditory neuropathy characterized by normal hearing thresholds but reduced amplitude of the sound-evoked auditory nerve compound action potential (CAP). It has been proposed that in humans HHL leads to speech discrimination and intelligibility deficits, particularly in noisy environments. Animal models originally indicated that HHL can be caused by moderate noise exposures or aging, and that loss of inner hair cell (IHC) synapses could be its cause. A recent study provided evidence that transient loss of cochlear Schwann cells also causes permanent auditory deficits in mice which have characteristics of HHL. Histological analysis of the cochlea after auditory nerve remyelination showed a permanent disruption of the myelination patterns at the heminode of type I spiral ganglion neuron (SGN) peripheral terminals, suggesting that this defect could be contributing to HHL. To shed light on the mechanisms of different HHL scenarios and to test their impact on type I SGN activity, we constructed a reduced biophysical model for a population of SGN peripheral axons. We found that the amplitudes of simulated sound-evoked SGN CAPs are lower and have greater latencies when the heminodes are disorganized, i.e. they are placed at different distances from the hair cell rather than at the same distance as seen in the normal cochlea. Thus, our model confirms that disruption of the position of the heminode causes desynchronization of SGN spikes leading to a loss of temporal resolution and reduction of the sound-evoked SGN CAP. We also simulated synaptopathy by removing high threshold IHC-SGN synapses and found that the amplitude of simulated sound-evoked SGN CAPs decreases while latencies remain unchanged, corresponding to what has been observed in noise exposed animals. This model can be used to further study the effects of synaptopathy or demyelination on auditory function. Author summaryHidden hearing loss is an auditory disorder caused by noise exposure, aging or peripheral neuropathy which is estimated to affect 12-15% of the worlds population. It is a hidden disorder because subjects have normal hearing thresholds, i.e., the condition cannot be revealed by standard audiological tests, but they report difficulties in understanding speech in noisy environments. Studies on animal models suggest two possible pathogenic mechanisms for hidden hearing loss: (1) loss of synapses between inner hair cells and auditory nerve fibers, and (2) disruption of auditory-nerve myelin. In this study, we constructed a computational model of sound-evoked auditory neuron fiber activity and auditory nerve compound action potential to understand how each one of these mechanisms affects nerve transmission. We show that disruption of auditory-nerve myelin desynchronizes sound-evoked auditory neuron spiking, decreasing the amplitude and increasing the latency of the compound action potential. In addition, elongation of the initial axon segment may cause spike generation failure leading to decreased spiking probability. In contrast, the effect of synapse loss is only to decrease the probability of firing, thus reducing the compound action potential amplitude without disturbing its latency. This model, which accurately represents the in vivo findings, could be useful to make further predictions on the consequences of HHL and extend it to explore the impact of synaptopathy and myelinopathy on hearing.

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bioRxiv2020

Non-REM sleep facilitates consolidation of contextual fear memory through temporal coding among hippocampal neurons

Sleep plays a critical role in memory consolidation, although the exact mechanisms mediating this process are unknown. Combining computational and in vivo experimental approaches, we test the hypothesis that reduced cholinergic input to the hippocampus during non-rapid eye movement (NREM) sleep generates stable spike timing relationships between neurons. We find that the order of firing among neurons during a period of NREM sleep reflects their relative firing rates during prior wake, and changes as a function of prior learning. We show that learning-dependent pattern formation (e.g. "replay") in the hippocampus during NREM, together with spike timing dependent plasticity (STDP), restructures network activity in a manner similar to that observed in brain circuits across periods of sleep. This suggests that sleep actively promotes memory consolidation by switching the network from rate-based to firing phase-based information encoding.

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bioRxiv2019

Network resonance during slow-wave sleep facilitates memory consolidation through phase-coding

Sleep plays a critical role in memory consolidation, however, the exact role that sleep and its effects on neural network dynamics play in this process is still unclear. Here, we combine computational and experimental approaches to study the dynamical, network-wide underpinnings of hippocampal memory consolidation during sleep. We provide data to support a novel hypothesis on the role of cellular resonance with sleep-associated theta band (4-12 Hz) hippocampal oscillations in this process. We show that increases in the stability of hippocampal memory representations after learning (which predicts successful memory consolidation) are mediated through emergent network-wide resonance and locking of neuronal activity to network oscillations. These changes arise in the network as a function of changes to network structure during learning, and mirror experimental findings in the hippocampus. Finally, we show that input-dependent pattern formation (e.g. "replay") in the hippocampus during sleep states, together with spike timing dependent plasticity (STDP)-based memory consolidation, leads to universal network activity reorganization. This reorganization generates heterogeneous changes in neuronal spiking frequency, similar to what has been observed in a variety of brain circuits across periods of sleep. Our results support the hypothesis that sleep plays an active role in memory consolidation by switching the hippocampal network from rate-based to phase-based information representation. The mechanisms through which this occurs supports the integration of heterogeneous cell populations into memory traces. Significance StatementIn this study, we provide a mechanistic explanation of how sleep selectively facilitates memory consolidation, through recruitment of heterogeneous neuronal populations and structural reorganization of the network into an engram. Specifically, we show that emergent theta band oscillations during sleep facilitate stabilization of memory representations via spike timing dependent reinforcement. This stabilization, together with STDP, allows for systematic reorganization of synaptic connections within these populations, universally redistributing firing rates of participating neurons. Simultaneously, network oscillations facilitate a switch from rate-to phase-coding of information among neuronal populations with highly heterogenous firing frequencies, incorporating more neurons into the engram. Our results reconcile discrepant findings on network reorganization during sleep, and demonstrate a clear mechanism for both strengthening and weakening of synaptic efficacy during sleep.

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