Biology subjects

Rinzel, J.

Publications and source records attributed to Rinzel, J..

10 recordsLinked to original sources

bioRxiv2024

A Recurrent Neural Network for Rhythmic Timing

Despite musics omnipresence, the specific neural mechanisms responsible to perceive and anticipate temporal patterns in music are unknown. To study potential mechanisms for keeping time in rhythmic contexts, we train a biologically constrained RNN on seven different stimulus tempos (2 - 8Hz) on a synchronization and continuation task, a standard experimental paradigm. Our trained RNN generates a network oscillator that uses an input current (context parameter) to control oscillation frequency and replicates key features of neural dynamics observed in neural recordings of monkeys performing the same task. We develop a reduced three-variable rate model of the RNN and analyze its dynamic properties. By treating our understanding of the mathematical structure for oscillations in the reduced model as predictive, we confirm that the dynamical mechanisms are found also in the RNN. Our neurally plausible reduced model reveals an E-I circuit with two distinct inhibitory sub-populations, of which one is tightly synchronized with the excitatory units.

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bioRxiv2024

Perceptual tri-stability, measured and fitted as emergent from a model for bistable alternations

The human auditory system in attempting to decipher ambiguous sounds appears to resort to perceptual exploration as evidenced by multi-stable perceptual alternations. This phenomenon has been widely investigated via the auditory streaming paradigm, employing ABA_ triplet sequences with much research focused on perceptual bi-stability with the alternate percepts as either a single integrated stream or as two simultaneous distinct streams. We extend this inquiry with experiments and modeling to include tri-stable perception. Here, the segregated percepts may involve a foreground/background distinction. We collected empirical data from participants engaged in a tri-stable auditory task, utilizing this dataset to refine a neural mechanistic model that had successfully reproduced multiple features of auditory bi-stability. Remarkably, the model successfully emulated basic statistical characteristics of tri-stability without substantial modification. This model also allows us to demonstrate a parsimonious approach to account for individual variability by adjusting the parameter of either the noise level or the neural adaptation strength.

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bioRxiv2023

An E-I Firing Rate Model for Gamma Oscillations

Firing rate models for describing the mean-field activities of neuronal ensembles can be used effectively to study network function and dynamics, including synchronization and rhythmicity of excitatory-inhibitory populations. However, traditional Wilson-Cowan-like models, being amenable to mathematical analysis, are found unable to capture some dynamics such as Interneuronal Network Gamma oscillations (ING) although use of an explicit delay can help. We resolve this issue by introducing a mean-voltage variable (v) that considers the subthreshold integration of inputs and works as an effective delay in the negative feedback loop between firing rate (r) and synaptic gating of inhibition (s). Here we describe a three-variable r-s-v firing rate model for I-I networks, which is biophysically interpretable and capable of generating ING-like oscillations. Linear stability analysis, numerical branch-tracking and simulations show that the rate model captures many of the common features of spiking network models for ING. We also propose an alternative r-u-s model for ING which considers an implicit delay by a pre-synaptic variable u. Furthermore, we extend the framework to E-I networks. With our six-variable r-s-v model, we describe for the first time in firing rate models the transition from Pyramidal-Interneuronal Network Gamma (PING) to ING by increasing the external drive to the inhibitory population without adjusting synaptic weights. Having PING and ING available in a single network, without invoking synaptic blockers, is plausible and natural for explaining the emergence and transition of two different types of gamma oscillations. Author SummaryGamma rhythm (30-90 Hz) is a neuronal oscillation broadly observed across various animal species. It is associated with cognitive functions and gamma oscillation dysfunction has been found in neurological diseases. Traditional studies of gamma oscillations are primarily conducted in detailed but computational demanding spiking network models. Motivated by the growing size of accessible datasets and large-scale modeling, we develop mean-field representations of gamma oscillations that are computationally lightweight and mathematically analyzable. We resolve the incapability of generating oscillation in inhibitory network with traditional Wilson-Cowan firing rate model by introducing implicit delay with a mean membrane potential variable and generalize the model for excitatory-inhibitory network to capture richer dynamics. Our work provides a potential building block for multi-area large-scale modeling to help understanding the functional roles of circuits at different levels.

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bioRxiv2022

Synaptic homeostasis transiently leverages Hebbian mechanisms for a multiphasic response to inactivity

Neurons use various forms of negative feedback to maintain their synaptic strengths within an operationally useful range. While this homeostatic plasticity is thought to distinctly counteract the destabilizing positive feedback of Hebbian plasticity, there is considerable overlap in the molecular components mediating both forms of plasticity. The varying kinetics of these components spurs additional inquiry into the dynamics of synaptic homeostasis. We discovered that upscaling of synaptic weights in response to prolonged inactivity is nonmonotonic. Surprisingly, this seemingly oscillatory adaptation involved transient appropriation of molecular effectors associated with Hebbian plasticity, namely CaMKII, L-type Ca2+ channels, and Ca2+-permeable AMPARs, and homeostatic elements such as calcineurin. We created a dynamic model that shows how traditionally "Hebbian" and "homeostatic" mechanisms can cooperate to autoregulate postsynaptic Ca2+ levels. We propose that this combination of mechanisms allows excitatory synapses to adapt to prolonged activity changes and safeguard the capability to undergo future strengthening on demand.

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bioRxiv2021

Distinct ground state and activated state modes of spiking in forebrain neurons

Neuronal firing patterns have significant spatiotemporal variability with no agreed upon theoretical framework. Using a combined experimental and modeling approach, we found that spike interval statistics can be described by discrete modes of activity. Of these, a "ground state" (GS) mode of low-rate spiking is universal among forebrain excitatory neurons and characterized by irregular spiking at neuron-specific rates. In contrast, "activated state" (AS) modes consist of spiking at characteristic timescales and regularity that are specific to neuron populations in a given region and brain state. The majority of spiking is contributed by GS mode, while neurons can transiently switch to AS spiking in response to stimuli or in coordination with population activity patterns. We hypothesize that GS spiking serves to maintain a persistent backbone of neuronal activity while AS modes support communication functions.

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bioRxiv2020

Buildup and bistability in auditory streaming as an evidence accumulation process with saturation

A repeating triplet-sequence ABA_ of non-overlapping brief tones, A and B, is a valued paradigm for studying auditory stream formation and the cocktail party problem. The stimulus is "heard" either as a galloping pattern (integration) or as two interleaved streams (segregation); the initial percept is typically integration then followed by spontaneous alternations between segregation and integration, each being dominant for a few seconds. The probability of segregation grows over seconds, from near-zero to a steady value, defining the buildup function, BUF. Its stationary level increases with the difference in tone frequencies, DF, and the BUF rises faster. Percept durations have DF -dependent means and are gamma-like distributed. Behavioral and computational studies usually characterize triplet streaming either during alternations or during buildup. Here, our experimental design and modeling encompass both. We propose a pseudo-neuromechanistic model that incorporates spiking activity in primary auditory cortex, A1, as input and resolves perception along two network-layers downstream of A1. Our model is straightforward and intuitive. It describes the noisy accumulation of evidence against the current percept which generates switches when reaching a threshold. Accumulation can saturate either above or below threshold; if below, the switching dynamics resemble noise-induced transitions from an attractor state. Our model accounts quantitatively for three key features of data: the BUFs, mean durations, and normalized dominance duration distributions, at various DF values. It describes perceptual alternations without competition per se, and underscores that treating triplets in the sequence independently and averaging across trials, as implemented in earlier widely cited studies, is inadequate. Author summarySegregation of auditory objects (auditory streaming) is widely studied using ambiguous stimuli. A sequence of repeating triplets ABA_ of non-overlapping brief pure tones, A and B, frequency-separated, is a valued stimulus. Studies typically focus on one of two behavioral phases: the early (say, ten seconds) buildup of segregation from the default integration or later spontaneous alternations (bistability) between seconds-long integration and segregation percepts. Our experiments and modeling encompass both. Our novel, data-driven, evidence-accumulation model accounts for key features of the observations, taking as input recorded spiking activity from primary auditory cortex (as opposed to most existing, more abstract, models). Our results underscore that assessing individual triplets independently and averaging across trials, as in some earlier studies, is inadequate (lacking neuronal-accountability for percept duration statistics, the underlying basis of buildup). Further, we identify fresh parallels between evidence accumulation and competition as potential dynamic processes for choice in the brain.

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bioRxiv2018

Soma-axon coupling configurations that enhance neuronal coincidence detection

Coincidence detector neurons transmit timing information by responding preferentially to concurrent synaptic inputs. Principal cells of the medial superior olive (MSO) in the mammalian auditory brainstem are superb coincidence detectors. They encode sound source location with high temporal precision, distinguishing submillisecond timing differences among inputs. We investigate computationally how dynamic coupling between the \"input\" region (soma and dendrite) and the spike-generating \"output\" region (axon and axon initial segment) can enhance coincidence detection in MSO neurons. To do this, we formulate a two-compartment neuron model and characterize extensively coincidence detection sensitivity throughout a parameter space of coupling configurations. We focus on the interaction between coupling configuration and two currents that provide dynamic, voltage-gated, negative feedback in subthreshold voltage range: sodium current with rapid inactivation and low-threshold potassium current, IKLT. These currents reduce synaptic summation and can prevent spike generation unless inputs arrive with near simultaneity. We show that strong soma-to-axon coupling promotes the negative feedback effects of sodium inactivation and is, therefore, advantageous for coincidence detection. Furthermore, the \"feedforward\" combination of strong soma-to-axon coupling and weak axon-to-soma coupling enables spikes to be generated efficiently (few sodium channels needed) and with rapid recovery that enhances high-frequency coincidence detection. These observations detail the functional benefit of the strongly feedforward configuration that has been observed in physiological studies of MSO neurons. We find that IKLT further enhances coincidence detection sensitivity, but with effects that depend on coupling configuration. For instance, in weakly-coupled models, IKLT in the spike-generator compartment enhances coincidence detection more effectively than IKLT in the input compartment. By using a minimal model of soma-to-axon coupling, we connect structure, dynamics, and computation. Here, we consider the particular case of MSO coincidence detectors. In principle, our method for creating and exploring a parameter space of two-compartment models can be applied to other neurons.\n\nAuthor summaryBrain cells (neurons) are spatially extended structures. The locations at which neurons receive inputs and generate outputs are often distinct. We formulate and study a minimal mathematical model that describes the dynamical coupling between the input and output regions of a neuron. We construct our model to reflect known properties of neurons in the auditory brainstem that play an important role in our ability to locate sound sources. These neurons are known as \"coincidence detectors\" because they are most likely to respond when they receive simultaneous inputs. We use simulations to explore coincidence detection sensitivity throughout the parameter space of input-output coupling and to identify the coupling configurations that are best for neural coincidence detection. We find that strong forward coupling (from input region to output region), enhances coincidence detection sensitivity in our model and that low-threshold potassium current further improves coincidence detection. Our study is significant in that we detail how cell structure affects neuronal dynamics and, consequently, the ability of neurons to perform as temporally-precise coincidence detectors.

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bioRxiv2018

A neuromechanistic model for rhythmic beat generation

When listening to music, humans can easily identify and move to the beat. Numerous experimental studies have identified brain regions that may be involved with beat perception and representation. Several theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat, is the question of how we learn to generate and hold a beat. In this paper, we introduce a neuronal framework for a beat generator that is capable of learning isochronous rhythms over a range of frequencies that are relevant to music and speech. Our approach combines ideas from error-correction and entrainment models to investigate the dynamics of how a biophysically-based neuronal network model synchronizes its period and phase to match that of an external stimulus. The model makes novel use of on-going faster gamma rhythms to form a set of discrete clocks that provide estimates, but not exact information, of how well the beat generator spike times match those of a stimulus sequence. The beat generator is endowed with plasticity allowing it to quickly learn and thereby adjust its spike times to achieve synchronization. Our model makes generalizable predictions about the existence of asymmetries in the synchronization process, as well as specific predictions about resynchronization times after changes in stimulus tempo or phase. Analysis of the model demonstrates that accurate rhythmic time keeping can be achieved over a range of frequencies relevant to music, in a manner that is robust to changes in parameters and to the presence of noise. Author summaryMusic is integral to human experience and is appreciated across a wide range of cultures. Although many features distinguish different musical traditions, rhythm is central to nearly all. Most humans can detect and move along to the beat through finger or foot tapping, hand clapping or other bodily movements. But many people have a hard time "keeping a beat", or say they have "no sense of rhythm". There appears to be a disconnect between our ability to perceive a beat versus our ability to produce a beat, as a drummer would do as part of a musical group. Producing a beat requires beat generation, the process by which we learn how to keep track of the specific time intervals between beats, as well as executing the motor movement needed to produce the sound associated with a beat. In this paper, we begin to explore neural mechanisms that may be responsible for our ability to generate and keep a beat. We develop a computational model that includes different neurons and shows how they cooperate to learn a beat and keep it, even after the stimulus is removed, across a range of frequencies relevant to music. Our dynamical systems model leads to predictions for how the brain may react when learning a beat. Our findings and techniques should be widely applicable to those interested in understanding how the brain processes time, particularly in the context of music.

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bioRxiv2018

Excitable dynamics of NREM sleep: a unifying model for neocortex and hippocampus

During non-rapid eye movement (NREM) sleep, the neocortex and hippocampus alternate between periods of neuronal spiking and inactivity. By directly comparing experimental observations with a mean field model of an adapting, recurrent neuronal population, we find that the neocortical alternations reflect a dynamical regime in which a stable active state is interrupted by transient inactive states (slow waves) while the hippocampal alternations reflect a stable inactive state interrupted by transient active states (sharp waves). We propose that during NREM sleep, hippocampal and neocortical populations are excitable: each in a stable state from which internal fluctuations or external perturbation can evoke the stereotyped population events that mediate NREM functions.

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bioRxiv2016

Sleep regulation of the distribution of cortical firing rates

Sleep is thought to mediate mnemonic and homeostatic functions. However, the mechanism by which this brain state can implement both the \"selective\" plasticity needed to consolidate novel memory traces as well as the \"general\" plasticity necessary to maintain a well-functioning neuronal system is unclear. Recent findings show that both of these functions differentially affect neurons based on their intrinsic firing rate, a ubiquitous neuronal heterogeneity. Furthermore, they are both implemented by the NREM slow oscillation, which also distinguishes neurons based on firing rate during sequential activity at the DOWN->UP transition. These findings suggest a mechanism by which spiking activity during the slow oscillation acts to maintain network statistics that promote a skewed distribution of neuronal firing rates, and \"perturbation\" of that activity by hippocampal replay acts to integrate new memory traces into the existing cortical network.

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