Biology subjects

Shouval, H.

Publications and source records attributed to Shouval, H..

6 recordsLinked to original sources

bioRxiv2026

Self-Organized Neural Integrators in Noisy Spiking Networks

AO_SCPLOWBSTRACTC_SCPLOWNeural integrators convert brief inputs into persistent firing and underlie functions such as working memory, evidence accumulation, and gaze holding. Classical integrator models typically rely on finely tuned recurrent connectivity. Here we identify a biologically plausible route by which randomly connected noisy spiking networks can approximate integration over a finite region of parameter space. Mean-field theory (MFT) reveals surprisingly simple dynamics in such networks, governed by the mean recurrent weight and mean feedforward weight, and shows that linear integration critically depends on noise. We further show that this regime can be reached through a local, reward-modulated two-trace plasticity rule. Comparing the model with new experimental results from a delay-switching tactile decision-making task, we find that it reproduces key features of adaptive ramp-to-threshold cortical dynamics during timing-related learning. The same framework further connects to oculomotor persistence and evidence accumulation, providing a mechanistic realization of single-boundary drift-diffusion dynamics.

neuroscience↗

bioRxiv2026

PKMζ-KIBRA interactions, molecular turnover, and memory

How can the molecules that strengthen synaptic connections maintain memory in the face of molecular turnover? Our previous work showed that persistent interaction between the postsynaptic scaffolding protein, KIBRA, and the autonomously active PKC isoform, PKM{zeta}, is crucial for maintaining synaptic long-term potentiation (LTP) and memory for at least a month. This duration is longer than the lifespans of individual KIBRA and PKM{zeta} molecules. Biophysical modeling of the interaction suggests oligomers of KIBRA-PKM{zeta} dimers, but not individual dimers or monomers, can overcome molecular turnover by continually incorporating newly synthesized KIBRA and PKM{zeta}, replacing those that have degraded. Here we used AlphaFold 3 to predict the structures of KIBRA-PKM{zeta} heterodimers and heterohexamers and to examine the sites of action of two structurally distinct inhibitors of KIBRA-PKM{zeta} interaction that disrupt established late-LTP and long-term memory. The structures predict that the peptide K-ZAP blocks formation of heterodimers, whereas the small molecule {zeta}-stat prevents PKM{zeta} of one heterodimer from binding a second KIBRA and PKM{zeta}, essential for forming larger oligomeric structures. We show that {zeta}-stat, like K-ZAP, disrupts 1-month-old spatial memory. Thus, continual formation of KIBRA-PKM{zeta} oligomers can be a core molecular mechanism driving the persistence of long-term memory in the face of molecular turnover.

neuroscience↗

bioRxiv2024

Maintenance of synaptic plasticity by negative-feedback of synaptic protein elimination: Dynamic modeling of KIBRA-PKMζ interactions in LTP and memory

Activity-dependent modifications of synaptic efficacies are a cellular substrate of learning and memory. Current theories propose that the long-term maintenance of synaptic efficacies and memory is accomplished via a positive-feedback loop at the level of production of a protein species or a protein state. Here we propose a qualitatively different theoretical framework based on negative feedback at the level of protein elimination. This theory is motivated by recent experimental findings regarding the binding of PKM{zeta} and KI-BRA, two synaptic proteins involved in maintenance of memory, and on how this binding downregulates the proteins degradation. We demonstrate this theoretical framework with two different models. First, a simple abstract model to explore generic features of the negative-feedback process. Second, a biophysical model based on PKM{zeta}-KIBRA dimers that cooperatively form larger complexes at active synapses. These larger complexes have slower degradation and diffusion, allowing for bistability of potentiated and unpotentiated synaptic states. The results of these models are qualitatively consistent with existing experiments showing reversal of long-term potentiation and erasure of long-term memory by inhibition of KIBRA-PKM{zeta} interactions. The theory generates novel predictions that could be experimentally tested to further validate or reject the negative-feedback theory.

neuroscience↗

bioRxiv2022

Learning and Expression of Dopaminergic Reward Prediction Error via Plastic Representations of Time

The dominant theoretical framework to account for reinforcement learning in the brain is temporal difference (TD) reinforcement learning. The normative motivation for TD theory is that the brain needs to learn about expected future rewards in order to learn how to maximize these rewards. The TD framework predicts that some neuronal elements should represent the reward prediction error (RPE), which means they signal the difference between the expected future rewards and the actual rewards. What makes the TD learning theory so prominent is that the firing properties of dopaminergic neurons in the ventral tegmental area (VTA) appear similar to those of RPE model-neurons in TD learning. Biologically plausible implementations of TD learning assume a fixed temporal basis for each stimulus that might eventually predict a reward. Here we show on the basis of first principles that such a fixed temporal basis is implausible. We also show that certain predictions of TD learning are inconsistent with experimental data. We propose instead an alternative theoretical framework, coined FLEX (Flexibly Learned Errors in Expected Reward). In FLEX, feature specific representations of time are learned, allowing for neural representations of stimuli to adjust their timing and relation to rewards in an online manner. As an indirect consequence, dopamine in FLEX resembles, but is not equivalent to RPE. In FLEX dopamine acts as an instructive signal which helps build temporal models of the environment. FLEX is a general theoretical framework that has many possible biophysical implementations. In order to show that FLEX is a feasible approach, we present a specific biophysically plausible model which implements the principles of FLEX. We show that this implementation can account for various reinforcement learning paradigms, and that its results and predictions are consistent with a preponderance of both existing and reanalyzed experimental data.

neuroscience↗

bioRxiv2020

Learning precise spatiotemporal sequences via biophysically realistic circuits with modular structure

The ability to express and learn temporal sequences is an essential part of learning and memory. Learned temporal sequences are expressed in multiple brain regions and as such there may be common design in the circuits that mediate it. This work proposes a substrate for such representations, via a biophysically realistic network model that can robustly learn and recall discrete sequences of variable order and duration. The model consists of a network of spiking leaky-integrate-and-fire model neurons placed in a modular architecture designed to resemble cortical microcolumns. Learning is performed via a learning rule with "eligibility traces", which hold a history of synaptic activity before being converted into changes in synaptic strength upon neuromodulator activation. Before training, the network responds to incoming stimuli, and contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. This model provides a possible framework for biologically realistic sequence learning and memory, and is in agreement with recent experimental results, which have shown sequence dependent plasticity in sensory cortex.

neuroscience↗

bioRxiv2018

Network dynamics of Broca’s area during word selection

Current models of word-production in Brocas area posit that sequential and staggered semantic, lexical, phonological and articulatory processes precede articulation. Using millisecond-resolution intra-cranial recordings, we evaluated spatiotemporal dynamics and high frequency functional interconnectivity between ventro-lateral prefrontal regions during single-word production. Through the systematic variation of retrieval, selection, and phonological loads, we identified specific activation profiles and functional coupling patterns between these regions that fit within current psycholinguistic theories of word production. However, network interactions underpinning these processes activate in parallel (not sequentially), while the processes themselves are indexed by specific changes in network state. We found evidence that suggests that pars orbitalis is coupled with pars triangularis during lexical retrieval, while lexical selection in Brocas area is terminated via coupled activity with M1 at articulation onset. Taken together, this work reveals that speech production relies on very specific inter-regional couplings in rapid sequence in the language dominant hemisphere.

neuroscience↗