Biology subjects

Tyulmankov, D.

Publications and source records attributed to Tyulmankov, D..

5 recordsLinked to original sources

bioRxiv2025

Switches from tonic to burst firing enable memory consolidation through late-phase synaptic plasticity

Neural circuits routinely alternate between input-driven tonic activity and collective burst firing. In the presence of Hebbian plasticity, bursts generate a robust attractor in weight space, creating a built-in drift that can be repurposed into a stabilizing trace of prior learning. We show that this phenomenon can be harnessed for memory consolidation through the introduction of a two-stage synaptic rule. The effective synaptic weight is defined as the product of a primary weight--updated by a Hebbian rule during both tonic and burst periods--and a secondary weight that updates in proportion with a coupling gain to the negative time-derivative of the primary weight. In a MNIST-like task, alternating tonic and burst epochs preserves earlier patterns, improves generalization to unseen inputs, and resists interference and noise, whereas replacing burst by quiescence or additional tonic epochs does not. Parameter sweeps reveal that coupling gain and the initial synaptic weights control whether bursts consolidate ("up-selection") or prune ("down-selection") synapses. Pairing the rule with alternative primary plasticity models yields distinct treatments of overlapping inputs, enabling either integration or separation. Studying switches in firing activity with a two-stage synaptic plasticity provides a plausible route to consolidation in biological and neuromorphic networks. Significance StatementNeural circuits alternate between tonic spiking and burst firing, yet most models of synaptic plasticity are limited to a single firing regime. We introduce a two--stage synaptic rule in which a primary weight encodes activity during both states, while a secondary weight--engaged only during bursts-- stabilizes learning from tonic periods. In conductance-based networks and a pattern recognition task, this rule preserves memories, improves generalization, and resists interference, whereas quiescence or extended tonic activity do not. The model further shows that bursts can consolidate or prune synapses depending on coupling gain and initial conditions. These findings identify a plausible, biologically motivated mechanism for how activity state transitions shape memory consolidation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=160 HEIGHT=200 SRC="FIGDIR/small/648539v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@7a8922org.highwire.dtl.DTLVardef@c13425org.highwire.dtl.DTLVardef@46a505org.highwire.dtl.DTLVardef@1fc0820_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

Computational Primitives for Cost-Benefit Decision-Making

Cost-benefit decision-making is a critical process performed by all organisms, including humans. Various factors, including risk1,2, uncertainty3, age4, sex5, and neuropsychiatric disorders6, can alter decision-making. To explore cost-benefit decision-making in humans, we developed a comprehensive task and analysis framework that presents participants with a series of approach-avoid trade-offs across a variety of contexts. With this system, we found that cost-benefit decisions in humans are made using a set of computational strategies that may be used for integrating costs and rewards, which we term decision-making primitives. We further show that these decision-making primitives are used by rodents performing a similar decision-making task7. We find that utilization of these primitives in both rodents and humans shifts based on factors like hunger and sex, and that individuals use primitives differently. We additionally demonstrate that using a naturally-inspired neural network architecture generates output that overlaps with human and rodent performance over a non-constrained neural network. This novel conceptual framework, by isolating discrete decision-making primitives, has potential to help us identify how different brain regions give rise to decision-making behavior, as well as to facilitate better diagnosis of neuropsychiatric disorders and development of naturally-inspired artificial intelligence systems of decision-making.

neuroscience↗

bioRxiv2024

Model of a striatal circuit exploring biological mechanisms underlying decision-making during normal and disordered states

Decision-making requires continuous adaptation to internal and external contexts. Changes in decision-making are reliable transdiagnostic symptoms of neuropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a mathematical space for decision-making computations depending on context, and how the matrix compartment defines action value depending on the space. The model explains multiple experimental results and unifies other theories like reward prediction error, roles of the direct versus indirect pathways, and roles of the striosome versus matrix, under one framework. We also found, through new analyses, that striosome and matrix neurons increase their synchrony during difficult tasks, caused by a necessary increase in dimensionality of the space. The model makes testable predictions about individual differences in disorder susceptibility, decision-making symptoms shared among neuropsychiatric disorders, and differences in neuropsychiatric disorder symptom presentation. The model reframes the role of the striosomal circuit in neuroeconomic and disorder-affected decision-making. HighlightsO_LIStriosomes prioritize decision-related data used by matrix to set action values. C_LIO_LIStriosomes and matrix have different roles in the direct and indirect pathways. C_LIO_LIAbnormal information organization/valuation alters disorder presentation. C_LIO_LIVariance in data prioritization may explain individual differences in disorders. C_LI eTOCBeck et al. developed a computational model of how a striatal circuit functions during decision-making. The model unifies and extends theories about the direct versus indirect pathways. It further suggests how aberrant circuit function underlies decision-making phenomena observed in neuropsychiatric disorders.

neuroscience↗

bioRxiv2023

Model Based Inference of Synaptic Plasticity Rules

Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, applicable to both neural and behavioral data. Our approach approximates plasticity rules using a parameterized function, employing either truncated Taylor series for theoretical interpretability or multilayer perceptrons. These plasticity parameters are optimized via gradient descent over entire trajectories to align closely with observed neural activity or behavioral learning dynamics. This method can uncover complex rules that induce long nonlinear time dependencies, particularly involving factors like postsynaptic activity and current synaptic weights. We validate our approach through simulations, successfully recovering established rules such as Ojas, as well as more intricate plasticity rules with reward-modulated terms. We assess the robustness of our technique to noise and apply it to behavioral data from Drosophila in a probabilistic reward-learning experiment. Notably, our findings reveal an active forgetting component in reward learning in flies, improving predictive accuracy over previous models. This modeling framework offers a promising new avenue for elucidating the computational principles of synaptic plasticity and learning in the brain.

neuroscience↗

bioRxiv2021

Meta-learning local synaptic plasticity for continual familiarity detection

Over the course of a lifetime, a continual stream of information is encoded and retrieved from memory. To explore the synaptic mechanisms that enable this ongoing process, we consider a continual familiarity detection task in which a subject must report whether an image has been previously encountered. We design a class of feedforward neural network models endowed with biologically plausible synaptic plasticity dynamics, the parameters of which are meta-learned to optimize familiarity detection over long delay intervals. After training, we find that anti-Hebbian plasticity leads to better performance than Hebbian and replicates experimental results from the inferotemporal cortex, including repetition suppression. Unlike previous models, this network both operates continuously without requiring any synaptic resets and generalizes to intervals it has not been trained on. We demonstrate this not only for uncorrelated random stimuli but also for images of real-world objects. Our work suggests a biologically plausible mechanism for continual learning, and demonstrates an effective application of machine learning for neuroscience discovery.

neuroscience↗