Biology subjects

Lansner, A.

Publications and source records attributed to Lansner, A..

7 recordsLinked to original sources

bioRxiv2025

Synergistic short-term synaptic plasticity mechanisms for working memory

Working memory (WM) is essential for almost every cognitive task. The neural and synaptic mechanisms supporting the rapid encoding and maintenance of memories in diverse tasks are the subject of an ongoing debate. The traditional view of WM as stationary persistent firing of selective neuronal populations has given room to newer ideas regarding mechanisms that support a more dynamic maintenance of multiple items. Various computational WM models based on different biologically plausible plasticity mechanisms have been proposed. We show that these proposed short-term plasticity mechanisms may not necessarily be competing explanations, but instead yield interesting interactions that broaden the functional range of models on a wide set of WM task motifs and simultaneously enhance the biological plausibility of spiking neural network models, in particular of the underlying synaptic plasticity. While reductionist models (WM function explained by one particular mechanism) are theoretically appealing and have increased our understanding of specific mechanisms, they are narrow explanations. In this study we evaluate the interactions between three commonly proposed classes of plasticity, namely intrinsic excitability, synaptic facilitation/augmentation and Hebbian plasticity. We systematically test combinations of mechanisms in a spiking neural network model on a broad suite of tasks or functional motifs deemed principally important for WM operation, such as one-shot encoding, free and cued recall, multi-item delay maintenance and updating. Our analysis of the operational task performance indicates that a composite model is superior to more reductionist variants. Importantly, we attribute the observable differences to the principle nature of specific types of plasticity. Significance StatementWorking memory - the ability to temporarily hold and manipulate information - is fundamental to cognition, yet the underlying neural and synaptic mechanisms remain debated. Traditional models propose and investigate isolated mechanisms like synaptic facilitation, often applied to specific tasks or rather simple delay maintenance. In contrast, this study proposes that broader working memory capabilities emerge from synergistic interactions among three commonly proposed short-term plasticity mechanisms: Hebbian learning, synaptic facilitation/augmentation and intrinsic excitability. Using biologically constrained spiking neural networks across a broad synthetic task battery, we show that combining mechanisms produces superior working memory capabilities compared with single isolated mechanism. This computational framework advances our understanding of how multiple plasticity mechanisms coordinate to support flexible, and robust working memory.

neuroscience↗

bioRxiv2025

Elusive scents: neurocomputational mechanisms of verbal omissions in free odor naming

Odor naming is considered a particularly challenging cognitive test, but the underlying cause of this difficulty is unknown. People often fail to report any source label to identify common odors, resulting in omissions (i.e., a lack of response). Here, with the support of a computational model, we offer a hypothesis about the neural network mechanisms underlying odor naming omissions. Based on an evaluation of behavioral data from almost 40,000 odor naming attempts, we suggest that high omission rates are driven by odors that are referred to by multiple linguistic labels. To explain this observation at the systems level, where olfactory perception and language (semantic) processing are produced by interacting cortical systems, we developed a computational model consisting of two associatively coupled attractor memory networks (odor and language networks), and investigated the effect of Hebbian-like learning on the simulated task performance. We used distributed network representations for the odor percepts and word label mental objects, and accounted for their statistical inter-relationships (correlations) extracted from collected data on odor perceptual similarity, and from a large Swedish odor language corpus, respectively. We evaluated a novel hypothesis, that Bayesian-Hebbian synaptic plasticity mechanisms can explain behavioral omissions in odor naming tasks, casting new light on the underlying mechanisms of this frequently observed memory phenomenon. Due to the nature of Bayesian-Hebbian associative learning connecting the two networks, there was a progressively weaker coupling for odors paired with multiple different labels in the encoding process (one-to-many mapping). Thus, when the model was cued with perceptual odor stimuli that established multiple word label associations (one-to-many mapping), the olfactory language network often produced subthreshold network responses, resulting in elevated omissions (opposite to one-to-few mapping scenario that led to improved performance scores). Our results are of theoretical interest, as they suggest a biologically plausible mechanism to explain a common, but poorly understood, behavioral phenomenon.

neuroscience↗

bioRxiv2024

Interplay of Long- and Short-term Synaptic Plasticity in a Spiking Network Model of Rat's Episodic Memory

We investigated the interaction of long-term episodic processes with effects of short-term dynamics of recency. This work takes inspiration from a seminal experimental work involving an odor-in-context association task conducted on rats (Panoz-Brown et al., 2016). In the experimental task, rats were presented with odor pairs in two arenas serving as old or new contexts for specific odors-items. Rats were rewarded for selecting the odor that was new to the current context. New odor items were deliberately presented with higher recency relative to old items, so that episodic memory was put in conflict with non-episodic recency effects. To study our hypothesis about the major role of synaptic interplay of long- and short-term plasticity phenomena in explaining rats performance in such episodic memory tasks, we built a computational spiking model consisting of two reciprocally connected networks that stored contextual and odor information as consolidated and distributed memory patterns (cell assemblies). We induced context-item coupling between the two networks using Bayesian-Hebbian plasticity with eligibility traces to account for reward based learning. We first reproduced quantitatively and explained mechanistically the findings of the experimental study, and further simulated alternative tasks, e.g. where old odor items were instead encoded with higher recency, thus synergistically confounding episodic memory with effects of recency. Our model predicted that higher recency of old items enhances item-in-context memory by boosting the activations of old items resulting in further enhancement of memory performance. We argue that the model offers a computational framework for studying behavioral implications of the synaptic underpinning of different memory effects in experimental episodic memory paradigms. Significance StatementAn important aspect of computational modeling is its ability to bridge spatial scales. Our cortical memory model represents a novel computational attempt to unravel neural and synaptic processes with mesoscopic manifestations underpinning the complex effects of short-term memory dynamics on episodic memory recall. We consider the quantitative match with Panoz-Brown et al.s (2016) experimental findings, obtained in a detailed spiking network model constrained by available biological data, a significant step towards bridging the gap between behavioral correlates of episodic memory and synaptic mechanisms. Our findings and additional predictions on a suite of different episodic memory tasks invite further experimental examination.

neuroscience↗

bioRxiv2021

Traces of semantization - from episodic to semantic memory in a spiking cortical network model

Episodic memory is the recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or "semantization", which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian-Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian-Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We also examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints whilst also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.

neuroscience↗

bioRxiv2019

Probabilistic associative learning suffices for learning the temporal structure of multiple sequences

AO_SCPLOWBSTRACTC_SCPLOWMany brain phenomena both at the cognitive and behavior level exhibit remarkable sequential characteristics. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-scale, in this work we attempt to characterize to what degree some of this properties can be explained as a consequence of simple associative learning. To this end, we employ a parsimonious firing-rate attractor network equipped with the Hebbian-like Bayesian Confidence Propagating Neural Network (BCPNN) learning rule relying on synaptic traces with asymmetric temporal characteristics. The proposed network model is able to encode and reproduce temporal aspects of the input, and offers internal control of the recall dynamics by gain modulation. We provide an analytical characterisation of the relationship between the structure of the weight matrix, the dynamical network parameters and the temporal aspects of sequence recall. We also present a computational study of the performance of the system under the effects of noise for an extensive region of the parameter space. Finally, we show how the inclusion of modularity in our network structure facilitates the learning and recall of multiple overlapping sequences even in a noisy regime.

neuroscience↗

bioRxiv2018

Introducing double bouquet cells into a modular cortical associative memory model

We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Bayesian-Hebbian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dales Principle.\n\nWe show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double-bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. We thus show that the resulting change to the microcircuit architecture improves the models biological plausibility without otherwise impacting the models spiking activity, basic operation, and learning abilities.

neuroscience↗

bioRxiv2018

Fast Hebbian Plasticity explains Working Memory and Neural Binding

Working memory (WM) is a key component of human memory and cognition. Computational models have been used to study the underlying neural mechanisms, but neglected the important role of short- and long-term memory interactions (STM, LTM) for WM. Here, we investigate these using a novel multi-area spiking neural network model of prefrontal cortex (PFC) and two parieto-temporal cortical areas based on macaque data. We propose a WM indexing theory that explains how PFC could associate, maintain and update multi-modal LTM representations. Our simulations demonstrate how simultaneous, brief multi-modal memory cues could build a temporary joint memory representation as an \"index\" in PFC by means of fast Hebbian synaptic plasticity. This index can then reactivate spontaneously and thereby reactivate the associated LTM representations. Cueing one LTM item rapidly pattern-completes the associated un-cued item via PFC. The PFC-STM network updates flexibly as new stimuli arrive thereby gradually over-writing older representations.

neuroscience↗