Biology subjects

Herman, P. A.

Publications and source records attributed to Herman, P. A..

8 recordsLinked to original sources

bioRxiv2026

NMDAR-mediated shift of neuronal gain across the cortical hierarchy

Neuronal gain is a critical feature of neuronal information processing, reflecting a cells excitability in response to presynaptic inputs. Evidence from spine counts in non-human primates suggests that the number of presynaptic inputs increases across the cortical hierarchy. Therefore, understanding the regulation of the neuronal gain can yield important insights into different integration strategies across the cortex. Along with the increasing spine counts, there is further evidence for a similar increase in NMDA-receptor-mediated currents. Here, we show, using detailed simulations of a thick-tufted layer 5 pyramidal neuron, that the amount of current mediated through NMDA receptors (NMDARs) strongly affects the neuronal gain function with balanced presynaptic inputs. Without NMDA, postsynaptic activity quickly saturates and declines with increasing inputs. However, with NMDARs, the gain of postsynaptic activity shifts into higher input regimes, enabling integration of dense inputs. We show that, with NMDARs, a significant fraction of postsynaptic activity arises from feedback between the voltage dependence of NMDARs and voltage-gated sodium channels. Lastly, we show, using a network model, that NMDAR knockout in single cells has differential effects on postsynaptic activity depending on network size (a proxy for cortical hierarchy). Thus, we propose that an NMDA gradient across the cortical hierarchy may be necessary to maintain excitability amid increasing presynaptic input.

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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.

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bioRxiv2025

Neural Mechanisms of Cognitive Flexibility and Interference Control in a Stroop Task Switching Paradigm

Task-switching paradigms, often used to study cognitive flexibility, frequently employ incongruent bivalent stimuli, triggering two tasks and potentially conflating cognitive flexibility with interference control. This study assesses cognitive flexibility using univalent stimuli (triggering one task) and congruent bivalent stimuli (same response across tasks) in a modified Stroop task to investigate well-established neural activity correlates of cognitive flexibility, manifested in human (females and males) electroencephalography (EEG) recordings, while isolating switch process from the influence of interference control at the response level. In particular, we analyzed EEG theta-band activity and event-related potential (ERP) components in switch (N2, P3a, P3b, late sustained potential (LSP)) and interference (N400, LSP and mid-frontal theta activity) conditions. We compared each of the switch and the interference condition to the control condition using cluster-based permutation test. In the switch condition, we observed fronto-central N2, reduced frontal P3a, and a positive occipital LSP. The interference condition showed increased frontal theta, parietal N400, and a positive occipital LSP. We also compared switch and interference conditions using cluster-based permutation test. We observed a larger N2 in the frontocentral regions during the switch condition and higher frontal theta activity during the interference condition, which aligns with their comparisons to the control condition. This result suggests that distinct neural mechanisms are used for each of the processes involved in conflict monitoring. Specifically, the theta activity may reflect sustained monitoring and conflict resolution during interference, while the N2 may reflect more transient conflict detection and the need to switch task sets. Significance StatementCognitive flexibility--the ability to adapt behavior to a changing environment--is essential for goal-directed actions. For assessing cognitive flexibility unlike the typical approach, we did not use incongruent bivalent stimuli. We used univalent and congruent bivalent stimuli to isolate cognitive flexibility while assessing interference control separately with incongruent bivalent stimuli. We analyzed well-documented brain activities related to cognitive flexibility (P3b, N2, P3a, LSP) and interference control (N400, LSP), and mid-frontal EEG theta activity. Despite using different stimuli, we observed all expected components associated with the switch process except for the P3b. Both processes share common parietal activity, and while the frontal lobe plays a role in both, its activity differs between them.

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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.

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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.

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bioRxiv2023

Distinct Functions for Beta and Alpha Bursts in Gating of Human Working Memory

Multiple neural mechanisms underlying gating to and from working memory (WM) have been proposed, with divergent results obtained in human and animal studies. Previous results from non-human primate studies suggest information encoding and retrieval is regulated by high-power bursts in the beta frequency range, whereas human studies suggest that alpha power in sensory regions filters out unwanted stimuli from entering WM. Discrepancies between studies, whether due to differences in analysis, species, or cortical regions, remain unexplained. We addressed this by performing similar single-trial burst analysis we earlier deployed on non-human primates on human whole-brain electrophysiological activity. Participants performed a sequential working memory task that allowed us to track the distinct electrophysiological activity patterns associated with neural processing of targets and distractors. Intriguingly, our results reconcile earlier findings by demonstrating that both alpha and beta bursts are involved in the filtering and control of WM items, but with region and task-specific differences between the two rhythms. Occipital beta burst patterns regulate the transition from sensory processing to WM retention whereas prefrontal and parietal beta bursts track sequence order and proactively suppress retained information prior to upcoming target encoding. Occipital alpha bursts instead suppress unwanted sensory stimuli during their presentation. These results suggest that human working memory is regulated by multiple neural mechanisms that operate in different cortical regions and serve distinct computational roles.

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bioRxiv2022

How short decoding times, stimulus dimensionality and spontaneous activity constrain the shape of tuning curves: A speed-accuracy trade-off

According to the efficient coding hypothesis, sensory neurons are adapted to provide maximal information about the environment given some biophysical constraints. Early sensory neurons modulate their average firing rates in response to some features of the external stimulus, creating tuned responses. In early visual areas, these modulations (or tunings) are predominantly single-peaked. However, periodic tuning, as exhibited by grid cells, has been linked to a significant increase in decoding performance. Does this imply that the tuning curves in early visual areas are sub-optimal? We argue that the time scale at which neurons encode information is imperative to understanding the relative advantages of single-peaked and periodic tuning curves. Because, if decoding ability scales differently with time for the different shapes of tuning curves, the time scale at which the neurons operate becomes critical. Here, we show that the possibility of catastrophic (large) errors due to overlapping neural responses for distinct stimulus conditions creates a trade-off between decoding time and decoding ability. Unfortunately, standard theoretical measures such as Fisher information do not capture these errors. We investigate how (very) short decoding times and stimulus dimensionality affect the optimal shape of tuning curves for stimuli with finite domains. In particular, we focus on the spatial periods of the tuning curves (or the number of "peaks") for a class of circular tuning curves. We show a general trend for minimal decoding time, i.e., the shortest decoding time required to produce a statistically reliable signal, to increase with increasing Fisher information implying a trade-off between accuracy and speed. This trade-off is reinforced whenever the stimulus dimensionality is high or there is ongoing activity. Thus, given constraints on processing speed, we present normative arguments for the existence of single-peaked, rather than a periodic, tuning organization observed in early visual areas.

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bioRxiv2022

Reduced variability of bursting activity during working memory

Working memories have long been thought to be maintained by persistent spiking. However, mounting evidence from multiple-electrode recording (and single-trial analyses) shows that the underlying spiking is better characterized by intermittent bursts of activity. A counterargument suggested this intermittent activity is at odds with observations that spike-time variability reduces during task performance. However, this counterargument rests on assumptions, such as randomness in the timing of the bursts, that may not be correct. Thus, we analyzed spiking and LFPs from the prefrontal cortex (PFC) of monkeys to determine if task-related reductions in variability can co-exist with intermittent spiking. We found that it does because both spiking and associated gamma bursts were task-modulated, not random. In fact, the task-related reduction in spike variability could be explained by a related reduction in gamma burst variability. Our results provide further support for the intermittent activity models of working memory as well as novel mechanistic insights into how spike variability is reduced during cognitive tasks.

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