Dopamine D1 receptor expression in dlPFC inhibitory parvalbumin neurons may contribute to higher visuospatial distractibility in marmosets versus macaques

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 16, 2024

Abstract Marmosets and macaques are common non-human primate models of cognition, but evidence suggests that marmosets perform more poorly appear distractible during cognitive tasks. The dorsolateral prefrontal cortex (dlPFC) plays a key role in regulating attention, prior research dopaminergic modulation inhibitory parvalbumin (PV) neurons could contribute to distractibility performance. Thus, we compared the two species using visual fixation task with distractors, performed molecular anatomical analyses dlPFC, linked functional microcircuitry performance computational modeling. We found than macaques, marmoset dlPFC PV contain higher levels dopamine-1 receptor (D1R) transcripts, similar mice, D1R protein. model suggested expression may increase by suppressing microcircuits, e.g., when dopamine is released salient stimuli.

Language: Английский

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Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex

eLife, Journal Year: 2025, Volume and Issue: 13

Published: Jan. 13, 2025

Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these contain both excitatory and inhibitory neurons (E/I assemblies), resulting co-tuning precise balance excitation inhibition. To understand computational consequences E/I under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp adult zebrafish, precisely balanced recurrent homologous piriform cortex. We found stabilized firing rate distributions compared with global Unlike classical models, did not show discrete attractor dynamics. Rather, responses learned inputs were locally constrained onto manifolds ‘focused’ activity into neuronal subspaces. The covariance structure supported pattern classification when was retrieved selected subsets. Networks therefore transformed geometry coding space, continuous representations reflected relatedness an individual’s experience. Such enable fast classification, can support continual learning, may provide basis for higher-order learning cognitive computations.

Language: Английский

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Synaptic plasticity facilitates oscillations in a V1 cortical column model with multiple interneuron types

Frontiers in Computational Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: April 30, 2025

Neural rhythms are ubiquitous in cortical recordings, but it is unclear whether they emerge due to the basic structure of microcircuits or depend on function. Using detailed electrophysiological and anatomical data mouse V1, we explored this question by building a spiking network model column incorporating pyramidal cells, PV, SST, VIP inhibitory interneurons, dynamics for AMPA, GABA, NMDA receptors. The resulting matched vivo cell-type-specific firing rates spontaneous stimulus-evoked conditions mice, although rhythmic activity was absent. Upon introduction long-term synaptic plasticity form an STDP rule, broad-band (15–60 Hz) oscillations emerged, with feedforward/feedback input streams enhancing/suppressing oscillatory drive, respectively. These plasticity-triggered relied all cell types, specific experience-dependent connectivity patterns were required generate oscillations. Our results suggest that neural not necessarily intrinsic properties circuits, rather may arise from structural changes elicited learning-related mechanisms.

Language: Английский

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Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex

eLife, Journal Year: 2024, Volume and Issue: 13

Published: May 9, 2024

Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these contain both excitatory and inhibitory neurons (E/I assemblies), resulting co-tuning precise balance excitation inhibition. To understand computational consequences E/I under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp adult zebrafish, precisely balanced recurrent homologous piriform cortex. We found stabilized firing rate distributions compared with global Unlike classical models, did not show discrete attractor dynamics. Rather, responses learned inputs were locally constrained onto manifolds ‘focused’ activity into neuronal subspaces. The covariance structure supported pattern classification when was retrieved selected subsets. Networks therefore transformed geometry coding space, continuous representations reflected relatedness an individual’s experience. Such enable fast classification, can support continual learning, may provide basis for higher-order learning cognitive computations.

Language: Английский

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Task success in trained spiking neural network models coincides with emergence of cross-stimulus-modulated inhibition

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 30, 2024

The neocortex is composed of spiking neurons interconnected in a sparse, recurrent network. Spiking within neocortical networks drives the computational processes that convert sensory inputs into suitable behavioral responses. In this study, we train biologically realistic neural network (SNN) models and identify architectural changes following training which enable task-appropriate computations. Specifically, employ binary state change detection task, where each defined by motion entropy. This task mirrors paradigms are performed lab. SNNs excitatory inhibitory units with connection likelihoods strengths matched to mouse neocortex. Following training, discover selectively adjust firing rates depending on entropy state, connectivity between input layers accordance rate modulation. Recurrent positively modulate one strengthened their connections opposite specific pattern cross-modulation inhibition emerged as solution regardless output encoding schemes when imposing Dale’s law throughout SNNs. Disrupting spike times significantly impaired performance, indicating precise coordination excitation critical for network's behavior. Using one-hot resulted balanced response two different states. With balance, same emerged. work underscores crucial role interneurons patterns shaping dynamics enabling information processing circuits.

Language: Английский

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Structured stabilization in recurrent neural circuits through inhibitory synaptic plasticity

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 12, 2024

Inhibitory interneurons play a dual role in recurrently connected biological circuits: they regulate global neural activity to prevent runaway excitation, and contribute complex computations. While the first can be achieved through unstructured connections tuned for homeostatic rate stabilization, computational tasks often require structured excitatory-inhibitory (E/I) connectivity. Here, we consider broad class of pairwise inhibitory spike-timing dependent plasticity (iSTDP) rules, demonstrating how synapses self-organize both stabilize excitation generate functionally relevant connectivity structures — process call “structured stabilization”. We show that E/I circuit motifs large spiking recurrent networks choice iSTDP rule lead either mutually pairs, or lateral inhibition, where an neuron connects excitatory does not directly connect back it. In one-dimensional ring network, if two populations follow these distinct forms iSTDP, effective within population self-organizes into Mexican-hat-like profile with influence center away from center. This leads emergent dynamical properties such as surround suppression modular spontaneous activity. Our theoretical work introduces family rules retains applicability simplicity spike-timing-based plasticity, while promoting structured, self-organized stabilization. These findings highlight rich interplay between structure, neuronal dynamics, offering framework understanding shapes network function.

Language: Английский

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Short-term plasticity of EI balance at single neurons can detect pattern transitions

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 31, 2024

Abstract Sensory input and internal context converge onto the hippocampus as spatio-temporal patterns of activity. Transitions in these are frequently salient, yet CA1 pyramidal neurons operate under conditions divisive normalisation summed patterned by excitatory-inhibitory (EI) balance which suppresses most responses. We characterized role short-term potentiation (STP) mediating change detection mouse CA3-CA1 network using optogenetic stimuli CA3 while recording from neurons. parameterized STP its effect on summation, developed a multiscale model projections hundreds E I boutons each including stochastic signaling to mediate postsynaptic neuron. show that modulates EI summation across patterns, predicted confirmed single can detect transitions Using we feedforward networks, coupled with moderate sparsity due pattern connections, strengthens rapid

Language: Английский

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Structural influences on synaptic plasticity: The role of presynaptic connectivity in the emergence of E/I co-tuning

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(10), P. e1012510 - e1012510

Published: Oct. 31, 2024

Cortical neurons are versatile and efficient coding units that develop strong preferences for specific stimulus characteristics. The sharpness of tuning efficiency is hypothesized to be controlled by delicately balanced excitation inhibition. These observations suggest a need detailed co-tuning excitatory inhibitory populations. Theoretical studies have demonstrated combination plasticity rules can lead the emergence excitation/inhibition (E/I) in driven independent, low-noise signals. However, cortical signals typically noisy originate from highly recurrent networks, generating correlations inputs. This raises questions about ability mechanisms self-organize co-tuned connectivity receiving noisy, correlated Here, we study input selectivity weight neuron network via plastic feedforward connections. We demonstrate while noise levels destroy readout neuron, introducing structures non-plastic pre-synaptic re-establish it favourable correlation structure population activity. further show structured impact statistics fully driving formation do not receive direct other areas. Our findings indicate dynamics created simple, biologically plausible structural patterns enhance synaptic learn input-output relationships higher brain

Language: Английский

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Efficient laminar-distributed interactions and orientation selectivity in the mouse V1 cortical column

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 5, 2024

Abstract The emergence of orientation selectivity in the visual cortex is a well-known phenomenon neuroscience, but details such and role different cortical layers cell types, particularly rodents which lack topographical organization orientation-selectivity (OS) properties, are less clear. To tackle this question, we use an existing biologically detailed model mouse V1 column, constrained by connectivity data across between pyramidal, PV, SST VIP types. Using as basis, implemented activity-dependent structural plasticity induced stimulation with orientated drifting gratings, leading to good match tuning properties pyramidal cells experimentally observed OS laminar distribution, their evoked firing rate width. We then employed mean-field uncover co-tuned subnetworks signal propagation explain effects intra- inter-laminar coupling distributions. Our plasticity-induced modified were able both excitatory enhancement through disynaptic inhibition. Overall, our work highlights importance clustering neural features for effective transmission circuits.

Language: Английский

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Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex

Published: Dec. 31, 2024

Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these contain both excitatory and inhibitory neurons (E/I assemblies), resulting co-tuning precise balance excitation inhibition. To understand computational consequences E/I under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp adult zebrafish, precisely balanced recurrent homologous piriform cortex. We found stabilized firing rate distributions compared with global Unlike classical models, did not show discrete attractor dynamics. Rather, responses learned inputs were locally constrained onto manifolds “focused” activity into neuronal subspaces. The covariance structure supported pattern classification when was retrieved selected subsets. Networks therefore transformed geometry coding space, continuous representations reflected relatedness an individual’s experience. Such enable fast classification, can support continual learning, may provide basis for higher-order learning cognitive computations.

Language: Английский

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