The topology of E/I recurrent networks regulates the effects of synaptic plasticity DOI Creative Commons
Emmanouil Giannakakis, O Vinogradov, Victor Buendía

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Фев. 28, 2023

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) cotuning 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

Язык: Английский

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks DOI Creative Commons
Samuel Eckmann, Edward Young, Julijana Gjorgjieva

и другие.

Proceedings of the National Academy of Sciences, Год журнала: 2024, Номер 121(25)

Опубликована: Июнь 13, 2024

Cortical networks exhibit complex stimulus–response patterns that are based on specific recurrent interactions between neurons. For example, the balance excitatory and inhibitory currents has been identified as a central component of cortical computations. However, it remains unclear how required synaptic connectivity can emerge in developing circuits where synapses neurons simultaneously plastic. Using theory modeling, we propose wide range response properties arise from single plasticity paradigm acts at all connections—Hebbian learning is stabilized by synapse-type-specific competition for limited supply resources. In plastic circuits, this enables formation decorrelation inhibition-balanced receptive fields. Networks develop an assembly structure with stronger connections similarly tuned normalization orientation-specific center-surround suppression, reflecting stimulus statistics during training. These results demonstrate self-organize into functional suggest essential role competitive development circuits.

Язык: Английский

Процитировано

9
HebCGNN: Hebbian-enabled causal classification integrating dynamic impact valuing DOI Creative Commons
Simi Job, Xiaohui Tao, Taotao Cai

и другие.

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113094 - 113094

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex DOI Creative Commons
Claire Meissner-Bernard, Friedemann Zenke, Rainer W. Friedrich

и другие.

eLife, Год журнала: 2025, Номер 13

Опубликована: Янв. 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.

Язык: Английский

Процитировано

0
Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex DOI Creative Commons
Claire Meissner-Bernard, Friedemann Zenke, Rainer W. Friedrich

и другие.

eLife, Год журнала: 2024, Номер 13

Опубликована: Май 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.

Язык: Английский

Процитировано

1
Efficient laminar-distributed interactions and orientation selectivity in the mouse V1 cortical column DOI Creative Commons

Licheng Zou,

Giulia Moreni, Cyriel M. A. Pennartz

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 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.

Язык: Английский

Процитировано

1
Structured stabilization in recurrent neural circuits through inhibitory synaptic plasticity DOI Creative Commons
Dylan Festa,

Claudia Cusseddu,

Julijana Gjorgjieva

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 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.

Язык: Английский

Процитировано

0
Structural influences on synaptic plasticity: The role of presynaptic connectivity in the emergence of E/I co-tuning DOI Creative Commons
Emmanouil Giannakakis, O Vinogradov, Victor Buendía

и другие.

PLoS Computational Biology, Год журнала: 2024, Номер 20(10), С. e1012510 - e1012510

Опубликована: Окт. 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

Язык: Английский

Процитировано

0
Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex DOI Open Access
Claire Meissner-Bernard, Friedemann Zenke, Rainer W. Friedrich

и другие.

Опубликована: Дек. 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.

Язык: Английский

Процитировано

0
The topology of E/I recurrent networks regulates the effects of synaptic plasticity DOI Creative Commons
Emmanouil Giannakakis, O Vinogradov, Victor Buendía

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Фев. 28, 2023

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) cotuning 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

Язык: Английский

Процитировано

0