A doubly stochastic renewal framework for partitioning spiking variability DOI
Cina Aghamohammadi, Chandramouli Chandrasekaran, Tatiana A. Engel

et al.

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

Published: Feb. 23, 2024

ABSTRACT The firing rate is a prevalent concept used to describe neural computations, but estimating dynamically changing rates from irregular spikes challenging. An inhomogeneous Poisson process, the standard model for partitioning and spiking irregularity, cannot account diverse spike statistics observed across neurons. We introduce doubly stochastic renewal point flexible mathematical framework variability, which captures broad spectrum of irregularity periodic super-Poisson. validate our using intracellular voltage recordings develop method data. find that cortical neurons decreases sensory association areas nearly constant each neuron under many conditions can also change task epochs. A network shows depends on connectivity with external input. These results help improve precision single trials constrain mechanistic models circuits.

Language: Английский

Preparatory activity and the expansive null-space DOI
Mark M. Churchland, Krishna V. Shenoy

Nature reviews. Neuroscience, Journal Year: 2024, Volume and Issue: 25(4), P. 213 - 236

Published: March 5, 2024

Language: Английский

Citations

39
Lyapunov spectra of chaotic recurrent neural networks DOI Creative Commons
Rainer Engelken, Fred Wolf, L. F. Abbott

et al.

Physical Review Research, Journal Year: 2023, Volume and Issue: 5(4)

Published: Oct. 16, 2023

The Lyapunov spectrum of recurrent neural networks is calculated and analytical approximations through random matrix theory are provided. dependency attractor dimensions entropy rates on coupling strength input fluctuations identified a point symmetry the revealed. A link shown between exponents to error propagation stability in trained for machine-learning applications.

Language: Английский

Citations

37
Flexible control of motor units: is the multidimensionality of motor unit manifolds a sufficient condition? DOI Creative Commons

François Dernoncourt,

Simon Avrillon,

Tijn Logtens

et al.

The Journal of Physiology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

Abstract Understanding flexibility in the neural control of movement requires identifying distribution common inputs to motor units. In this study, we identified large samples units from two lower limb muscles: vastus lateralis (VL; up 60 per participant) and gastrocnemius medialis (GM; 67 participant). First, applied a linear dimensionality reduction method assess manifolds underlying unit activity. We subsequently investigated under conditions: sinusoidal contractions with torque feedback, online visual feedback on firing rates. Overall, found that activity GM was effectively captured by single latent factor defining unidimensional manifold, whereas VL were better represented three factors multidimensional manifold. Despite difference dimensionality, recruitment muscles exhibited similarly low levels flexibility. Using spiking network model, tested hypothesis derived factorization does not solely represent descending cortical commands but is also influenced spinal circuitry. demonstrated heterogeneous units, or specific configurations recurrent inhibitory circuits, could produce This study clarifies an important debated issue, demonstrating while firings non‐compartmentalized muscle can lie CNS may still have limited capacity for flexible these image Key points To generate movement, distributes both excitatory The level remains topic debate significant implications smallest control. By combining experimental data silico models, sample be manifold; however, show very their recruitment. manifold directly reflect instead relate organization local circuits.

Language: Английский

Citations

1
Approximating Nonlinear Functions With Latent Boundaries in Low-Rank Excitatory-Inhibitory Spiking Networks DOI Creative Commons
William F. Podlaski, Christian K. Machens

Neural Computation, Journal Year: 2024, Volume and Issue: 36(5), P. 803 - 857

Published: April 23, 2024

Abstract Deep feedforward and recurrent neural networks have become successful functional models of the brain, but they neglect obvious biological details such as spikes Dale’s law. Here we argue that these are crucial in order to understand how real circuits operate. Towards this aim, put forth a new framework for spike-based computation low-rank excitatory-inhibitory spiking networks. By considering populations with rank-1 connectivity, cast each neuron’s threshold boundary low-dimensional input-output space. We then show combined thresholds population inhibitory neurons form stable space, those excitatory an unstable boundary. Combining two boundaries results rank-2 (EI) network inhibition-stabilized dynamics at intersection boundaries. The resulting can be understood difference convex functions is thereby capable approximating arbitrary non-linear mappings. demonstrate several properties networks, including noise suppression amplification, irregular activity synaptic balance, well relate rate limit becomes soft. Finally, while our work focuses on small (5-50 neurons), discuss potential avenues scaling up much larger Overall, proposes perspective may serve starting point mechanistic understanding computation.

Language: Английский

Citations

8
Decoding the brain: From neural representations to mechanistic models DOI Creative Commons
Mackenzie Weygandt Mathis, Adriana Perez Rotondo, Edward F. Chang

et al.

Cell, Journal Year: 2024, Volume and Issue: 187(21), P. 5814 - 5832

Published: Oct. 1, 2024

Language: Английский

Citations

6
Distributed dopaminergic signaling in the basal ganglia and its relationship to motor disability in Parkinson's disease DOI Creative Commons
Shenyu Zhai, Qiaoling Cui, DeNard V. Simmons

et al.

Current Opinion in Neurobiology, Journal Year: 2023, Volume and Issue: 83, P. 102798 - 102798

Published: Oct. 30, 2023

The degeneration of mesencephalic dopaminergic neurons that innervate the basal ganglia is responsible for cardinal motor symptoms Parkinson's disease (PD). It has been thought loss signaling in one region - striatum was solely network pathophysiology causing PD symptoms. While our understanding dopamine (DA)'s role modulating striatal circuitry deepened recent years, it also become clear acts other regions to influence movement. Underscoring this point, examination a new progressive mouse model shows DA depletion alone not sufficient induce parkinsonism and restoration extra-striatal attenuates parkinsonian deficits once they appear. This review summarizes advances effort understand circuitry, its modulation by DA, how dysfunction drives

Language: Английский

Citations

15
Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns DOI Creative Commons
Oleg V. Maslennikov, Matjaž Perc, Vladimir I. Nekorkin

et al.

Frontiers in Computational Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: Feb. 23, 2024

In this study, we focus on training recurrent spiking neural networks to generate spatiotemporal patterns in the form of closed two-dimensional trajectories. Spike trains trained are examined terms their dissimilarity using Victor–Purpura distance. We apply algebraic topology methods matrices obtained by rank-ordering entries distance matrices, specifically calculating persistence barcodes and Betti curves. By comparing features different types output patterns, uncover complex relations between low-dimensional target signals underlying multidimensional spike trains.

Language: Английский

Citations

5
Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits DOI Creative Commons
Diek W. Wheeler, Jeffrey D. Kopsick, Nate Sutton

et al.

eLife, Journal Year: 2023, Volume and Issue: 12

Published: Sept. 27, 2023

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, v1.0 established foundational classification system identifying 122 based axonal dendritic morphologies, main neurotransmitter, membrane biophysics, molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered aggregation literature-mined data, including among others counts, spiking patterns, synaptic physiology, in vivo firing phases, connection probabilities. Those additional properties increased online information content this public resource over 100-fold, enabling numerous independent discoveries by scientific community. v2.0, introduced here, besides incorporating 50 new types, now recenters its focus extending functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, freely downloadable model parameters are directly linked specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses circuit connectivity neural network simulations activity dynamics. These advances can help generate precise, experimentally testable hypotheses shed light mechanisms underlying associative memory spatial navigation.

Language: Английский

Citations

11
Emergent Rate-Based Dynamics in Duplicate-Free Populations of Spiking Neurons DOI Creative Commons
Valentin Schmutz, Johanni Brea, Wulfram Gerstner

et al.

Physical Review Letters, Journal Year: 2025, Volume and Issue: 134(1)

Published: Jan. 6, 2025

Can spiking neural networks (SNNs) approximate the dynamics of recurrent networks? Arguments in classical mean-field theory based on laws large numbers provide a positive answer when each neuron network has many "duplicates", i.e., other neurons with almost perfectly correlated inputs. Using disordered model that guarantees absence duplicates, we show duplicate-free SNNs can converge to networks, thanks concentration measure phenomenon. This result reveals general mechanism underlying emergence rate-based SNNs.

Language: Английский

Citations

0
Rethinking the network determinants of motor disability in Parkinson’s disease DOI Creative Commons
D. James Surmeier, Shenyu Zhai, Qiaoling Cui

et al.

Frontiers in Synaptic Neuroscience, Journal Year: 2023, Volume and Issue: 15

Published: June 28, 2023

For roughly the last 30 years, notion that striatal dopamine (DA) depletion was critical determinant of network pathophysiology underlying motor symptoms Parkinson's disease (PD) has dominated field. While basal ganglia circuit model underpinning this hypothesis been great heuristic value, itself never directly tested. Moreover, studies in couple decades have made it clear fails to incorporate key features ganglia, including fact DA acts throughout not just striatum. Underscoring point, recent work using a progressive mouse PD shown alone is sufficient induce parkinsonism and restoration extra-striatal signaling attenuates parkinsonian deficits once they appear. Given broad array discoveries field, time for new determinants disability PD.

Language: Английский

Citations

10