scholarly journals Fast and flexible sequence induction in spiking neural networks via rapid excitability changes

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Rich Pang ◽  
Adrienne L Fairhall
Keyword(s):  
Neural Networks ◽  
Cognitive Flexibility ◽  
Long Term Potentiation ◽  
Spiking Neural Networks ◽  
Intrinsic Excitability ◽  
Stimulus Response ◽  
Term Potentiation ◽  
Spiking Network ◽  
Sequence Induction

Cognitive flexibility likely depends on modulation of the dynamics underlying how biological neural networks process information. While dynamics can be reshaped by gradually modifying connectivity, less is known about mechanisms operating on faster timescales. A compelling entrypoint to this problem is the observation that exploratory behaviors can rapidly cause selective hippocampal sequences to ‘replay’ during rest. Using a spiking network model, we asked whether simplified replay could arise from three biological components: fixed recurrent connectivity; stochastic ‘gating’ inputs; and rapid gating input scaling via long-term potentiation of intrinsic excitability (LTP-IE). Indeed, these enabled both forward and reverse replay of recent sensorimotor-evoked sequences, despite unchanged recurrent weights. LTP-IE ‘tags’ specific neurons with increased spiking probability under gating input, and ordering is reconstructed from recurrent connectivity. We further show how LTP-IE can implement temporary stimulus-response mappings. This elucidates a novel combination of mechanisms that might play a role in rapid cognitive flexibility.

scholarly journals Fast and Flexible Sequence Induction In Spiking Neural Networks Via Rapid Excitability Changes

2018 ◽  
Author(s):  
Rich Pang ◽  
Adrienne Fairhall
Keyword(s):  
Neural Networks ◽  
Cognitive Flexibility ◽  
Structural Changes ◽  
Network Dynamics ◽  
Long Term Potentiation ◽  
Recurrent Network ◽  
Task Demands ◽  
Stimulus Response ◽  
Term Potentiation ◽  
Sequence Induction

AbstractCognitive flexibility, the adaptation of mental processing to changes in task demands, is thought to depend on biological neural networks’ ability to rapidly modulate the dynamics governing how they process information. While extensive work has elucidated how network dynamics can be reshaped by slowly occurring structural changes, e.g. the gradual modification of recurrent synaptic patterns, much less is known about how dynamics might be reconfigured over faster timescales of seconds. One compelling example of rapid and selective modulation of network dynamics potentially involved in cognitive flexibility is observed in rodent hippocampus, where short bouts of exploratory behavior cause new activity sequences to preferentially “replay” during subsequent awake rest periods without continued sensory input. Fast mechanisms for selectively biasing sequential activity through networks, however, remain unknown. Using a spiking neural network model, we asked whether a simplified version of sequence replay could arise from three biophysically plausible components: recurrent, spatially organized connectivity; homogeneous, stochastic “gating” inputs; and rapid, activity-dependent scaling of gating input strengths, based on a phenomenon known as long-term potentiation of intrinsic excitability (LTP-IE). Indeed, these enabled both forward and reverse replay of flexible sequences reflecting recent behavior, despite unchanged recurrent weights. Specifically, activation-triggered LTP-IE “tags” neurons in the recurrent network by increasing their spiking probability when gating input is applied, and the sequential ordering of spikes is reconstructed by the existing recurrent connectivity. In a proof-of-concept demonstration, we also show how LTP-IE-based sequences can implement temporary stimulus-response mappings in a straightforward manner. These results elucidate a simple yet previously unexplored combination of biological mechanisms that converge in hippocampus and suffice for fast and flexible reconfiguration of sequential network dynamics, suggesting their potential role in cognitive flexibility over rapid timescales.

scholarly journals Knockdown of MicroRNA-1 in the Hippocampus Ameliorates Myocardial Infarction Induced Impairment of Long-Term Potentiation

2018 ◽  
Vol 50 (4) ◽  
pp. 1601-1616 ◽  
Author(s):  
Ji-Chao Ma ◽  
Ming-Jing Duan ◽  
Ke-Xin Li ◽  
Das Biddyut ◽  
Shuai Zhang ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Sham Group ◽  
Long Term Potentiation ◽  
Stimulus Response ◽  
Protein Levels ◽  
Term Potentiation ◽  
Passive And Active Avoidance ◽  
Related Proteins

Backgrounds/Aims: It has been reported that myocardial infarction (MI) is a risk factor for vascular dementia. However, the molecular mechanism remains largely unknown. Methods: MI mice were generated by ligation of the left coronary artery (LCA) for 4 weeks. Passive and active avoidance tests were performed to evaluate the cognitive ability of MI mice. A theta-burst stimulation (TBS) protocol was applied to elicit long-term potentiation (LTP) of the perforant pathway-dentate gyrus synapse (PP-DG). Western blot analysis was employed to assess protein levels. Results: In this study, we demonstrated that after 4 weeks of MI, C57BL/6 mice had significantly impaired memory. Compared with the sham group, in vivo physiological recording in the MI group revealed significantly decreased amplitude of population spikes (PS) with no effect on the latency and duration of the stimulus-response curve. The amplitude of LTP was markedly decreased in the MI group compared with the sham group. Further examination showed that the expression of the TBS-LTP-related proteins BDNF, GluA1 and phosphorylated GluA1 were all decreased in the MI group compared with those in the sham group. Strikingly, all these changes were prevented by hippocampal stereotaxic injection of an anti-miR-1 oligonucleotide fragment carried by a lentivirus vector (lenti-pre-AMO-1). Conclusion: MI induced cognitive decline and TBS-LTP impairment, and decreased BDNF and GluA1 phosphorylation levels from overexpression of miR-1ated were involved in this process.

ADAPTIVE CEREBELLAR SPIKING MODEL EMBEDDED IN THE CONTROL LOOP: CONTEXT SWITCHING AND ROBUSTNESS AGAINST NOISE

2011 ◽  
Vol 21 (05) ◽  
pp. 385-401 ◽  
Author(s):  
N. R. LUQUE ◽  
J. A. GARRIDO ◽  
R. R. CARRILLO ◽  
S. TOLU ◽  
E. ROS
Keyword(s):  
Degrees Of Freedom ◽  
Mossy Fibers ◽  
Long Term Potentiation ◽  
Biologically Inspired ◽  
Context Switching ◽  
Term Potentiation ◽  
Spiking Network ◽  
Three Degrees Of Freedom ◽  
Spiking Model

This work evaluates the capability of a spiking cerebellar model embedded in different loop architectures (recurrent, forward, and forward&recurrent) to control a robotic arm (three degrees of freedom) using a biologically-inspired approach. The implemented spiking network relies on synaptic plasticity (long-term potentiation and long-term depression) to adapt and cope with perturbations in the manipulation scenario: changes in dynamics and kinematics of the simulated robot. Furthermore, the effect of several degrees of noise in the cerebellar input pathway (mossy fibers) was assessed depending on the employed control architecture. The implemented cerebellar model managed to adapt in the three control architectures to different dynamics and kinematics providing corrective actions for more accurate movements. According to the obtained results, coupling both control architectures (forward&recurrent) provides benefits of the two of them and leads to a higher robustness against noise.

Long-term potentiation of intrinsic excitability in trigeminal motoneurons

2010 ◽  
Vol 1312 ◽  
pp. 32-40 ◽  
Author(s):  
Reiko Okamoto ◽  
Akifumi Enomoto ◽  
Hidehiko Koizumi ◽  
Susumu Tanaka ◽  
Kohji Ishihama ◽  
...  
Keyword(s):  
Long Term Potentiation ◽  
Intrinsic Excitability ◽  
Term Potentiation ◽  
Trigeminal Motoneurons

scholarly journals Selective Presynaptic Propagation of Long-Term Potentiation in Defined Neural Networks

2000 ◽  
Vol 20 (9) ◽  
pp. 3233-3243 ◽  
Author(s):  
Hui-zhong W. Tao ◽  
Li I. Zhang ◽  
Guo-qiang Bi ◽  
Mu-ming Poo
Keyword(s):  
Neural Networks ◽  
Long Term Potentiation ◽  
Term Potentiation

Perceptual Correlate of Nociceptive Long-Term Potentiation (LTP) in Humans Shares the Time Course of Early-LTP

2006 ◽  
Vol 96 (6) ◽  
pp. 3551-3555 ◽  
Author(s):  
Thomas Klein ◽  
Walter Magerl ◽  
Rolf-Detlef Treede
Keyword(s):  
Healthy Subjects ◽  
Time Course ◽  
Pain Perception ◽  
Long Term Potentiation ◽  
Afferent Input ◽  
High Frequency Stimulation ◽  
Stimulus Response ◽  
Term Potentiation ◽  
Relay Nucleus

As in neocortex and hippocampus, neurons in the dorsal horn of the spinal cord develop long-term potentiation of synaptic efficacy (LTP) on high-frequency stimulation (HFS) of their afferent input, although how long LTP lasts in this nociceptive relay nucleus has not yet been addressed. Here we studied neurogenic hyperalgesia, a perceptual correlate of nociceptive LTP, in 13 healthy subjects, after HFS (5 × 1 s at 100 Hz) of superficial cutaneous afferents. HFS led to a mean upward shift of the stimulus–response function for pinprick-evoked pain (punctate mechanical hyperalgesia) in all subjects by a factor of 2.5 ( P < 0.001) that lasted undiminished for the initial 1-h observation period. Follow-up tests until the next day revealed that this type of neurogenic hyperalgesia decayed with a t1/2 of 3.3 h (99% CI: 3.1–3.5 h) and disappeared completely within 25.4 h (99% CI: 20.4–31.6 h). Touch-evoked pain (dynamic mechanical allodynia) developed in eight of 13 subjects, decayed with a t1/2 of 2.9 h from the maximum and disappeared within 9.3 h. These findings suggest that a single HFS session induces nociceptive LTP in healthy subjects that corresponds to early-LTP (LTP1), implying primarily posttranslational mechanisms for this type of plasticity of human pain perception.

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