Inhibitory plasticity

Tim Vogels discusses the interaction of excitatory and inhibitory plasticity and its implications for learning and stable network activity.

Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity. The sequence-generating circuit in zebra finch HVC provides a reliable timing scaffold for motor output in song and demonstrates a remarkable capacity for unsupervised recovery following perturbation. Ins

…been attributed to excitatory synaptic plasticity and the effect of inhibitory plasticity on memory capacity and recall remains underexplored. To elucidate how inhibitory plasticity affects memory capacity and recall, we compare two inhibitory plasticity rules in spiking autoassociative networks: a homeostatic plasticity rule and a rule that maintains firing rate heterogeneity. Our results

The hippocampus is a key player in learning and memory. However, the microcircuit logic that endows the hippocampus with these capabilities remains virtually unknown. Hippocampal neurons exhibit tremendous diversity in terms of their gene expression, connectivity, and intrinsic properties. Recent work suggests that this diversity is rooted in the temporal progression of embryonic development, with neurons born at the same gestational timepoint exhibiting shared input and output connectivity, as well as correlated activity in-vivo. In this work, we combined embryonic birthdating of CA3-CA1 neur

Inhibitory neurons (I), akin to their excitatory (E) counterparts, adjust synaptic efficacies in response to neural activity. Contrary to the traditional view of inhibition as a purely global activity modulator, recent studies highlighted the importance of specificity in (E/I) wiring and motif structures. These motifs, encompassing mutual and lateral inhibition, take essential functional roles in tuning of receptive fields, learning of representations, predictive coding, and in the regulation of the degrees of freedom of the network’s dynamics (i.e. the network dimensionality). Despite numero

Replay of neuronal activity patterns during sharp-wave ripples (SPWr) is a crucial event for memory consolidation. Past events reactivation can be estimated by the explained variance (EV) between the pairwise neuronal correlations of consecutive behavioral episodes.We have investigated the physiological mechanisms of hippocampal reactivation in mice after the exploration of a linear maze. We found a strong reactivation during the subsequent SPWr events (EV=33.2±17.15%; n=19sessions), which did not correlate with changes in the average firing rate or the ripple rate of pyramidal neurons or inte

…by a Hebbian spike time-dependent plasticity

…challenges inferring the synaptic plasticity rule from the experimental data. Here, we considered a biologically plausible synaptic plasticity model, and explored its key properties that enable efficient inference. As a recent theoretical work suggested the firing rates to be the most critical under in vivo-like conditions, we considered firing-rate dependent plasticity and its matrix representation. To enable efficient recovery

The hippocampus reinstates learned sequences in temporally compressed "replay" epochs inside sharp-wave ripples (SWRs), which occur while an animal is at rest. Replay is required for the long-term consolidation of memories. However, the content of these replay episodes, and in particular whether specific experiences or a task-optimized representation of the world are consolidated, remains controversial. Using two-photon calcium imaging of CA3 outputs combined with simultaneous local field potential (LFP) recordings, our group has shown that the statistics of replay deviate significantly from t

Synaptic plasticity is known to be a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the nature of the local changes at individual synapses and their link with emerging network-level computations remain unclear. In theoretical work, synaptic plasticity is often modelled with unsupervised local plasticity rules. However, deriving a complete set of functional plasticity rules analytically will require divine intuition and numerous assumptions. Here, we approach the problem numerically. We show how to deduce plasticity rules in silico with supervi

Spike trains recorded from the cortex of behaving animals can be complex, highly variable from trial to trial, and therefore challenging to interpret. A fraction of cells exhibit obvious trial-averaged, task-related responses such as pure tone frequency tuning in auditory cortex. However, a substantial number of cells do not appear to fire in a task-related manner and are often neglected from analysis. Previous work used a novel single-trial, spike-timing-based analysis to show that both classically and non-classically responsive cortical neurons contain significant information about sensory s

…we show that spike-timing-dependent plasticity of inhibitory-to-excitatory synapses generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a n