STDP

Plasticity rules governing synaptic changes vary widely across species, brain regions, and brain states. Despite this variability, these rules often give rise to neural circuits with similar structural motifs and functional properties, a phenomenon known as degeneracy. While functional degeneracy---where different plasticity rules result in similar network functions---has been widely studied, the structural aspects of degeneracy remain underexplored. Structural degeneracy refers to cases where distinct plasticity rules produce neural circuits with similar connectivity patterns. To investigate

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

…A STDP-based rule is used to learn patterns of activat

Stable neural activity plays a pivotal role in various cognitive, behavioral, and physiological functions, including information processing, memory consolidation, behavioral regulation, and attentional mechanisms. The orchestration of such stability is governed by the intricate interplay of synaptic connections within neural populations. Extensive theoretical and empirical investigations have underscored the influential role of synaptic weights in shaping both the collective dynamics and the fluctuations of neural activity. A salient inquiry arising from this understanding pertains to the mech

Intelligent behavior depends on the brain’s ability to anticipate future events. However, the learning rules that enable neurons to predict and fire ahead of sensory inputs remain largely unknown. We propose a plasticity rule based on pre-dictive processing, where the neuron learns a low-rank model of the synaptic input dynamics in its membrane potential. Neurons thereby amplify those synapses that maximally predict other synaptic inputs based on their temporal relations, which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local inf

Understanding the trade-off between plasticity and stability–how neural networks form new memories while avoiding runaway potentiation or fast overwriting of memories–is a fundamental problem in neuroscience. Previous theoretical studies that relied on non-plausible plasticity rules did not reproduce realistic synaptic weight distributions or neural activity statistics and predicted fast forgetting times. We revisit this problem by investigating the implications of a multiplicative calcium-based plasticity rule in a recurrent network. This rule was recently fitted to experiments done in physio

Memories may be encoded in the brain via assemblies, groups of neurons that coactivate upon memory recall. The concept of Hebbian plasticity suggests that these assemblies are generated through synaptic plasticity, strengthening the recurrent connections within select groups of neurons that receive correlated stimulation. To remain stable in absence of such stimulation, these assemblies need to be self-reinforcing under the plasticity rule. Previous models of such assembly generation and maintenance required mechanisms of heterosynaptic competition or homeostatic plasticity often with biologic

…whether plasticity rules such as STDP measured in vitro apply in vivo. Network models with STDP predict that activity patterns (e.g., place-cell spatial selectivity) should change much faster than observed experimentally. We address this gap by investigating a nonlinear calcium-based plasticity rule fit to experiments done in physiological conditions. In this model, LTP and LTD result from intracellular calcium transients arising almost exclusively from synchronous c

…on spike timing dependent plasticity (STDP) and examine under what conditions this unsupervised learning rule can facilitate the propagation of rhythmic activity downstream i