Spike-timing-dependent plasticity

Flexible behaviors are critical for survival in diverse and dynamic environments. However, the synaptic mechanisms that underlie flexible behaviors are largely unknown. Neuronal responses during behavior are diverse, ranging from reliable ‘classical’ responses (e.g. pure tone tuning in auditory cortex) to irregular ‘non-classically responsive’ firing. Previous work has shown that both classically and non-classically responsive cortical neurons contain significant information about sensory stimuli and behavioral decisions and are necessary for task performance suggesting that non-classically re

…Activity-dependent synaptic plasticity shapes and refines this intricate relationship. Connectivity motifs, small subnetworks of a few neurons, have been found to play a key role in linking circuit structure to the dynamical properties of neural activity, such as covariability and dimensionality. Significant progress has been made in measuring precise neural connectivity; multipatch experiments and recent connectomic studies have quantified thr

…mediated by both synaptic plasticity and neuromodulation. Traditional methods for extracting connectivity from activity focus on modeling static connectivity. On the other hand, recent transformer-based models are able to capture nonstationary interactions, but their deep layered structures and nonlinear attention mechanisms make them challenging to interpret, particularly in terms of

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

Neural activity in the cortex exhibits a wide range of timescales, allowing animals to respond to rapidly changing sensory stimuli while maintaining information in working memory. Recent studies show how top-down inputs representing locomotive state modulates these timescales differently across visual areas in mice. In particular, the sign of correlations between speed and neural activity varies by region, highlighting the interplay between bottom-up sensory inputs, top-down pathways and connectivity motifs in shaping cortical dynamics. In addition, cortical circuits comprise a large diversity

All-optical techniques play a pivotal role in the direct interrogation of biological neural networks and the investigation of their computational properties. Such techniques combine spatiotemporally precise optogenetic stimulation at cellular resolution to manipulate neuronal states in the network with optical imaging of fluorescent genetically-encoded indicators for simultaneous readout of neuronal activity [1, 2]. The biophysical models of various optogenetic tools have been studied computationally in single neurons, accurately fitting experimental data [3, 4]. However, the effects of optoge

To survive in a changing world, animals often need to suppress a previously learnt, but now obsolete, behavior and acquire a new behavior. This process is known as reversal learning (RL). The neural mechanisms underlying RL in spatial navigation has received limited attention and it remains unclear what neural mechanisms maintain behavioral flexibility. To address this issue, we extended an existing closed-loop simulator of spatial navigation and learning [1], based on spiking neural networks. The activity of place cells and boundary cells were fed as inputs to action selection neurons, which

Understanding the fine-scale connectivity structure of neural networks is key to deciphering their behaviour. Motifs in network connectivity are instrumental in relating circuit structure to dynamical properties of neural activity, such as covariability and dimensionality [1]. Experiments have made significant progress in measuring precise neural connectivity: multi-patch experiments, as well as more recent studies in connectomics, have found that certain types of three-neuron (triplet) connectivity motifs are over- or underrepresented relative to chance (Fig.1A, [2,3]). However, it is still u

While inhibition is a building block of biological neural networks, its role in synaptic plasticity is yet to be fully understood. So far its role has only been addressed in rate and spike-timing based models (Wilmes et al., 2016; Agnes & Vogels,2024; Miehl & Gjorgijeva, 2022). Yet, experimental results show that the hyperpolarising effect of inhibition on the sub-threshold membrane potential can alter the expression of plasticity at excitatory synapses (Hayama et al. 2013; Paille et al. 2013). To address this gap, we explore the effects of inhibition in three voltage-dependent models: the vol

Astrocytes play a crucial role in regulating synaptic strength and orchestrating synaptic plasticity, which is fundamental for learning and memory. However, the specific mechanisms by which astrocytes and their neuroactive transmitters, like D-serine, control these processes remain largely unexplored. D-serine, a co-agonist for the NMDA receptor, has garnered increasing interest as a potential regulatory molecule in synaptic plasticity. Given its activity-dependent release mediated by astrocytes, D-serine is a key player in balancing long-term synaptic depression (LTD) and potentiation (LTP) d

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

Experimental studies have demonstrated that spontaneous task-independent replay of sequences encoding a learned route during sharp-wave–ripple (SWR) events can be beneficial for consolidating path memory [1] and discovering new behavioral strategies, such as forming novel space-action representations, finding shortcuts, and integrating and associating different routes[2]. Additionally, It has been shown experimentally that hippocampal theta sequences, generated during active exploration of the environment, play a crucial role in selecting appropriate actions in goal-directed tasks[3,4]. Howeve

…have been shown to drive synaptic plasticity. For instance, spike-timing dependent plasticity (STDP), a phenomenological description of long-term synaptic plasticity, can shape neural circuits based on spike-bas

…studies show the importance of diverse plasticity processes in shaping the dynamics of neuronal networks, being the basis of cognitive processes such as learning and memory. On the other hand, in recent years, stud

Creating place fields in place cells is crucial for forming spatially dependent episodic memories in the brain, with the hippocampus playing a central role. Specifically, we focus on CA1 pyramidal neurons, which receive inputs from the CA3 and entorhinal cortex (EC). No widely accepted network-based theory for place field formation exists, but many models offer mechanisms for place-field firing. One type of hypotheses proposes that it results from summated output of spatially tuned cells, like grid cells or boundary vector cells. Another suggests CA1 place fields are replicate of CA3 place fie

…which are the subject of profound plasticity dynamics. Due to this complexity, understanding how top-down projections affect the timescales of

Despite large advances in neuroscientific research, understanding the spontaneous replay of patterns of activity and the emergence of diverse collective dynamics in the cortex from neuronal interactions is still a key challenge in neuroscience. This research elucidates how static structural connections may give rise to the emergence of a large repertoire of dynamic patterns in spontaneous brain activity on a large scale.We employ a modular neural model. Each module, consisting of an ensemble of leaky integrate and fire (LIF) neurons, represents a cortical region, to mimic neuronal cortical dyn

Having two ears enables us to localize sound sources by exploiting interaural time differences (ITDs) in sound arrival. Principal neurons of the medial superior olive (MSO) are sensitive to ITD, and each MSO neuron responds optimally to a best ITD (bITD). In many cells, especially those tuned to low sound frequencies, these bITDs correspond to ITDs for which the contralateral ear leads, and are often larger than the ecologically relevant range, defined by the ratio of the interaural distance and the speed of sound. Using in vivo recordings in gerbils, we found that shortly after hearing onset

…Our prior work has involved exploring spike-timing-dependent plasticity using a synapse model with astrocyte contributions (Manninen et al., 2020). Additionally, we have studied in detail existing astrocyte models tailored for various brain areas and functions (M

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

…However, the plasticity of these conne

Current voltage-dependent plasticity models assume that any plastic event needs a postsynaptic action potential and use induction mechanisms that are insensitive to postsynaptic voltage fluctuations. However, experiments have demonstrated that inhibitory inputs can affect plasticity updates, likely through voltage-dependent effects. To address such findings, here we introduce the novel Voltage-Dependent Pathway model (VDP) where the induction variables track the membrane potential, increasing its sensitivity to fast fluctuations. We first confirm that this model produces spike-timing dependent

Neuronal assemblies — groups of neurons with strong recurrent connectivity — are thought to be the basic building blocks of perception and memory in the brain: representations of specific concepts. Activation of an assembly signals the presence of its associated concept to downstream targets. An efficient implementation should allow similar concepts to be represented by similar assemblies, sharing a subset of neurons. However, learning overlapping assemblies in computational models is difficult. Similar assemblies tend to either merge or diverge during the learning process. The same mechanism

…length and diameter may compromise spike-timing-dependent plasticity (STDP) by dispersing action potential timing, preventing synaptic potentiation and depression. Activity-dependent myelination (ADM), a form of brain plasticity relying on neuron-glia feedback, has been shown to normalize conduction times across different axons through adaptive, neuron-activity-dependent changes in axonal conduction velocity (CV). While historically seen as a mere compensat