Topic: spike-timing-dependent plasticity

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

Animals depend on fast and reliable detection of novel stimuli in their environment. Neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular, and synaptic mechanisms underlie those responses. Here, 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

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 trial-averaged responses with obvious task-related features such as pure tone frequency tuning in auditory cortex. However, a substantial number of cells (including cells in primary sensory cortex) do not appear to fire in a task-related manner and are often neglected from analysis. We recently used a novel single-trial, spike-timing-based analysis to show that both classically responsive and non-classically responsiv

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

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

Arbor is a software library that serves to efficiently simulate large-scale networks of neurons with detailed morphological structure. It combines facilitated customization of neuronal and synaptic mechanisms with high-performance computing, enabling to use diverse backend architectures such as multi-core CPU and GPU systems [1]. On the one hand, many computational and experimental 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

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. The timescales of neural activity are not only affected by bottom-up sensory inputs, but also by top-down inputs originating from corticocortical and neuromodulatory pathways. In addition, cortical circuits comprise a large diversity of cell types connected in non-random ways, which are the subject of profound plasticity dynamics. Due to this complexity, understanding how top-down projections affect the timescales of