Synaptic strengths

Recent studies have shown that synaptic spines undergo spontaneous size fluctuations, a phenomenon hypothesized to be critical for understanding synaptic plasticity and neural network stability. In this work, we develop a model for spine size dynamics using a simple stochastic process based on minimal biological assumptions. The model effectively captures the key characteristics of spontaneous spine fluctuations, particularly replicating the observed lognormal distribution of spine sizes. We then train an artificial neural network in which synaptic strengths fluctuate according to our model. T

Recent studies have shown that synaptic spines undergo spontaneous size fluctuations, a phenomenon that is hypothesized to be critical to understanding synaptic plasticity and neural network stability [1,2]. In this work, we develop a model for spine size dynamics using a simple stochastic process based on minimal biological assumptions. This model effectively captures the key characteristics of spontaneous spine fluctuations, particularly replicating the observed lognormal distribution of spine sizes. We then use an artificial neural network structure where synaptic strengths fluctuate accor

In networks of neurons, correlations and synaptic strengths are heavy-tailed, with a small number of neurons interacting much more strongly than the vast majority of pairs. Yet, it remains unclear whether, and how, such heavy-tailed connectivity emerges from simple underlying mechanisms. Building upon recent advances in neuroimaging, here we show that the correlations between thousands of neurons in the mouse visual cortex have heavy-tailed distributions spanning three decades in strength. Moreover, we find that these distributions are robust to variations in the visual stimuli, even persistin

Despite the recent success of deep learning in artificial intelligence, the lack of biological plausibility and labeled data in natural learning still poses a challenge in understanding biological learning. At the other extreme lies Hebbian learning, the simplest local and unsupervised one, yet considered to be computationally less efficient. In this talk, I would introduce a novel method to infer the form of Hebbian learning from in vivo data. Applying the method to the data obtained from the monkey inferior temporal cortex for the recognition task indicates how Hebbian learning changes th

…the basis of highly variable cellular, synaptic, and circuit properties. This is crucial for proper nervous system function throughout an animal’s life in the face of growth, perturbations, and molecular turnover. But how can reliable output arise from neurons and synapses whose parameter vary between individuals in a population, and within an individual over time? I will review how a combination of experimental and computational methods can be used to examine how neuron and network function depends on the und

Synaptic scaling is a homeostatic normalization mechanism that preserves relative synaptic strengths by adjusting them with a common factor. This multiplicative change is believed to be critical, since synaptic strengths are involved in learning and memory retention. Further, this homeostatic process is thought to be crucial for neuronal stability, playing a stabilizing role in otherwise runaway Hebbian plasticity [1-3]. Synaptic scaling requires a mechanism to sense total neuron activity and globally adjust synapses to achieve some activity set-point [4]. This process is relatively slow, whic