Topic: Hebbian plasticity

Connectomic traces of Hebbian plasticity in the entorhinalhippocampal system

Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning. Here, we propose that Hebbian plasticity is fundamental for computations in biological neural systems. We introduce a novel spiking neural network (SNN) architecture that is enriched by Hebbian synaptic plasticity. We experimentally

Understanding how neural circuits generate sequential activity is a longstanding challenge. While theoretical models have previously demonstrated how sequences can be stored as memories with Hebbian plasticity rules, these models considered only a particular Hebbian rule that is not observed in experiments. Here we consider a recently introduced model for arbitrary Hebbian plasticity rules and show how the choice of these rules and of the neural activity patterns influences memorized sequence retrieval. In particular, we derive a general theory that predicts the speed of sequence traversal. Th

The free-energy principle and active inference have received a significant attention in the fields of neuroscience and machine learning. However, it remains to be established whether active inference is an apt explanation for any given neural network that actively exchanges with its environment. To address this issue, we show that a class of canonical neural networks of rate coding models implicitly performs variational Bayesian inference under a well-known form of partially observed Markov decision process model (Isomura, Shimazaki, Friston, Commun Biol, 2022). Based on the proposed theory, w

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

Synaptic changes driven by Hebbian plasticity are unstable and require compensatory mechanisms to homeostatically control neuronal activity. Synaptic scaling regulates the synaptic efficacies to bring individual neurons to a set firing regime [1]. However, the timescales of synaptic scaling are too slow to counterbalance the rapid growth due to Hebbian mechanisms [2]. Heterosynaptic plasticity that operates on timescales similar to Hebbian processes can prevent runaway potentiation [3] but its ability to homeostatically control activity is uncertain. Here, we introduce aggregate scaling, a si

The brain computes internal representations by applying highly recurrent dynamics to feed-forward input. Such dynamics may be viewed as analogous to the inference step in latent variables models, where one usually follows the gradient of the latent posterior distribution until a stable state is reached. At the stable state, when this gradient has approximately zero norm, a parameter optimization step can be applied to slightly increase the likelihood of the current sample under the model. Here we suggest this procedure, which closely matches the expectation-maximization (EM) algorithm, can be

Various working memory (WM) models based on a diverse set of biologically plausible synaptic and neural plasticity mechanisms have been proposed. We show that these proposed short-term plasticity mechanisms may not necessarily be competing explanations, but instead yield interesting functional interactions on a wide set of WM tasks and enhance the biological plausibility of spiking neural network models of WM. While reductionist models (WM explained by one particular mechanism) are theoretically appealing and have increased our understanding of specific mechanisms, they are insufficient. WM mo

The hippocampus plays a central role in memory processing. In order to avoid deleterious interference between stored and ongoing experience, theoretical considerations require a separation between phases of memory encoding and recall within the same neural circuit, putatively controlled by hippocampal theta oscillations [1]. However, the neural mechanisms subserving this separation remain unknown. Classical models either remain moot about these mechanisms [2], or assume purpose-built neuromodulatory interactions that are in conflict with biologically realistic timescales of synaptic modulation