Topic: synaptic noise

Recent work (Aitchison et al. 2021) suggests that synaptic plasticity performs Bayesian inference and that, moreover, variability in synaptic efficacy might reflect uncertainty in the synaptic weight. Here, we consider whether similar phenomena emerge merely from minimizing synapse energetic costs during learning. Following established biophysical principles, we assume that noisy synaptic transmission is energetically cheaper than precise transmission. However, stochastic synapses may be detrimental to performance because noisy transmission adds randomness to a neural network’s activity, and s

Representational drift has been observed in different parts of the nervous system. Nevertheless a complete mechanistic understanding about it is missing. In a recent experimental study in the olfactory system, recordings from the piriform cortex demonstrate drift in the representation of a single olfactory stimulus, despite apparently stable odor identification. Such drift is characterized by a decay in the self-similarity of the stimulus representation vector as a function of the time between the recordings. Additionally, the rate of the decay was shown to be smaller for more frequent stimuli

Mechanisms of representational drift are still under investigation. Under one set of hypotheses, drift is a result of noisy continual learning in the brain. Previous computational studies have demonstrated that drift could occur under different types of noise such as synaptic noise, or sample stochasticity. However, exact contribution of various noises remains elusive. For example, it’s unclear what’s the contribution of stimuli that are relevant for the task, and the ones that are continually present in the environment but the agent learns to ignore in a context. In this study, using theory a

Synaptic integration is crucial for inter-neuronal communication, and excitatory-inhibitory integration is utterly important in lateral superior olive (LSO) neurons, enabling sound localization. For this purpose, LSO neurons integrate excitatory inputs from the ipsilateral cochlear nucleus (CN) with inhibitory inputs from the medial nucleus of the trapezoid body (MNTB). The strength and number of excitatory and inhibitory inputs show notable differences. The convergence of multiple excitatory input fibers (~10‑40) is a source for synaptic noise in the spike driving inputs. We focused on how sy

Behaviourally relevant sensory input is routinely transmitted and processed in biological networks, despite perpetual perceptually unrelated external, and spontaneously generated internal activity. Such activity has traditionally been understood as noise. In feed-forward networks, such noise can pose a challenge, because disambiguating stimulus-evoked activity and noise in deeper layers becomes increasingly difficult, for example in pattern classification tasks. On the other hand, the beneficial effects of noise for stochastic regularization and preventing co-adaptation were recognized in deep

The locus coeruleus (LC) is the primary source of norepinephrine in the brain and is known to modulate brain-wide arousal state. Recent evidence suggests that it also regulates immediate attentional responses by modulating the gain or reset of downstream cortical networks to optimize behavioral outcomes. Accordingly, cortical regions of high cognitive function, such as the medial prefrontal cortex (mPFC), are thought to directly influence LC output for this purpose. However, physiological evidence for monosynaptic connections between mPFC and LC neurons and their properties are still limited.