Biology subjects

Shaham, N.

Publications and source records attributed to Shaham, N..

3 recordsLinked to original sources

bioRxiv2025

Stabilization of memory on neural manifolds through multiple synaptic time scales

Working memory is commonly thought to be implemented in the brain by the persistent activity of attractor neural networks. Compelling evidence exists for the implementation of such memory networks in vertebrate and invertebrate brains. Naively, retention of continuous memories in attractor networks - as observed in the head direction systems of mammals and insects and the oculomotor integrator in vertebrates - is particularly sensitive to neural stochasticity, because noise can continuously shift the activity of the network along its manifold of steady states. As these random drifts accumulate over time, the stored memory degrades. Small networks, such as the head direction system of insects, are particularly prone to this form of memory deterioration, and it is unclear how such networks can maintain their persistent state over behaviorally relevant timescales. Here we identify a neural mechanism that effectively counteracts random drifts by employing a combination of slow excitatory and fast inhibitory synaptic connections. We show that the proposed mechanism can substantially decrease diffusivity in linear and nonlinear networks. Hence, the mechanism offers a plausible explanation for how the brain can stably store memories of continuous quantities, despite the ubiquity of noise. Finally, we identify how to engineer connectivity such that variability in neural activity perpendicular to the attractor is unaffected by the stabilization mechanism. This scheme allows for tight confinement of neural activity patterns to a low dimensional manifold, as well as the rapid relaxation of transient modes of activity back into the attractor. The theory suggests that neurons in head direction cell networks that are commonly thought to be utilized for velocity integration may also aid in stabilization against noise-driven motion.

neuroscience↗

bioRxiv2021

Stochastic Consolidation of Lifelong Memory

Humans have the remarkable ability to continually store new memories, while maintaining old memories for a lifetime. How the brain avoids catastrophic forgetting of memories due to interference between encoded memories is an open problem in computational neuroscience. Here we present a model for continual learning in a recurrent neural network combining Hebbian learning, synaptic decay and a novel memory consolidation mechanism. Memories undergo stochastic rehearsals with rates proportional to the memorys basin of attraction, causing self-amplified consolidation, giving rise to memory lifetimes that extend much longer than synaptic decay time, and capacity proportional to a power of the number of neurons. Perturbations to the circuit model cause temporally-graded retrograde and anterograde deficits, mimicking observed memory impairments following neurological trauma.

neuroscience↗

bioRxiv2021

Fixational drift is driven by diffusive dynamics in central neural circuitry

During fixation and between saccades, our eyes undergo diffusive random motion called fixational drift [1]. The role of fixational drift in visual coding and inference has been debated in the past few decades, but the mechanisms that underlie this motion remained unknown. In particular, it has been unclear whether fixational drift arises from peripheral sources, or from central sources within the brain. Here we show that fixational drift is correlated with neural activity, and identify its origin in central neural circuitry within the oculomotor system. We analyzed a large data set of ocular motoneuron (OMN) recordings in the rhesus monkey, alongside precise measurements of eye position [2, 3], and found that most of the variance of fixational eye drifts must arise upstream of the OMNs. The diffusive statistics of the motion points to the oculomotor integrator, a memory circuit responsible for holding the eyes still between saccades, as a likely source of the motion. Theoretical modeling, constrained by the parameters of the primate oculomotor system, supports this hypothesis by accounting for the amplitude as well as the statistics of the motion. Thus, we propose that fixational ocular drift provides a direct observation of diffusive dynamics in a neural circuit responsible for storage of continuous parameter memory in persistent neural activity. The identification of a mechanistic origin for fixational drift is likely to advance the understanding of its role in visual processing and inference.

neuroscience↗