Biology subjects

Raman, D. V.

Publications and source records attributed to Raman, D. V..

4 recordsLinked to original sources

bioRxiv2025

Learning in nonstationary environments with minimal neuralcircuitry

Animals must learn and make predictions in a world that is not only uncertain, but shaped by unmodelled contingencies: unknown unknowns. Despite this, current theories of how this is achieved are based on optimal (Bayesian) strategies in simple settings, where it is possible to fully represent a model of the world and infer its state based on sensory data. This could be impractical in situations where an animal has limited prior experience and cognitive capacity, particularly in short-lived animals with small brains. Using the neural circuitry of Drosophila as inspiration, we derive a learning framework, which we call Hetlearn, for coping in such situations. Hetlearn sacrifices asymptotic optimality on idealised inference tasks, making it less fragile to abrupt changes in environmental statistics and providing more accurate state information than standard Bayesian procedures when data are limited. Hetlearn explains key physiological and anatomical features of the Drosophila Mushroom body (MB), and predicts behavioural features of learning that are conserved across species, such as second-order conditioning and reversal learning.

neuroscience↗

bioRxiv2020

Optimal synaptic dynamics for memory maintenance in the presence of noise

Synaptic connections in many brain areas have been found to fluctuate significantly, with substantial turnover and remodelling occurring over hours to days. Remarkably, this flux in connectivity persists in the absence of overt learning or behavioural change. What proportion of these ongoing fluctuations can be attributed to systematic plasticity processes that maintain memories and neural circuit function? We show under general conditions that the optimal magnitude of systematic plasticity is typically less than the magnitude of perturbations due to internal biological noise. Thus, for any given amount of unavoidable noise, 50% or more of total synaptic turnover should be effectively random for optimal memory maintenance. Our analysis does not depend on specific neural circuit architectures or plasticity mechanisms and predicts previously unexplained experimental measurements of the activity-dependent component of ongoing plasticity.

neuroscience↗

bioRxiv2019

Stable task information from an unstable neural population

Over days and weeks, neural activity representing an animals position and movement in sensorimotor cortex has been found to continually reconfigure or drift during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. We show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioural variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

neuroscience↗

bioRxiv2018

Fundamental bounds on learning performance in neural circuits

How does the size of a neural circuit influence its learning performance? Intuitively, we expect the learning capacity of a neural circuit to grow with the number of neurons and synapses. Larger brains tend to be found in species with higher cognitive function and learning ability. Similarly, adding connections and units to artificial neural networks can allow them to solve more complex tasks. However, we show that in a biologically relevant setting where synapses introduce an unavoidable amount of noise, there is an optimal size of network for a given task. Beneath this optimal size, our analysis shows how adding apparently redundant neurons and connections can make tasks more learnable. Therefore large neural circuits can either devote connectivity to generating complex behaviors, or exploit this connectivity to achieve faster and more precise learning of simpler behaviors. Above the optimal network size, the addition of neurons and synaptic connections starts to impede learning performance. This suggests that overall brain size may be constrained by the need to learn efficiently with unreliable synapses, and may explain why some neurological learning deficits are associated with hyperconnectivity. Our analysis is independent of specific learning rules and uncovers fundamental relationships between learning rate, task performance, network size and intrinsic noise in neural circuits.

neuroscience↗