Biology subjects

Benna, M. K.

Publications and source records attributed to Benna, M. K..

10 recordsLinked to original sources

bioRxiv2026

Learning with interacting dendrites improves neuronal familiarity detection

Biological neurons can perform nonlinear computations within their dendrites and support branch-localized plasticity. This raises the possibility that single cells can store memories more efficiently and with less interference by confining synaptic modifications to specific dendrites. We study a parallel-dendrite model performing online familiarity detection and compare three dendrite-update rules during learning: (i) independent thresholding, (ii) an interacting rule that adapts the target local dendritic activation per item, and (iii) an interacting n-winners-take-all (WTA) rule that constrains the number of updated branches per item. The interacting rules substantially improve capacity by limiting variance in memory responses and decorrelating weights across branches -- even when inputs are strongly correlated. These results suggest that competition among dendrites, consistent with resource-limited plasticity mechanisms, can enhance single-cell memory beyond non-interacting schemes.

neuroscience↗

bioRxiv2026

Shared Representation Discovery for Multi-Subject Neural Data Analysis

Neural recordings from different individuals vary substantially even when behavior is broadly shared. The sampled neurons differ, and the same behavior occurs at different times. Standard cross-subject analyses rely on matched time points or anatomical correspondence, which excludes many datasets. We recently introduced Shared Representation Discovery (ShaReD), which identifies neural-behavioral relationships conserved across subjects by learning a shared behavioral projection together with subject-specific neural projections. Here we develop and benchmark this method using synthetic, primate, and rat data. On synthetic data, ShaReD recovers common structure across noise levels, sample sizes, and subject counts, and separates components confined to different groups of subjects. In non-human primate motor cortex, ShaReD identifies kinematic representations that generalize across individuals and across reaching tasks with different movement statistics. In rats navigating a spatial alternation task, ShaReD isolates behavior-aligned directions within the CA1-to-PFC communication subspace. ShaReD thus extends multi-subject analysis to datasets in which comparable behaviors occur without cross-subject temporal correspondence.

neuroscience↗

bioRxiv2025

Social Exclusion Amplifies Behavioral Responses to Physical Pain via Insular Neuromodulation

The "Pain Overlap Theory" (1) proposes that the experience of social pain overlaps with and amplifies the experience of physical pain by sharing parts of the same underlying processing systems (2-6). In humans, the insular cortex has been implicated in this overlap of physical and social pain, but a mechanistic link has not been made (2,4,5,7-9). To determine whether social pain can subsequently impact responses to nociceptive stimuli via convergent electrical signals (spikes) or convergent chemical signals (neuromodulators), we designed a novel Social Exclusion paradigm termed the Fear of Missing Out (FOMO) Task which facilitates a mechanistic investigation in mice. We found that socially-excluded mice display more severe responses to physical pain, disrupted valence encoding, and impaired neural representations of nociceptive stimuli. We performed a systematic biosensor panel and found that endocannabinoid and oxytocin signaling in the insular cortex have opposing responses during trials where mice were attending or not attending to the Social Exclusion events respectively, demonstrating distinct neuromodulatory substrates that underpin different states of Social Exclusion. We also found that intra-insular blockade of oxytocin signaling increased the response to physical pain following Social Exclusion. Together these findings suggest Social Exclusion effectively alters physical pain perception using neuromodulatory signaling in the insular cortex.

neuroscience↗

bioRxiv2024

Parallel Synapses with Transmission Nonlinearities Enhance Neuronal Classification Capacity

Cortical neurons often establish multiple synaptic contacts with the same postsynaptic neuron. To avoid functional redundancy of these parallel synapses, it is crucial that each synapse exhibits distinct computational properties. Here we model the current to the soma contributed by each synapse as a sigmoidal transmission function of its presynaptic input, with learnable parameters such as amplitude, slope, and threshold. We evaluate the classification capacity of a neuron equipped with such nonlinear parallel synapses, and show that with a small number of parallel synapses per axon, it substantially exceeds that of the Perceptron. Furthermore, the number of correctly classified data points can increase superlinearly as the number of presynaptic axons grows. When training with an unrestricted number of parallel synapses, our model neuron can effectively implement an arbitrary aggregate transmission function for each axon, constrained only by monotonicity. Nevertheless, successful learning in the model neuron often requires only a small number of parallel synapses. We also apply these parallel synapses in a feedforward neural network trained to classify MNIST images, and show that they can increase the test accuracy. This demonstrates that multiple nonlinear synapses per input axon can substantially enhance a neurons computational power.

neuroscience↗

bioRxiv2023

Automatic Discovery of Cognitive Strategies with Tiny Recurrent Neural Networks

Normative modeling frameworks such as Bayesian inference and reinforcement learning provide valuable insights into the fundamental principles governing adaptive behavior. While these frameworks are valued for their simplicity and interpretability, their reliance on few parameters often limits their ability to capture realistic biological behavior, leading to cycles of handcrafted adjustments that are prone to research subjectivity. Here, we present a novel modeling approach leveraging recurrent neural networks to discover the cognitive algorithms governing biological decision-making. We show that neural networks with just 1-4 units often outperform classical cognitive models and match larger neural networks in predicting the choices of individual animals and humans across six well-studied reward learning tasks. Critically, we then interpret the trained networks using dynamical systems concepts, enabling a unified comparison of cognitive models and revealing detailed mechanisms underlying choice behavior. Our approach also estimates the dimensionality of behavior and offers insights into algorithms implemented by AI agents trained in a meta-reinforcement learning setting. Overall, we present a systematic approach for discovering interpretable cognitive strategies in decision-making, offering insights into neural mechanisms and a foundation for studying both healthy and dysfunctional cognition.

animal behavior and cognition↗

bioRxiv2022

Amygdalostriatal transition zone neurons encode sustained valence to direct conditioned behaviors

To ensure survival, the brain must rapidly identify threats and maintain defensive behaviors for as long as danger is present. The amygdala has been studied as a key site for fear responses, but responses to threat cues in amygdala neurons are largely transient and shorter in duration than defensive responses observed. Here, we present the amygdalostriatal transition zone (ASt) as a missing piece of the circuits mediating fear responses. Using single-nucleus RNA sequencing (snRNA-seq), we demonstrate that the ASt is genetically distinct from adjacent striatal and amygdalar structures. In vivo electrophysiology and calcium imaging reveal that ASt neurons have robust, sustained responses to shock-predicting cues. Further, photostimulation of the ASt is sufficient to drive freezing and avoidance behaviors, and optogenetic inhibition experiments show that Drd2+ ASt neurons are necessary for cue-conditioned fear responses. Our findings establish the ASt as a previously unappreciated yet critical structure for encoding learned associations and directing defensive behaviors.

neuroscience↗

bioRxiv2021

The geometry of face memory

The ability to recognize familiar visual objects is critical to survival. A central assumption of neuroscience is that long-term memories are represented by the same brain areas that encode sensory stimuli (1). Neurons in inferotemporal (IT) cortex represent the sensory percept of visual objects using a distributed axis code (2-4). Whether and how the same IT neural population represents the long-term memory of visual objects remains unclear. Here, we examined how familiar faces are encoded in face patch AM and perirhinal cortex. We found that familiar faces were represented in a distinct subspace from unfamiliar faces. The familiar face subspace was shifted relative to the unfamiliar face subspace at short latency and then distorted to increase neural distances between familiar faces at long latency. This distortion enabled markedly improved discrimination of familiar faces in both AM and PR. Inactivation of PR did not affect these memory traces in AM, suggesting that the memory traces arise from intrinsic recurrent processes within IT cortex or interactions with downstream regions outside the medial temporal lobe (5, 6). Overall, our results reveal that memories of familiar faces are represented in IT and perirhinal cortex by a distinct long-latency code that is optimized to distinguish familiar identities.

neuroscience↗

bioRxiv2019

Face familiarity detection with complex synapses

Synaptic plasticity is a complex phenomenon involving multiple biochemical processes that operate on different timescales. We recently showed that this complexity can greatly increase the memory capacity of neural networks when the variables that characterize the synaptic dynamics have limited precision, as in biological systems. These types of complex synapses have been tested mostly on simple memory retrieval problems involving random and uncorrelated patterns. Here we turn to a real-world problem, face familiarity detection, and we show that also in this case it is possible to take advantage of synaptic complexity to store in memory a large number of faces that can be recognized at a later time. In particular, we show that the familiarity memory capacity of a system with complex synapses grows almost linearly with the number of the synapses and quadratically with the number of neurons. Complex synapses are superior to simple ones, which are characterized by a single variable, even when the total number of dynamical variables is matched. We further show that complex and simple synapses have distinct signatures that are testable in proposed experiments. Our results indicate that a memory system with complex synapses can be used in real-world tasks such as face familiarity detection. SignificanceThe complexity of biological synapses is probably important for enabling us to remember the past for a long time and rapidly store new memories. The advantage of complex synapses in terms of memory capacity is significant when the variables that characterize the synaptic dynamics have limited precision. This advantage has been estimated under the simplifying assumption that the memories to be stored are random and uncorrelated. Here we show that synaptic complexity is important also in a more challenging and realistic face familiarity detection task. We built a simple neural circuit that can report whether a face has been previously seen or not. This circuit incorporates complex synapses that operate on multiple timescales. The memory performance of this circuit is significantly higher than in the case in which synapses are simple, indicating that the complexity of biological synapses can be important also in real-world memory tasks.

neuroscience↗

bioRxiv2019

Are place cells just memory cells? Memory compression leads to spatial tuning and history dependence

The observation of place cells has suggested that the hippocampus plays a special role in encoding spatial information. However, place cell responses are modulated by several non-spatial variables, and reported to be rather unstable. Here we propose a memory model of the hippocampus that provides a novel interpretation of place cells consistent with these observations. We hypothesize that the hippocampus is a memory device that takes advantage of the correlations between sensory experiences to generate compressed representations of the episodes that are stored in memory. A simple neural network model that can efficiently compress information naturally produces place cells that are similar to those observed in experiments. It predicts that the activity of these cells is variable and that the fluctuations of the place fields encode information about the recent history of sensory experiences. Place cells may simply be a consequence of a memory compression process implemented in the hippocampus. Significance StatementNumerous studies on humans revealed the importance of the hippocampus in memory formation. The rodent literature instead focused on the spatial representations that are observed in navigation experiments. Here we propose a simple model of the hippocampus that reconciles the main findings of the human and rodent studies. The model assumes that the hippocampus is a memory system that generates compressed representations of sensory experiences using previously acquired knowledge about the statistics of the world. These experiences can then be memorized more efficiently. The sensory experiences during the exploration of an environment, when compressed by the hippocampus, lead naturally to spatial representations similar to those observed in rodent studies and to the emergence of place cells.

neuroscience↗

bioRxiv2018

The geometry of abstraction in hippocampus and prefrontal cortex

The curse of dimensionality plagues models of reinforcement learning and decision-making. The process of abstraction solves this by constructing abstract variables describing features shared by different specific instances, reducing dimensionality and enabling generalization in novel situations. Here we characterized neural representations in monkeys performing a task where a hidden variable described the temporal statistics of stimulus-response-outcome mappings. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training. This type of generalization requires a particular geometric format of neural representations. Neural ensembles in dorsolateral pre-frontal cortex, anterior cingulate cortex and hippocampus, and in simulated neural networks, simultaneously represented multiple hidden and explicit variables in a format reflecting abstraction. Task events engaging cognitive operations modulated this format. These findings elucidate how the brain and artificial systems represent abstract variables, variables critical for generalization that in turn confers cognitive flexibility.

neuroscience↗