Biology subjects

Onken, A.

Publications and source records attributed to Onken, A..

4 recordsLinked to original sources

bioRxiv2026

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

For datasets with thousands of neurons and images, vision transformers have proven successful at predicting neural responses to stimuli. However, they are expected to underperform in low-data regimes, where CNNs and Gaussian processes are considered more effective. We ask whether transformers can be made competitive for small-scale neural prediction, and show that underperformance in this regime can be overturned with the right inductive bias. We equip a vision transformer with a differentiable per-neuron circular crop in feature space. The crop is centered on each neuron's receptive field, with a radius selected per neuron, so the model only sees the small image region that drives that neuron instead of the whole image. This makes the cost of attention scale with the size of the receptive field rather than with the size of the image. A single transformer stack is shared across all neurons: each neuron's specificity resides in the crop, not in the architecture. We call this model circular receptive-field vision transformer (CiRF-ViT). We evaluate it on small multi-electrode-array recordings of mouse and salamander retinas: a mouse preparation of 41 ganglion cells and two salamander preparations totaling 49 ganglion cells, each with only a few thousand stimulus-response pairs, two orders of magnitude below the scale at which transformers are typically trained. Against CNN and Gaussian-process baselines, CiRF-ViT reaches the highest mean explained variance on both datasets (0.94 on mouse, 0.95 on salamander). Probed with the local spike-triggered average (LSTA), a zero-shot test of context-dependent, nonlinear stimulus sensitivity, CiRF-ViT reproduces the qualitative polarity inversion that a linear model cannot capture by construction. A biologically grounded, per-neuron locality prior is therefore enough to make transformers competitive for neural prediction well below their usual data scale, while matching the reference models on an established functional signature of the retina's nonlinear computation.

neuroscience↗

bioRxiv2025

Movie-trained transformer reveals novel response properties to dynamic stimuli in mouse visual cortex

Understanding how the brain encodes complex, dynamic visual stimuli remains a fundamental challenge in neuroscience. Here, we introduce ViV1T, a transformer-based model trained on natural movies to predict neuronal responses in mouse primary visual cortex (V1). ViV1T outperformed state-of-the-art models in predicting responses to both natural and artificial dynamic stimuli, while requiring fewer parameters and reducing runtime. Despite being trained exclusively on natural movies, ViV1T accurately captured core V1 properties, including orientation and direction selectivity as well as contextual modulation, despite lacking explicit feedback mechanisms. ViV1T also revealed novel functional features. The model predicted a wider range of contextual responses when using natural and model-generated surround stimuli compared to traditional gratings, with novel model-generated dynamic stimuli eliciting maximal V1 responses. ViV1T also predicted that dynamic surrounds elicited stronger contextual modulation than static surrounds. Finally, the model identified a subpopulation of neurons that exhibit contrast-dependent surround modulation, switching their response to surround stimuli from inhibition to excitation when contrast decreases. These predictions were validated through semi-closed-loop in vivo recordings. Overall, ViV1T establishes a powerful, data-driven framework for understanding how brain sensory areas process dynamic visual information across space and time. Code available at github.com/bryanlimy/ViV1T-closed-loop.

neuroscience↗

bioRxiv2019

State-dependent pontine ensemble dynamics and interactions with cortex across sleep states

The pontine nuclei play a crucial role in sleep-wake regulation. However, pontine ensemble dynamics underlying sleep regulation remain poorly understood. By monitoring population activity in multiple pontine and adjacent brainstem areas, here we show slow, state-predictive pontine ensemble dynamics and state-dependent interactions between the pons and the cortex in mice. On a timescale of seconds to minutes, pontine populations exhibit diverse firing across vigilance states, with some of these dynamics being attributed to cell type-specific activity. Pontine population activity can predict pupil dilation and vigilance states: pontine neurons exhibit longer predictable power compared with hippocampal neurons. On a timescale of sub-seconds, pontine waves (P-waves) are observed as synchronous firing of pontine neurons primarily during rapid eye movement (REM) sleep, but also during non-REM (NREM) sleep. Crucially, P-waves functionally interact with cortical activity in a state-dependent manner: during NREM sleep, hippocampal sharp wave-ripples (SWRs) precede P-waves. On the other hand, P-waves during REM sleep are phase-locked with ongoing hippocampal theta oscillations and are followed by burst firing in a subset of hippocampal neurons. Thus, the directionality of functional interactions between the hippocampus and pons changes depending on sleep states. This state-dependent global coordination between pontine and cortical regions implicates distinct functional roles of sleep.

neuroscience↗

bioRxiv2018

Categorical encoding of decision variables in orbitofrontal cortex

A fundamental and recurrent question in systems neuroscience is that of assessing what variables are encoded by a given population of neurons. Such assessments are often challenging because neurons in one brain area may encode multiple variables, and because neuronal representations might be categorical (different neurons encoding different variables) or mixed (individual neurons encoding a combination of variables). These issues are particularly pertinent to the representation of decision variables in the orbitofrontal cortex (OFC) - an area implicated in economic choices. Here we present a new algorithm to assess whether a neuronal representation is categorical or mixed, and to identify the encoded variables if the representation is indeed categorical. The algorithm is based on two clustering procedures, one variable-independent and the other variable-based. The two partitions are then compared through adjusted mutual information. The present algorithm overcomes limitations of previous approaches and is widely applicable. We tested the algorithm on synthetic data and then used it to examine neuronal data recorded in the primate OFC during economic decisions. Confirming previous assessments, we found the neuronal representation in OFC to be categorical in nature. We also found that neurons in this area encode the value of individual offers, the binary choice outcome and the chosen value. In other words, during economic choice, neurons in the primate OFC encode decision variables in a categorical way.\n\nAuthor SummaryMental functions such as sensory perception or decision making ultimately rely on the activity of neuronal populations in different brain regions. Much research in neuroscience is devoted to understanding how different groups of neurons support specific brain functions by representing behaviorally relevant variables. In this respect, one important question is whether individual neurons represent single variables, or linear combination of variables. Here we developed a new algorithm to assess this general issue. We then used the algorithm to examine neurons in the orbitofrontal cortex (OFC) recorded while non-human primates performed economic decisions. We found that different neurons represent different variables (categorical encoding). Specifically, neurons in the OFC encoded the value of individual offers, the binary choice outcome, and the chosen value. The present results support the hypothesis that economic decisions are formed within the OFC.

neuroscience↗