Neural network models

…in classical theoretical and practical models of motion estimation, in which velocity flow fields invert when inputs are reversed in time. However, this symmetry of motion perception upon time reversal is broken in real visual systems. We designed a set of visual stimuli to investigate time reversal symmetry (TRS) breaking in the fruit fly Drosophila’s optomotor rotation behavior. We

Biological agents continually reconcile the internal states of their brain circuits with incoming sensory and environmental evidence to evaluate when and how to act. The brains of biological agents, including animals and humans, exploit many evolutionary innovations, chiefly modularity—observable at the level of anatomically-defined brain regions, cortical layers, and cell types among others—that can be repurposed in a compositional manner to endow the animal with a highly flexible behavioral repertoire. Accordingly, their behaviors show their own modularity, yet such behavioral modules seldom

…to enhance the predictive validity of neural network models based on suicidal ideation with the help of indirect psychometric indications, but without any direct questioning with regard to self-harm.We analyzed a dataset of 935 individuals aged between 18 and 40 years (mean age 23.5 years) obtained from the Koç University Psychotherapy Center. The initial model included demographic information, lifestyle factors, items of the DSM5 Level 1 Cross-Sectional Scale (except item

…emerge from the idiosyncratic neural circuitry of each individual. The overall organisation of neural circuits is preserved across individuals because of their common evolutionarily specified developmental programme. Such organisation at the circuit level may constrain neural activity, leading to low-dimensional l

Classical studies in reinforcement learning (RL) heavily rely on normative models of behavior, models that often stress interpretability over predictive capabilities. More recently, neural network models emerged as a descriptive modeling paradigm capable of high predictive power, however, with limited interpretation. To address these issues, we sought to augment the expressiveness of theoretically interpretable RL models with the high flexibility and predictive power of neural networks. Specifically, we introduce a novel framework whereby a neural network is trained to predict theoretically me

Inhibition plays an important role in shaping neural representations. Still, it is unclear whether inhibitory neurons merely refine the tuning of excitatory (EX) neurons or encode a parallel neural representation. We aimed to uncover the principles of inhibitory tuning in the cortex, using a simple system encoding a 1-dimensional sensory signal - the head-direction (HD) system. To this end, we recorded populations of neurons in the postsubiculum (PoSub), the primary cortical hub of the HD system. While excitatory HD cells had narrow receptive fields, fast-spiking (FS) interneurons had broad an

The world around us is complex, but at the same time full of meaningful regularities. We can detect, learn and exploit these regularities automatically in an unsupervised manner i.e. without any direct instruction or explicit reward. For example, we effortlessly estimate the average tallness of people in a room, or the boundaries between words in a language. These regularities and prior knowledge, once learned, can affect the way we acquire and interpret new information to build and update our internal model of the world for future decision-making processes. Despite the ubiquity of passively l

…Neural network models provide a clear account of how these binocular neurons encode

Dr. Rajan and her lab design neural network models based on experimental data, and reverse-engineer them to figure out how brain circuits function in health and disease. They recently developed a powerful framework for tracing neural paths across multiple brain regions— called Current-Based Decomposition (CURBD). This new approach enables the computation of excitatory and inhibitory input currents that drive a given neuron, aiding in the discovery of how entire populations of neurons behave across multiple interacting brain regions. Dr. Rajan’s team has applied this method to studying the neur

Rajanlab designs neural network models constrained by experimental data, and reverse engineers them to figure out how brain circuits function in health and disease. Recently, we have been developing a powerful new theory-based framework for “in-vivo tract tracing” from multi-regional neural activity collected experimentally. We call this framework CURrent-Based Decomposition (CURBD). CURBD employs recurrent neural networks (RNNs) directly constrained, from the outset, by time series measurements acquired experimentally, such as Ca2+ imaging or electrophysiological data. Once trained, these da