multiplexing

Off-policy learning in the basal ganglia

I will discuss work with Jack Lindsey modeling reinforcement learning for action selection in the basal ganglia. I will argue that the presence of multiple brain regions, in addition to the basal ganglia, that contribute to motor control motivates the need for an off-policy basal ganglia learning algorithm. I will then describe a biological implementation of such an algorithm that predicts tuning of dopamine neurons to a quantity we call "action surprise," in addition to reward prediction error. In the same model, an implementation of learning from a motor efference copy also predicts a novel solution to the problem of multiplexing feedforward and efference-related striatal activity. The solution exploits the difference between D1 and D2-expressing medium spiny neurons and leads to predictions about striatal dynamics.

Precise spatio-temporal spike patterns in cortex and model

The cell assembly hypothesis postulates that groups of coordinated neurons form the basis of information processing. Here, we test this hypothesis by analyzing massively parallel spiking activity recorded in monkey motor cortex during a reach-to-grasp experiment for the presence of significant ms-precise spatio-temporal spike patterns (STPs). For this purpose, the parallel spike trains were analyzed for STPs by the SPADE method (Stella et al, 2019, Biosystems), which detects, counts and evaluates spike patterns for their significance by the use of surrogates (Stella et al, 2022 eNeuro). As a result we find STPs in 19/20 data sets (each of 15min) from two monkeys, but only a small fraction of the recorded neurons are involved in STPs. To consider the different behavioral states during the task, we analyzed the data in a quasi time-resolved analysis by dividing the data into behaviorally relevant time epochs. The STPs that occur in the various epochs are specific to behavioral context - in terms of neurons involved and temporal lags between the spikes of the STP. Furthermore we find, that the STPs often share individual neurons across epochs. Since we interprete the occurrence of a particular STP as the signature of a particular active cell assembly, our interpretation is that the neurons multiplex their cell assembly membership. In a related study, we model these findings by networks with embedded synfire chains (Kleinjohann et al, 2022, bioRxiv 2022.08.02.502431).

Space and its computational challenges

How our senses work both separately and together involves rich computational problems. I will discuss the spatial and representational problems faced by the visual and auditory system, focusing on two issues. 1. How does the brain correct for discrepancies in the visual and auditory spatial reference frames? I will describe our recent discovery of a novel type of otoacoustic emission, the eye movement related eardrum oscillation, or EMREO (Gruters et al, PNAS 2018). 2. How does the brain encode more than one stimulus at a time? I will discuss evidence for neural time-division multiplexing, in which neural activity fluctuates across time to allow representations to encode more than one simultaneous stimulus (Caruso et al, Nat Comm 2018). These findings all emerged from experimentally testing computational models regarding spatial representations and their transformations within and across sensory pathways. Further, they speak to several general problems confronting modern neuroscience such as the hierarchical organization of brain pathways and limits on perceptual/cognitive processing.

Multiplexing and Demultiplexing with cerebral organoids for neurological diseases

Scalable microelectrode arrays: moving beyond time division multiplexing

Bernstein Conference 2024

Coordinated multiplexing of information about distinct objects in visual cortex

COSYNE 2022

Biologically plausible credit assignment via neuronal frequency multiplexing

COSYNE 2025

Multiplexing of spatial and temporal information in prefrontal cortex during complex behavior

The structure of neuronal information multiplexing in the frontal eye field accounts for both neuronal and behavioral variability