Biology subjects

Machens, C. K.

Publications and source records attributed to Machens, C. K..

3 recordsLinked to original sources

bioRxiv2021

The geometry of the representation of decision variable and stimulus difficulty in the parietal cortex

Lateral intraparietal (LIP) neurons represent formation of perceptual decisions involving eye movements. In circuit models for these decisions, neural ensembles that encode actions compete to form decisions. Consequently, decision variables (DVs) are represented as partially potentiated action plans, where ensembles increase their average responses for stronger evidence supporting their preferred actions. As another consequence, DV representation and readout are implemented similarly for decisions with identical competing actions, irrespective of input and task context differences. Here, we challenge those core principles using a novel face-discrimination task, where LIP firing rates decrease with supporting evidence, contrary to conventional motion-discrimination tasks. These opposite response patterns arise from similar mechanisms in which decisions form along curved population-response manifolds misaligned with action representations. These manifolds rotate in state space based on task context, necessitating distinct readouts. We show similar manifolds in lateral and medial prefrontal cortices, suggesting a ubiquitous representational geometry across decision-making circuits.

neuroscience↗

bioRxiv2020

Robust coding with spiking networks: a geometric perspective

Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional representations, and we show that the neurons subthreshold voltages are confined to a convex region in a lower-dimensional voltage subspace, which we call a bounding box. Any changes in network parameters (such as number of neurons, dimensionality of inputs, firing thresholds, synaptic weights, or transmission delays) can all be understood as deformations of this bounding box. Using these insights, we show that functionality is preserved as long as perturbations do not destroy the integrity of the bounding box. We suggest that the principles underlying robustness in these networks--low-dimensional representations, heterogeneity of tuning, and precise negative feedback--may be key to understanding the robustness of neural systems at the circuit level.

neuroscience↗

bioRxiv2020

Dopamine responses reveal efficient coding of cognitive variables

Reward expectations based on internal knowledge of the external environment are a core component of adaptive behavior. However, internal knowledge may be inaccurate or incomplete due to errors in sensory measurements. Some features of the environment may also be encoded inaccurately to minimise representational costs associated with their processing. We investigate how reward expectations are affected by differences in internal representations by studying rodents behaviour and dopaminergic activity while they make time based decisions. Several possible representations allow a reinforcement learning agent to model animals choices during the task. However, only a small subset of highly compressed representations simultaneously reproduce, both, animals behaviour and dopaminergic activity. Strikingly, these representations predict an unusual distribution of response times that closely matches animals behaviour. These results can inform how constraints of representational efficiency may be expressed in encoding representations of dynamic cognitive variables used for reward based computations.

neuroscience↗