Akam, T. | Search bioRxiv

bioRxiv2018

pyPhotometry: Open source Python based hardware and software for fiber photometry data acquisition.

Fiber photometry is the process of recording bulk neural activity by measuring fluorescence changes in activity sensitive indicators (e.g. GCaMP) through an optical fiber. We present a system of open source hardware and software for fiber photometry data acquisition consisting of a compact, low cost, data acquisition board built around the Micropython microcontroller, and a cross platform graphical user interface (GUI) for controlling acquisition and visualising signals. The system can acquire two analog and two digital signals, and control two external LEDs via built in LED drivers. Time-division multiplexed illumination allows independent readout of fluorescence evoked by different excitation wavelengths from a single photoreceiver signal. Validation experiments indicate this approach offers better signal to noise for a given average excitation light intensity than sinusoidally-modulated illumination. pyPhotometry is substantially cheaper than commercial hardware filling the same role, and we anticipate, as an open source and comparatively simple tool, it will be easily adaptable and therefore of broad interest to a wide range of users.

neuroscience↗

bioRxiv2017

Single-Trial Inhibition of Anterior Cingulate Disrupts Model-based Reinforcement Learning in a Two-step Decision Task.

The anterior cingulate cortex (ACC) is implicated in learning the value of actions, but it remains poorly understood whether and how it contributes to model-based mechanisms that use action-state predictions and afford behavioural flexibility. To isolate these mechanisms, we developed a multi-step decision task for mice in which both action-state transition probabilities and reward probabilities changed over time. Calcium imaging revealed ramps of choice-selective neuronal activity, followed by an evolving representation of the state reached and trial outcome, with different neuronal populations representing reward in different states. ACC neurons represented the current action-state transition structure, whether state transitions were expected or surprising, and the predicted state given chosen action. Optogenetic inhibition of ACC blocked the influence of action-state transitions on subsequent choice, without affecting the influence of rewards. These data support a role for ACC in model-based reinforcement learning, specifically in using action-state transitions to guide subsequent choice. HighlightsO_LIA novel two-step task disambiguates model-based and model-free RL in mice. C_LIO_LIACC represents all trial events, reward representation is contextualised by state. C_LIO_LIACC represents action-state transition structure, predicted states, and surprise. C_LIO_LIInhibiting ACC impedes action-state transitions from influencing subsequent choice. C_LI

neuroscience↗