Dorsolateral prefrontal cortex (DLPFC) is associated with executive control and response selection, but the extent to which it is involved in effector-specific transformations is unclear. We addressed this question by recording single neurons from dorsolateral prefrontal cortex (DLPFC) while two trained monkeys performed a head-unrestrained reaching paradigm that allowed freely coordinated motion of gaze, head reaching in depth. Animals touched one of three central LEDs at waist level while maintaining gaze on a central fixation dot and were rewarded if they touched a target appearing at one of 15 locations in a 40° x 20° (visual angle) array. Preliminary analysis of 271 neurons in both monkeys showed an assortment of target/stimulus, gaze, pre-reach and reach-timed responses in DLPFC. We first tested for gaze, head, and hand gain fields during the different neuronal responses and found that 38% of the responses were gain modulated by initial hand position. A small fraction of neurons showed gain fields for initial eye position (4%), and for both initial eye and hand position (6%). After removing the gain field effects, we fitted the residual data against various spatial models and found that the visual response best encoded the target relative to space (Ts), whereas responses at gaze and hand onset showed a tendency towards coding hand displacement (dA). In addition, some (20%) neurons showed a preference for coding final head position or displacement. A more complete analysis will describe the complete coding and distribution of gaze, head, and reach signals in this region.

Effector-specific spatial codes in dorsolateral prefrontal cortex during a head-unrestrained reach task

Allocentric (landmark-centered) and egocentric (eye-centered) visual information are optimally integrated for goal-directed movements. This process has been observed within the supplementary and frontal eye fields, but the underlying processes for this combination remain a puzzle, mainly due to inadequacy of current theoretical models to explain data at different levels (i.e., behavior, single neuron, and distributed network). Here, we propose a physiologically inspired neural network with two major components: First, a Convolutional Neural Network (CNN) is used to extract the allocentric information (target and landmark): We used repeated (2 layers) convolution, rectifications, and normalization followed by a feature pooling layer to extract allocentric information (target and landmark). Second, a Multi-Layer Perceptron network (MLP, 3 fully connected layers) is used to incrementally transform allocentric information into an integrated motor response. We added an additional layer to transform motor responses into final gaze positions. The network was trained on both idealized and actual monkey gaze behavior. MLP output units accurately simulated prefrontal motor responses (including open-ended response fields that shifted partially with the landmark) and their decoded output achieved good correspondence (MLP: R2 = 0.80) with actual gaze behavior (Bharmauria et al. Cerebral Cortex 2020). These results suggest that our framework works and provides a suitable tool to study the underlying mechanisms of allocentric and egocentric integration. Supported by a VISTA Program fellowship.

Mechanisms for integrating allocentric and egocentric visual information for goal-directed movements: a neural network approach

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PS-2-D -  Sensory and Motor Systems

poster

Colleagues and Friends:

I am very happy to welcome you to the 14th annual Canadian Neuroscience Meeting, and to the first virtual edition of our meeting. I want to highlight the important work the CAN meeting chair, **Roger Thompson** and co-chair, **Alyson Fournier**, did to put together the great program we are offering this year. Moving to a virtual format required a complete re-design of the program, and both Roger and Alyson did a splendid job in providing us with an appealing and well adapted program for our first virtual event. I wish to thank them for this very important effort.

Highlights of this year’s scientific meeting include invited speakers **Michael Hausser, Franck Polleux, Gwyneth Card, Magdalena Götz**and Brain Prize winner **Huda Zoghbi**. Plenary and parallel symposia, proposed by our members complete our diverse [scientific program.](https://can-acn.org/meeting-2021/program-2021/)

The 2021 Advocacy session, chaired and organized by CAN advocacy committee chair **Karun Singh**, will feature a special lecture and question period with Member of Parliament and past Science Minister **Kirsty Duncan**. This will be followed by a short overview of CAN’s advocacy activities over the last year. We invite you to participate in this session for insight from Dr. Duncan on the workings of Parliament Hill, and to learn more about what CAN has been doing to promote neuroscience research in Canada.

We are also excited to host **Lisa Willis** in the Equity Diversity and Inclusion session, organized by EDI committee chair **Jibran Khokhar**.

A special highlight of our meeting will be the lectures by award-winning young neuroscientists. We are please to host the CAN 2020 Young Investigator Award winner **Mihaela Iordanova**, and CAN 2021 Young Investigator Award winners **Aaron Phillips** and **Tabrez Siddiqui**. Highlights will also include presentation by the top three winners of the CAN-CIHR-INMHA Brain Star Award winners, **Claire Gizowski, Nuria Daviu Abant** and **Lukas Tamayo-Orrego**.

Independently organized satellite symposia complement and complete this exciting program, check them out here: https://can-acn.org/meeting-2021/2021-satellite-symposia/

Some of the most engaging sessions are always the poster sessions featuring the work of our trainees, so make sure to log in to Gather Town during the poster sessions to chat with the presenters.
We hope you enjoy this program, and look forward to welcoming you, virtually.

Shernaz Bamji

President of the Canadian Association for Neuroscience

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Parisa Abedi Khoozani

2

SHORT BIO

Presentations

Effector-specific spatial codes in dorsolateral prefrontal cortex during a head-unrestrained reach task

Mechanisms for integrating allocentric and egocentric visual information for goal-directed movements: a neural network approach

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