The Challenge: Decode a non-trivial memory from a static map of synaptic connectivity
Welcome fellow neuroscientists to the Aspirational Neuroscience community. We are a donor-funded nonprofit (est. 2018) focused on a subset of challenging questions in neuroscience. Our community of neuroscience students and practitioners attempts to balance aspirational thinking with respect to the long-term implications of a successful neuroscience with practical realism about our current state of ignorance and knowledge.
Research over the last 100 years has already made tremendous progress by identifying the key structural and molecular building blocks of neuronal computation. Recent years have seen an explosion of new neuroscience techniques, such as: 1. Large-scale recording and manipulation of neuronal ensemble dynamics, 2. Tagging and optogenetic manipulation of cellular and synaptic memory engrams, 3. Advanced techniques for probing the structural and molecular changes underlying learning and memory, and 4. Automated electron microscopic mapping of dense synaptic connectivity (synapse-resolution connectomics). In parallel, computational models of brain function have advanced considerably in their depth and precision, pushed by, and helping push forward, advances in AI and artificial deep neural networks.
We believe that these parallel advancements in technique and theory have set the stage for the achievement of a major milestone in neuroscience: the first demonstration of the decoding of a non-trivial memory (or other learned function) based on only a static map of synaptic connectivity. Today’s neuroscience theories of brain function and memory strongly imply that such decoding should be possible, and the recent development large-scale synapse-resolution connectome mapping has finally provided the tools needed to put these theories to the test.
With this milestone in mind we hereby announce the following Awards and Prizes:
Four Annual Research Awards: To honor and highlight research into how learning and memory are physically encoded in the brain.
Memory Decoding Challenge Prize: To the first team to decode a non-trivial memory from a static map of synaptic connectivity.
We also have established:
Aspirational Neuroscience Discussion Group: A private and invitation-only email discussion group for neuroscientists to discuss relevant research, nominate papers, and to decide what precisely should qualify as a successful decoding of a non-trivial memory. (Email kenneth.hayworth@gmail.com to request to join)
Yearly Conference: Bringing together researchers across the following neuroscience disciplines:
1. Neuronal ensemble dynamics (recording, decoding, and manipulation of neuronal ensembles)
2. Memory engrams (tagging and manipulation of cellular and synaptic memory engrams)
3. Structural and molecular changes underlying learning and memory
4. Synapse-resolution connectomics
5. Theoretical neuroscience and computational modeling
Follow us on Twitter at: @AspirationNeur
Tagging and manipulation of cellular and synaptic memory engrams
Automated, high-throughput, synapse-resolution connectomics
Structural and molecular changes underlying learning and memory
Recording, decoding, and manipulation of neuronal ensemble dynamics
Theoretical models of biological neuronal function, including still-poorly understood phenomena (oscillations, field couplings)
Biophysically-realistic neural simulations and comparisons to artificial neural network research
The current neuroscience consensus is that the vast majority of long-term learning and memory is encoded in the patterns and strengths of synaptic connections among neurons. But there remains some doubt because most experimental evidence is indirect. This challenge, to decode a memory from connectivity alone, represents a powerful test of the quality of our neuroscience theories of learning and memory.
It is anticipated that it will take many years for any research lab to claim the Memory Decoding Prize. Because of this, we are offering a set of four smaller Research Awards each year to highlight and honor important research into the physical and functional basis of learning and memory.
