Brain Inspired

Show notes:

• Check out Anne's lab website.
• Follow her on twitter: @anne_churchland
• Anne's List, the list of female systems neuroscientists to invite as speakers.
• The papers we discuss:
  • Single-trial neural dynamics are dominated by richly varied movements.
  • Lapses in perceptual judgments reflect exploration.
  • Complexity vs Stimulus-Response Compatibility vs Stimulus-response ethological validity.
  • Perceptual Decision-Making: A Field in the Midst of a Transformation.

Show notes:

• Máté's Cambridge website.
• He's part of the Computational Learning and Memory Group there.
• Here's his webpage at Central European University.
• A review to introduce his subsequent work:
  • Statistically optimal perception and learning: from behavior to neural representations.
• Related recent talks:
  • Bayesian models of perception, cognition and learning - CCCN 2017.
  • Sampling: coding, dynamics, and computation in the cortex (Cosyne 2018).

Show notes:

• Nathaniel will deliver a keynote address at the upcoming CCN conference.
• Check out his lab website.
• Follow him on Twitter: @nathanieldaw.
• The paper we discuss:
  • Prioritized memory access explains planning and hippocampal replay
  • Or see a related talk: Rational planning using prioritized experience replay.

Show notes:

• Roshan will deliver a keynote address at the upcoming CCN conference.
• Roshan's Motivational and Cognitive Control lab.
• Follow her on Twitter: @CoolsControl.
• Her TED Talk on Trusting Science.
• Papers related to the research we discuss:
  • The costs and benefits of brain dopamine for cognitive control.
  • Or see her variety of related works.

Show notes:

• This is the first in a series of episodes where I interview keynote speakers at the upcoming Cognitive Computational Neuroscience conference in September in Berlin.
  • Thomas Naseralis summarizes the origins and vision of the CCN.
• Tim’s Neuroscience homepage:
• The papers we discuss:
  • Generalisation of structural knowledge in the hippocampal-entorhinal system (referred to in the podcast at "The Tolman Eichenbaum Machine”)
• Human replay spontaneously reorganizes experience. (In press at Cell - below is an abstract for it from COSYNE 2018)
  • Inference in replay through factorized representations.

Show notes:

• Tony’s lab site, where there are links to his auditory decision making work and connectome work we discuss.
• Here are a few talks online about that:
  • Corticostriatal circuits underlying auditory decisions.

Show Notes:

• Federico's website.
• Federico’s papers we discuss:
  • Conflicting emergences. Weak vs. strong emergence for the modelling of brain function
  • From homeostasis to behavior: balanced activity in an exploration of embodied dynamic environmental-neural interaction
• Free Energy Principle.
• Integrated Information Theory.
• The Tononi paper about Integrated Information Theory and its relation to emergence:
  • Quantifying causal emergence shows that macro can beat micro
• Default mode as large scale oscillation:
  • The brain's code and its canonical computational motifs. From sensory cortex to the default mode network: A multi-scale model of brain function in health and disease.

Show notes:

• Visit Rafal’s Lab Website.
• Rafal's papers we discuss:
  • Theories of Error Back-Propagation in the Brain.
  • An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.
  • A tutorial on the free-energy framework for modelling perception and learning.
• Check out Episode 9 with Blake Richards about how apical dendrites could do back-prop.
• The Randall O’Reilly early paper describing biologically plausible back propagation:
  • O'Reilly, R.C. (1996). Biologically Plausible Error-driven Learning using Local Activation Differences: The Generalized Recirculation Algorithm. Neural Computation, 8, 895-938.

• Cortical.io
• The white paper we discuss: Semantic Folding Theory And its Application in Semantic Fingerprinting.
• A nice talk Francisco gave: Semantic fingerprinting: Democratising natural language processing
• Francisco was influenced by Jeff Hawkins’ work and book On Intelligence.
• See episode 017 to learn more about Jeff Hawkins’ approach to modeling cortex.
• Douglas Hofstadter’s Analogy as the Core of Cognition.

• Jay's homepage at Stanford.
• Implementing mathematical reasoning in machines:
  • The video lecture.
  • The paper.
• Parallel Distributed Processing by Rumelhart and McClelland.
• Complimentary Learning Systems Theory and Its Recent Update.
• Episode 28 with Sam Gershman about building machines that learn and think like humans.
• Check out my interview on Ginger Campbell's Brain Science podcast.