Neural constraints on learning

By P. Sadtler, Kristin M. Quick, Matthew D. Golub, Steven M. Chase, Stephen I. Ryu, E. Tyler-Kabara, Byron M. Yu, Aaron P. Batista. 2014

666 citations recorded by the source corpus.

Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain–computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain–computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.

Subfields: Cognitive Neuroscience

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Citations in the corpus

Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, neutral): “An alternative theory states that neural representations are mixed selective but structured on a low-dimensional and task-specific manifold (Ganguli et al., 2008; Sadtler et al., 2014; Gao and Ganguli, 2015; Chaudhuri et al., 2019; Cueva et al., 2020) where correlated patterns of firing confer robus”
Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization (2018, background): “Although it is unknown whether a similar mechanism occurs in the brain, the idea that a downstream cortical network could control the parameter space allowed for learning in an upstream network (Sadtler et al., 2014) is an appealing idea.”
Cortical areas interact through a communication subspace (2019, positive): “These fluctuations involve multiple dimensions of activity shared among neurons (Yu et al., 2009; Harvey et al., 2012; Ecker et al., 2014; Sadtler et al., 2014; Kaufman et al., 2015; Lin et al., 2015; Rabinowitz et al., 2015; Mazzucato et al., 2016;Williamson et al., 2016), as identified using dimen”
Towards the neural population doctrine. (2019, methodology): “An example of this method is a novel Brain Computer Interface paradigm that perturbs the neural association of tasks ‘within’ a learned neural manifold and compares this to a perturbation ‘outside’ the manifold [76].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, methodology): “(684) devised a method that made adaptation to BMI control particularly difficult.”
Speech synthesis from neural decoding of spoken sentences (2019, methodology): “Another aspect that may underlie this difference is that articulatory kinematics lie on a low-dimensional manifold that constrain the potential high-dimensionality of acoustic signals (Extended Data Figure 5) 6,7,23 .”
Long-term stability of cortical population dynamics underlying consistent behavior (2020, neutral): “The set of neural modes defines a neural manifold 1,3,4 , a surface that captures much of the variance in the recorded neural activity (Fig.”
Navigating the Neural Space in Search of the Neural Code. (2017, neutral): “These behaviors are likely to rely on similarly low-dimensional patterns of neural activity (Brody et al., 2003; Churchland et al., 2012; Cunningham and Yu, 2014; Fitzgerald et al., 2013; Ganguli et al., 2008; Holdefer and Miller, 2002; Kaufman et al., 2014; Kobak et al., 2016; Li et al., 2016; Mant”