The Role of Neurocomputational Principles in Skill Savings
The Role of Neurocomputational Principles in Skill Savings Ashish Gupta & David C. Noelle ({ashish.gupta, david.noelle}@vanderbilt.edu) Department of Electrical Engineering and Computer Science Vanderbilt University, Box 1679 Station B Nashville, TN 37235, USA behaviors. From this perspective, savings arise when the interference produced by experience with new be- haviors is insufficient to completely erase the synaptic modifications introduced by the initial learning of the skill. This lack of “unlearning” could be due to neu- ral specialization, with some neurons being employed by the first skill but not by subsequent activities. To the degree that the sets of neurons associated with differ- ent skills are disjoint, learning one skill will not affect the synapses associated with another. When neurons are shared between skills, savings could be due to sub- threshold residual synaptic weights associated with the initial skill — weights that have been driven down by interfering experiences to below the threshold for neural firing, but not all the way down to their initial values. Finally, skills may share components or “sub-tasks”. To the degree that such components have isolated neural representations, learning a new skill may actually rein- force portions of a previously learned skill. Traditional artificial neural network models of skill ac- quisition fail to display savings when skills are learned se- quentially. Instead, these networks exhibit “catastrophic interference”, where the later learning of a second re- lated skill obliterates essentially all knowledge of an ini- tially acquired skill (McCloskey and Cohen, 1989). Re- searchers have proposed a number of specialized neu- ral network architectures and learning algorithms de- signed to reduce catastrophic interference (French, 1994; Brashers-Krug et al., 1995; McClelland et al., 1995). Most of these proposed mechanisms involve isolating the sets of neurons associated with different skills, either through some form of explicit architectural modulariza- tion, or through the use of learned sparse representa- tions, where only a few neurons in some internal layer of the network are highly active at any one time. We have explored the possibility that computational models of skill acquisition need not posit dedicated mechanisms for shielding from catastrophic interference. Instead, it is possible that biological constraints im- posed by the structure of cortical circuitry may em- body the necessary properties to promote skill savings. Specifically, we have examined the neurocomputational principles forming the Leabra cognitive modeling frame- work (O’Reilly and Munakata, 2000), and we have found that these biologically motivated principles give rise to savings without the need for auxiliary mechanisms. We trained a Leabra network to produce motion trajectories Abstract Humans exhibit savings in skills. A skill is rarely forgot- ten completely, even if it remains unused for long peri- ods. Also, the reacquisition of a skill to its previous level of competence is faster than initial skill learning. Tradi- tional artificial neural network models of skill learning have been unable to exhibit savings comparable to that seen in humans because they suffer from catastrophic in- terference. They are commonly trained to perform only one specific task, and when trained on a new task, they forget the original task completely. A number of special- ized connectionist architectures and learning rules have been suggested as means to avoid catastrophic interfer- ence. Instead of introducing such a new mechanism, we have investigated the degree to which the founda- tional neurocomputational principles embodied by the Leabra cognitive modeling framework are sufficient to ameliorate catastrophic interference. In particular, this framework includes both fast lateral inhibition and a local synaptic plasticity model that incorporates both Hebbian and error-based dynamics, grounded in known properties of cortical circuits. In this paper, we provide evidence that these fundamental computational proper- ties of neural circuits can support savings during sequen- tial learning of multiple motor skills. Introduction Learned motor skills are central to many activities, from bicycle riding to piano playing, from typing to playing ping-pong. To maintain and improve our motor skills, we need practice. If a ping-pong player does not play for a while, it is likely that his proficiency will wane, but the skill will not be forgotten completely. Further- more, reacquistion of proficiency will typically be rapid, as compared to the period of initial learning. This re- tention of skill knowledge, sometimes in a latent form, is called savings. Why do skills degrade when unpracticed? What is the neural basis of skill savings? A common explana- tion of proficiency loss involves interference. The ini- tial acquisition of a skill is driven by synaptic plasticity shaped by experience. Plasticity continues once practice on that skill ceases. Thus experience with other activ- ities continues to shape neural circuits, often in a way that interferes with the proper performance of the orig- inal skill. The interfering task experience may modify the response properties of neurons that are directly in- volved in the performance of the original skill, or the interference might take the form of a strengthening of the response of competing neurons that encode the new