The Nature-Nurture controversy has been with us since it was first outlined by Plato and Aristotle. Nobody likes it anymore. All reasonable scholars today agree that genes and environment interact to determine complex cognitive outcomes. So why does the controversy persist? First, it persists because it has practical implications that cannot be postponed (i.e., what can we do to avoid bad outcomes and insure better ones?), a state of emergency that sometimes tempts scholars to stake out claims they cannot defend. Second, the controversy persists because we lack a precise, testable theory of the process by which genes and environment interact. In the absence of a better theory, innateness is often confused with (1) domain specificity (Outcome X is so peculiar that it must be innate), (2) species specificity (we are the only species who do X, so X must lie in the human genome), (3) localization (Outcome X is mediated by a particular part of the brain, so X must be innate), and (4) learnability (we cannot figure out how X could be learned, so X must be innate). We believe that an explicit and plausible theory of interaction is now around the corner, and that many of the classic maneuvers to defend or attack innateness will soon disappear. In the interim, some serious errors can be avoided if we keep these confounded issues apart. That is the major goal of this paper, i.e., not to attack innateness but to clarify what

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Population coding, where neurons with broad and overlapping firing rate tuning curves collectively encode information about a stimulus, is a common feature of sensory systems.We use decoding methods and measured properties of NMDA-mediated LTP induction to study the impact of long-term potentiation of synapses between the neurons of such a coding array. We find that, due to a temporal asymmetry in the induction of NMDA-mediated LTP, firing patterns in a neuronal array that initially represent the current value of a sensory input will, after training, provide an experienced-based prediction of that input instead. We compute how this prediction arises from and depends on the training experience. We also show how the encoded prediction can be used to generate learned motor sequences, such as the movement of a limb. This involves a novel form of memory recall that is driven by the motor response so that it automatically generates new information at a rate appropriate for the task being per...

The origin of orientation selectivity in visual cortical responses is a central problem for understanding cerebral cortical circuitry. In cats, many experiments suggest that orientation selectivity arises from the arrangement of lateral geniculate nucleus (LGN) afferents to layer 4 simple cells. However, this explanation is not sufficient to account for the contrast invariance of orientation tuning. To understand contrast invariance, we first characterize the input to cat simple cells generated by the oriented arrangement of LGN afferents. We demonstrate that it has two components: a spatial-phase-specific component (i.e., one that depends on receptive field spatial phase), which is tuned for orientation, and a phase-nonspecific component, which is untuned. Both components grow with contrast. Second, we show that a correlation-based intracortical circuit,

Some forms of synaptic plasticity depend on the temporal coincidence of presynaptic activity and postsynaptic response. This requirement is consistent with the Hebbian, or correlational, type of learning rule used in many neural network models. Recent evidence suggests that synaptic plasticity may depend in part on the production of a membrane permeant-diffusible signal so that spatial volume may also be involved in correlational learning rules. This latter form of synaptic change has been called volume learning. In both Hebbian and volume learning rules, interaction among synaptic inputs depends on the degree of coincidence of the inputs and is otherwise insensitive to their exact temporal order. Conditioning experiments and psychophysical studies have shown, however, that most animals are highly sensitive to the temporal order of the sensory inputs. Although these experiments assay the behavior of the entire animal or perceptual system, they raise the possibility that nervous systems may be sensitive to temporally ordered events at many spatial and temporal scales. We suggest here the existence of a new class of learning rule, called apredictiue Hebbian learning rule, that is sensitive to the temporal ordering of synaptic inputs. We show how this predictive learning rule could act at single synaptic connections and through diffuse neuromodulatory systems.

We describe a linear network that models correlations between real-valued visible variables using one or more real-valued hidden variables — a factor analysis model. This model can be seen as a linear version of the “Helmholtz machine”, and its parameters can be learned using the “wake-sleep ” method, in which learning of the primary “generative” model is assisted by a “recognition ” model, whose role is to fill in the values of hidden variables based on the values of visible variables. The generative and recognition models are jointly learned in “wake ” and “sleep ” phases, using just the delta rule. This learning procedure is comparable in simplicity to Oja’s version of Hebbian learning, which produces a somewhat different representation of correlations in terms of principal components. We argue that the simplicity of wake-sleep learning makes factor analysis a plausible alternative to Hebbian learning as a model of activity-dependent cortical plasticity. 1

The formation of ocular dominance and orientation columns in the mammalian visual cortex is briefly reviewed. Correlation-based models for their development are then discussed, beginning with the models of Von der Malsburg. For the case of semi-linear models, model behavior is well understood: correlations determine receptive field structure, intracortical interactions determine projective field structure, and the "knitting together" of the two determines the cortical map. This provides a basis for simple but powerful models of ocular dominance and orientation column formation: ocular dominance columns form through a correlationbased competition between left-eye and right-eye inputs, while orientation columns can form through a competition between ON-center and OFF-center inputs. These models account well for receptive field structure, but are not completely adequate to account for the details of cortical map structure. Alternative approaches to map structure, including the...

Abstract not found

Predictability minimization (PM Schmidhuber, 1992) exhibits various intuitive and theoretical advantages over many other methods for unsupervised redundancy reduction. So far, however, there were only toy applications of PM. In this paper, we apply semilinear PM to static real world images and find: without a teacher and without any significant pre-processing, the system automatically learns to generate distributed representations based on well-known feature detectors, such as orientation sensitive edge detectors and off-center-on-surround-like structures, thus extracting simple features related to those considered useful for image pre-processing and compression.

Neurons in the primary visual cortex (V1) respond preferentially to stimuli with distinct orientations and spatial frequencies. Although the organization of orientation selectivity has been thoroughly described, the arrangement of spatial frequency (SF) preference in V1 is controversial. Several layouts have been suggested, including laminar, columnar, clustered, pinwheel, and binary (high and low SF domains). We have reexamined the cortical organization of SF preference by imaging intrinsic cortical signals induced by stimuli of various orientations and SFs. SF preference maps, produced from optimally oriented stimuli, were verified using targeted microelectrode recordings. We found that a wide range of SFs is represented independently and mostly continuously within V1. Domains with SF preferences at the extremes of the SF continuum were separated by no more than 3 ⁄4 mm (conforming to the hypercolumn description of cortical The columnar arrangement of striate cortex is arguably its hallmark and, as a result, much of the work aimed at understanding the mechanisms of vision has focused on characterizing visual cortical columns (for review, see LeVay and Nelson, 1991). Although the maps of both ocular dominance (LeVay et al., 1978; Anderson et al., 1988; Bonhoeffer et al., 1995) and orientation preference (Hubel and Wiesel, 1962; Thompson et al., 1983; Bonhoeffer and Grinvald, 1991) of the cat have been well characterized in the primary visual cortex (V1), the cortical organization of spatial frequency (SF) preference is less clear. Past experiments have described the organization of SF preference in cats as laminar