Digitized neural networks: long-term stability from forgetful neurons

Integration of direction cues is invariant to the temporal gap between them

Many decisions involve integration of evidence conferred by discrete cues over time. However, the neural mechanism of this integration is poorly understood. Several decision-making models suggest that integration of evidence is implemented by a dynamic system whose state evolves toward a stable point representing the decision outcome. The internal dynamics of such point attractor models render them sensitive to the temporal gaps between cues because their internal forces push the state forward once it is dislodged from the initial stable point. We asked whether human subjects are as sensitive to such temporal gaps. Subjects reported the net direction of stochastic random dot motion, which was presented in one or two brief observation windows (pulses). Pulse strength and interpulse interval varied randomly from trial to trial. We found that subjects' performance was largely invariant to the interpulse intervals up to at least 1 s. The findings question the implementation of the integration process via mechanisms that rely on autonomous changes of network state. The mechanism should be capable of freezing the state of the network at a variety of firing rate levels during temporal gaps between the cues, compatible with a line of stable attractor states.

Neural integrators for decision making: a favorable tradeoff between robustness and sensitivity

A key step in many perceptual decision tasks is the integration of sensory inputs over time, but a fundamental questions remain about how this is accomplished in neural circuits. One possibility is to balance decay modes of membranes and synapses with recurrent excitation. To allow integration over long timescales, however, this balance must be exceedingly precise. The need for fine tuning can be overcome via a “robust integrator” mechanism in which momentary inputs must be above a preset limit to be registered by the circuit. The degree of this limiting embodies a tradeoff between sensitivity to the input stream and robustness against parameter mistuning. Here, we analyze the consequences of this tradeoff for decision-making performance. For concreteness, we focus on the well-studied random dot motion discrimination task and constrain stimulus parameters by experimental data. We show that mistuning feedback in an integrator circuit decreases decision performance but that the robust integrator mechanism can limit this loss. Intriguingly, even for perfectly tuned circuits with no immediate need for a robustness mechanism, including one often does not impose a substantial penalty for decision-making performance. The implication is that robust integrators may be well suited to subserve the basic function of evidence integration in many cognitive tasks. We develop these ideas using simulations of coupled neural units and the mathematics of sequential analysis.

Computational models of decision making: integration, stability, and noise

Decision making demands the accumulation of sensory evidence over time. Questions remain about how this occurs, but recent years have seen progress on several fronts. The first concerns when optimal accumulation of evidence coincides with the simplest method of accumulating neural activity: summation over time. The second involves what computations the brain might perform when summation is difficult due to imprecision in neural circuits or is suboptimal due to uncertainty or variability in how evidence arrives. Finally, the third concerns sources of noise in decision circuits. Empirical studies have better constrained the extent of this noise, and modeling work is helping to clarify its possible origins.

Context-dependent retrieval of information by neural-network dynamics with continuous attractors

Memory retrieval in neural networks has traditionally been described by dynamic systems with discrete attractors. However, recent neurophysiological findings of graded persistent activity suggest that memory retrieval in the brain is more likely to be described by dynamic systems with continuous attractors. To explore what sort of information processing is achieved by continuous-attractor dynamics, keyword extraction from documents by a network of bistable neurons, which gives robust continuous attractors, is examined. Given an associative network of terms, a continuous attractor led by propagation of neuronal activation in this network appears to represent keywords that express underlying meaning of a document encoded in the initial state of the network-activation pattern. A dominant hypothesis in cognitive psychology is that long-term memory is archived in the network structure, which resembles associative networks of terms. Our results suggest that keyword extraction by the neural-network dynamics with continuous attractors might symbolically represent context-dependent retrieval of short-term memory from long-term memory in the brain.

Broca's arrow: evolution, prediction, and language in the brain

Brodmann's areas 44 and 45 in the human brain, also known as Broca's area, have long been associated with language functions, especially in the left hemisphere. However, the precise role Broca's area plays in human language has not been established with certainty. Broca's area has homologs in the great apes and in area F5 in monkeys, which suggests that its original function was not linguistic at all. In fact, great ape and hominid brains show very similar left-over-right asymmetries in Broca's area homologs as well as in other areas, such as homologs to Wernicke's area, that are normally associated with language in modern humans. Moreover, the so-called mirror neurons are located in Broca's area in great apes and area F5 in monkeys, which seem to provide a representation of cause and effect in a primate's environment, particularly its social environment. Humans appear to have these mirror neurons in Broca's area as well. Similarly, genetic evidence related to the FOXP2 gene implicates Broca's area in linguistic function and dysfunction, but the gene itself is a highly conserved developmental gene in vertebrates and is shared with only two or three differences between humans and great apes, five between humans and mice, and eight between humans and songbirds. Taking neurons and portions of the brain as discrete computational segments in the sense of constituting specific Turing machines, this evidence points to a predictive motor and conceptual function for Broca's area in primates, especially for social concepts. In human language, this is consistent with evidence from typological and cognitive linguistics.

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