Growing dendrites enhance a neuron's computational power and memory capacity.

Neocortical pyramidal neurons have many dendrites, and such dendrites are capable of, in isolation of one-another, generating a neuronal spike. It is also now understood that there is a large amount of dendritic growth during the first years of a humans life, arguably a period of prodigious learning. These observations inspire the construction of a local, stochastic algorithm based on an earlier stochastic, homeostatic, Hebbian developmental theory.

Communication consumes 35 times more energy than computation in the human cortex, but both costs are needed to predict synapse number.

Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energy-efficient viewpoint, we define neural computation and make use of an energy-constrained computational function.

Constructing multilayered neural networks with sparse, data-driven connectivity using biologically-inspired, complementary, homeostatic mechanisms.

The immense complexity of the brain requires that it be built and controlled by intrinsic, self-regulating mechanisms. One such mechanism, the formation of new connections via synaptogenesis, plays a central role in neuronal connectivity and, ultimately, performance. Adaptive synaptogenesis networks combine synaptogenesis, associative synaptic modification, and synaptic shedding to construct sparse networks.

Linearization of excitatory synaptic integration at no extra cost.

In many theories of neural computation, linearly summed synaptic activation is a pervasive assumption for the computations performed by individual neurons. Indeed, for certain nominally optimal models, linear summation is required. However, the biophysical mechanisms needed to produce linear summation may add to the energy-cost of neural processing.

Limited synapse overproduction can speed development but sometimes with long-term energy and discrimination penalties.

Neural circuit development requires that synapses be formed between appropriate neurons. In addition, for a hierarchical network, successful development involves a sequencing of developmental events. It has been suggested that one mechanism that helps speed up development of proper connections is an early overproduction of synapses.

A consensus layer V pyramidal neuron can sustain interpulse-interval coding.

In terms of a single neuron's long-distance communication, interpulse intervals (IPIs) are an attractive alternative to rate and binary codes. As a proxy for an IPI, a neuron's time-to-spike can be found in the biophysical and experimental intracellular literature. Using the current, consensus layer V pyramidal neuron, the present study examines the feasibility of IPI-coding and examines the noise sources that limit the information rate of such an encoding.

Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination.

Intelligent organisms face a variety of tasks requiring the acquisition of expertise within a specific domain, including the ability to discriminate between a large number of similar patterns. From an energy-efficiency perspective, effective discrimination requires a prudent allocation of neural resources with more frequent patterns and their variants being represented with greater precision. In this work, we demonstrate a biologically plausible means of constructing a single-layer neural network that adaptively (i.

Neuronal dynamics during the learning of trace conditioning in a CA3 model of hippocampal function.

The present article develops quantitative behavioral and neurophysiological predictions for rabbits trained on an air-puff version of the trace-interval classical conditioning paradigm. Using a minimal hippocampal model, consisting of 8,000 primary cells sparsely and randomly interconnected as a model of hippocampal region CA-3, the simulations identify conditions which produce a clear split in the number of trials individual animals should need to learn a criterion response. A trace interval that is difficult to learn, but still learnable by half the experimental population, produces a bimodal population of learners: an early learner group and a late learner group.

A hippocampal model predicts a fluctuating phase transition when learning certain trace conditioning paradigms.

The hippocampus is needed for at least one kind of trace classical conditioning, the air-puff eye-blink paradigm. A simple model of region CA3 predicts three basic, quantitative observations of the learning behavior of rabbits. One particular quantified prediction is the learnable trace interval.

Theta-Modulated Input Reduces Intrinsic Gamma Oscillations in a Hippocampal Model.

Introducing theta-modulated input into a minimal model of the CA3 region of the hippocampus has significant effects on gamma oscillations. In the absence of theta-modulated input, the gamma oscillations are robust across a range of parameters. Introducing theta-modulated input weakens the gamma oscillations to a power more consistent with power spectra acquired from laboratory animals.

Metabolic energy cost of action potential velocity.

The action potential of the unmyelinated nerve is metabolically expensive. Using the energetic cost per unit length for the biophysically modeled action potential of the squid giant axon, we analyze this cost and identify one possible optimization. The energetic cost arising from an action potential is divided into three separate components: 1) the depolarization of the rising phase; 2) the hyperpolarization of the falling phase; and 3) the largest component, the overlapping of positive and negative currents, which has no electrical effect.

Interpreting hippocampal function as recoding and forecasting.

A model of hippocampal function, centered on region CA3, reproduces many of the cognitive and behavioral functions ascribed to the hippocampus. Where there is precise stimulus control and detailed quantitative data, this model reproduces the quantitative behavioral results. Underlying the model is a recoding conjecture of hippocampal computational function.

When logic fails: implicit transitive inference in humans.

Transitive inference (TI) in animals (e.g., choosing A over C on the basis of knowing that A is better than B and B is better than C) has been interpreted by some as reflecting a declarative logical inference process.

Configural representations in transverse patterning with a hippocampal model.

The hippocampus is necessary in both humans and rats for learning configural representations in tasks such as transverse patterning. The transverse patterning task, (A+B-, B+C-, C+A-), requires representing individual stimuli in the context of other stimuli. This paper extends a previous application to rat data [INNS World Congress on Neural Networks, 1995; Biol Cybern 6 (1998a) 203] by applying a model of the CA3 region of the hippocampus to human data.

Analysis of the optimal channel density of the squid giant axon using a reparameterized Hodgkin-Huxley model.

A reparameterized Hodgkin-Huxley-type model is developed that improves the 1952 model's fit to the biological action potential. In addition to altering Na(+) inactivation and K(+) activation kinetics, a voltage-dependent gating-current mechanism has been added to the model. The resulting improved model fits the experimental trace nearly exactly over the rising phase, and it has a propagation velocity that is within 3% of the experimentally measured value of 21.

Energy-efficient neuronal computation via quantal synaptic failures.

Organisms evolve as compromises, and many of these compromises can be expressed in terms of energy efficiency. For example, a compromise between rate of information processing and the energy consumed might explain certain neurophysiological and neuroanatomical observations (e.g.