Unsupervised learning by competing hidden units.

It is widely believed that end-to-end training with the backpropagation algorithm is essential for learning good feature detectors in early layers of artificial neural networks, so that these detectors are useful for the task performed by the higher layers of that neural network. At the same time, the traditional form of backpropagation is biologically implausible. In the present paper we propose an unusual learning rule, which has a degree of biological plausibility and which is motivated by Hebb's idea that change of the synapse strength should be local-i.e., should depend only on the activities of the pre- and postsynaptic neurons. We design a learning algorithm that utilizes global inhibition in the hidden layer and is capable of learning early feature detectors in a completely unsupervised way. These learned lower-layer feature detectors can be used to train higher-layer weights in a usual supervised way so that the performance of the full network is comparable to the performance of standard feedforward networks trained end-to-end with a backpropagation algorithm on simple tasks.

Understanding Emergent Dynamics: Using a Collective Activity Coordinate of a Neural Network to Recognize Time-Varying Patterns.

In higher animals, complex and robust behaviors are produced by the microscopic details of large structured ensembles of neurons. I describe how the emergent computational dynamics of a biologically based neural network generates a robust natural solution to the problem of categorizing time-varying stimulus patterns such as spoken words or animal stereotypical behaviors. The recognition of these patterns is made difficult by their substantial variation in cadence and duration. The neural circuit behaviors used are similar to those associated with brain neural integrators. In the larger context described here, this kind of circuit becomes a building block of an entirely different computational algorithm for solving complex problems. While the network behavior is simulated in detail, a collective view is essential to understanding the results. A closed equation of motion for the collective variable describes an algorithm that quantitatively accounts for many aspects of the emergent network computation. The feedback connections and ongoing activity in the network shape the collective dynamics onto a reduced dimensionality manifold of activity space, which defines the algorithm and computation actually performed. The external inputs are weak and are not the dominant drivers of network activity.

Two cultures? Experiences at the physics-biology interface.

'I didn't really think of this as moving into biology, but rather as exploring another venue in which to do physics.' John Hopfield provides a personal perspective on working on the border between physical and biological sciences.

Neurodynamics of mental exploration.

Thinking allows an animal to take an effective action in a novel situation based on a mental exploration of possibilities and previous knowledge. We describe a model animal, with a neural system based loosely on the rodent hippocampus, which performs mental exploration to find a useful route in a spatial world it has previously learned. It then mentally recapitulates the chosen route, and this intent is converted to motor acts that move the animal physically along the route. The modeling is based on spiking neurons with spike-frequency adaptation. Adaptation causes the continuing evolution in the pattern of neural activity that is essential to mental exploration. A successful mental exploration is remembered through spike-timing-dependent synaptic plasticity. The system is also an episodic memory for an animal chiefly concerned with locations.

Rate-specific synchrony: using noisy oscillations to detect equally active neurons.

Although gamma frequency oscillations are common in the brain, their functional contributions to neural computation are not understood. Here we report in vitro electrophysiological recordings to evaluate how noisy gamma frequency oscillatory input interacts with the overall activation level of a neuron to determine the precise timing of its action potentials. The experiments were designed to evaluate spike synchrony in a neural circuit architecture in which a population of neurons receives a common noisy gamma oscillatory synaptic drive while the firing rate of each individual neuron is determined by a slowly varying independent input. We demonstrate that similarity of firing rate is a major determinant of synchrony under common noisy oscillatory input: Near coincidence of spikes at similar rates gives way to substantial desynchronization at larger firing rate differences. Analysis of this rate-specific synchrony phenomenon reveals distinct spike timing "fingerprints" at different firing rates that emerge through a combination of phase shifting and abrupt changes in spike patterns. We further demonstrate that rate-specific synchrony permits robust detection of rate similarity in a population of neurons through synchronous activation of a postsynaptic neuron, supporting the biological plausibility of a Many Are Equal computation. Our results reveal that spatially coherent noisy oscillations, which are common throughout the brain, can generate previously unknown relationships among neural rate codes, noisy interspike intervals, and precise spike synchrony codes. All of these can coexist in a self-consistent manner because of rate-specific synchrony.

Vibrissa resonance as a transduction mechanism for tactile encoding.

We present evidence that resonance properties of rat vibrissae differentially amplify high-frequency and complex tactile signals. Consistent with a model of vibrissa mechanics, optical measurements of vibrissae revealed that their first mechanical resonance frequencies systematically varied from low (60-100 Hz) in longer, posterior vibrissae to high ( approximately 750 Hz) in shorter, anterior vibrissae. Resonance amplification of tactile input was observed in vivo and ex vivo, and in a variety of boundary conditions that are likely to occur during perception, including stimulation of the vibrissa with moving complex natural stimuli such as sandpaper. Vibrissae were underdamped, allowing for sharp tuning to resonance frequencies. Vibrissa resonance constitutes a potentially useful mechanism for perception of high-frequency and complex tactile signals. Amplification of small amplitude signals by resonance could facilitate detection of stimuli that would otherwise fail to drive neural activity. The systematic map of frequency sensitivity across the face could facilitate texture discrimination through somatotopic encoding of frequency content. These findings suggest strong parallels between vibrissa tactile processing and auditory encoding, in which the cochlea also uses resonance to amplify low-amplitude signals and to generate a spatial map of frequency sensitivity.

Separating objects and 'neural' computation.

This article demonstrates how that the effectiveness of nervous system in doing the computations essential to an organism can be based on using algorithms that are readily implemented by nervous system 'device biophysics'. Collective effects and collective algorithms that exploit their dynamics provide powerful potential for useful neuronal computations.

Rapid, parallel path planning by propagating wavefronts of spiking neural activity.

Efficient path planning and navigation is critical for animals, robotics, logistics and transportation. We study a model in which spatial navigation problems can rapidly be solved in the brain by parallel mental exploration of alternative routes using propagating waves of neural activity. A wave of spiking activity propagates through a hippocampus-like network, altering the synaptic connectivity. The resulting vector field of synaptic change then guides a simulated animal to the appropriate selected target locations. We demonstrate that the navigation problem can be solved using realistic, local synaptic plasticity rules during a single passage of a wavefront. Our model can find optimal solutions for competing possible targets or learn and navigate in multiple environments. The model provides a hypothesis on the possible computational mechanisms for optimal path planning in the brain, at the same time it is useful for neuromorphic implementations, where the parallelism of information processing proposed here can fully be harnessed in hardware.