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Dezhe Z Jin

Publications and source records attributed to Dezhe Z Jin.

7 recordsLinked to original sources

Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories.

Learning to perform a behavioural procedure as a well-ingrained habit requires extensive repetition of the behavioural sequence, and learning not to perform such behaviours is notoriously difficult. Yet regaining a habit can occur quickly, with even one or a few exposures to cues previously triggering the behaviour. To identify neural mechanisms that might underlie such learning dynamics, we made long-term recordings from multiple neurons in the sensorimotor striatum, a basal ganglia structure implicated in habit formation, in rats successively trained on a reward-based procedural task, given extinction training and then given reacquisition training. The spike activity of striatal output neurons, nodal points in cortico-basal ganglia circuits, changed markedly across multiple dimensions during each of these phases of learning. First, new patterns of task-related ensemble firing successively formed, reversed and then re-emerged. Second, task-irrelevant firing was suppressed, then rebounded, and then was suppressed again. These changing spike activity patterns were highly correlated with changes in behavioural performance. We propose that these changes in task representation in cortico-basal ganglia circuits represent neural equivalents of the explore-exploit behaviour characteristic of habit learning.

Acoustic Stimulation↗

Tilt aftereffect and adaptation-induced changes in orientation tuning in visual cortex.

The tilt aftereffect (TAE) is a visual illusion in which prolonged adaptation to an oriented stimulus causes shifts in subsequent perceived orientations. Historically, neural models of the TAE have explained it as the outcome of response suppression of neurons tuned to the adapting orientation. Recent physiological studies of neurons in primary visual cortex (V1) have confirmed that such response suppression exists. However, it was also found that the preferred orientations of neurons shift away from the adapting orientation. Here we show that adding this second factor to a population coding model of V1 improves the correspondence between neurophysiological data and TAE measurements. According to our model, the shifts in preferred orientation have the opposite effect as response suppression, reducing the magnitude of the TAE.

Adaptation, Physiological↗

The coordinated mapping of visual space and response features in visual cortex.

Whether general principles can explain the layouts of cortical maps remains unresolved. In primary visual cortex of ferret, the relationships between the maps of visual space and response features are predicted by a "dimension-reduction" model. The representation of visual space is anisotropic, with the elevation and azimuth axes having different magnification. This anisotropy is reflected in the orientation, ocular dominance, and spatial frequency domains, which are elongated such that their directions of rapid change, or high-gradient axes, are orthogonal to the high-gradient axis of the visual map. The feature maps are also strongly interdependent-their high-gradient regions avoid one another and intersect orthogonally where essential, so that overlap is minimized. Our results demonstrate a clear influence of the visual map on each feature map. In turn, the local representation of visual space is smooth, as predicted when many features are mapped within a cortical area.

Animals↗

Spiking neural network for recognizing spatiotemporal sequences of spikes.

Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons.

Action Potentials↗

Fast convergence of spike sequences to periodic patterns in recurrent networks.

The dynamical attractors are thought to underlie many biological functions of recurrent neural networks. Here we show that stable periodic spike sequences with precise timings are the attractors of the spiking dynamics of recurrent neural networks with global inhibition. Almost all spike sequences converge within a finite number of transient spikes to these attractors. The convergence is fast, especially when the global inhibition is strong. These results support the possibility that precise spatiotemporal sequences of spikes are useful for information encoding and processing in biological neural networks.

Action Potentials↗

Fast computation with spikes in a recurrent neural network.

Neural networks with recurrent connections are sometimes regarded as too slow at computation to serve as models of the brain. Here we analytically study a counterexample, a network consisting of N integrate-and-fire neurons with self excitation, all-to-all inhibition, instantaneous synaptic coupling, and constant external driving inputs. When the inhibition and/or excitation are large enough, the network performs a winner-take-all computation for all possible external inputs and initial states of the network. The computation is done very quickly: As soon as the winner spikes once, the computation is completed since no other neurons will spike. For some initial states, the winner is the first neuron to spike, and the computation is done at the first spike of the network. In general, there are M potential winners, corresponding to the top M external inputs. When the external inputs are close in magnitude, M tends to be larger. If M>1, the selection of the actual winner is strongly influenced by the initial states. If a special relation between the excitation and inhibition is satisfied, the network always selects the neuron with the maximum external input as the winner.

Journal Article↗

Is the world full of circles?

The statistical arrangement of oriented segments in natural scenes was recently proposed to be indicative of a cocircularity rule. In particular, the probability density function of the relative position of two oriented segments was found to be maximal along fixed angles on the plane, consistent with the two segments being tangent to two points of a circle. Does this observation point to a prevalence of circles in natural scenes? Here we demonstrate that similar statistics can be obtained even when circles are not very common in visual scenes. The reason is that circles or near circular objects can heavily skew the distribution in favor of the cocircularity rule.

Discrimination, Psychological↗