Physical exercise is known to augment brain functioning, improving memory and cognition. However, while some of the physiological effects of physical activity on the brain are known, little is known about its effects ...
详细信息
Physical exercise is known to augment brain functioning, improving memory and cognition. However, while some of the physiological effects of physical activity on the brain are known, little is known about its effects on the neural code. Using calcium imaging in freely behaving mice, we study how voluntary exercise affects the quality and long-term stability of hippocampal place codes. We find that running accelerates the emer-gence of a more informative spatial code in novel environments and increases code stability over days and weeks. Paradoxically, although runners demonstrated an overall more stable place code than their sedentary peers, their place code changed faster when controlling for code quality level. A model-based simulation shows that the combination of improved code quality and faster representational drift in runners, but neither of these effects alone, could account for our results. Thus, exercise may enhance hippocampal function via a more informative and dynamic place code.
How does organized cognition arise from distributed brain activity? Recent analyses of fluid intelligence suggest a core process of cognitive focus and integration, organizing the components of a cognitive operation i...
详细信息
How does organized cognition arise from distributed brain activity? Recent analyses of fluid intelligence suggest a core process of cognitive focus and integration, organizing the components of a cognitive operation into the required computational structure. A cortical 'multiple-demand' (MD) system is closely linked to fluid intelligence, and recent imaging data define nine specific MD patches distributed across frontal, parietal, and occipitotemporal cortex. Wide cortical distribution, relative functional specialization, and strong connectivity suggest a basis for cognitive integration, matching electrophysiological evidence for binding of cognitive operations to their contents. Though still only in broad outline, these data suggest how distributed brain activity can build complex, organized cognition.
In this research, we develop a neuromorphic system to study neural signaling at the level of first order tactile afferents which are slowly adapting type I (SA1) and rapidly adapting type I (RA1) mechanoreceptors. Con...
详细信息
In this research, we develop a neuromorphic system to study neural signaling at the level of first order tactile afferents which are slowly adapting type I (SA1) and rapidly adapting type I (RA1) mechanoreceptors. Considering, the linearized Izhikevich model, two digital circuits are developed for both afferents and are executed on the field programmable gate array (FPGA). After implementation of the digital circuits, we investigate how much information is encoded by this hardware-based neuromorphic system. Indeed, the artificial spiking sequences are evoked by applying different force profiles to the sensor connected to the FPGA. Next, the obtained neural responses are classified based on the two fundamental neural coding for brain information processing: spike timing and rate coding. Considering temporal coding, k-nearest neighbors (kNN), support vector machine (SVM) and Decision Tree algorithms are used for forces recognition using acquired artificial spike patterns. The results of classification show that the digital RA1 is susceptible to signal variations, while the digital SA1, on the other hand, is sensitive to the ramp and hold inputs. Furthermore, these responses are better distinguishable to different stimuli when both artificial SA1 and RA1 afferents are regarded. These results, which are functionally compatible with biological observations, yield the promise for fabrication and development of new tactile sensing modules to be employed in bio-robotic and prosthetic applications. (C) 2019 Elsevier B.V. All rights reserved.
Pattern separation is a basic principle of neuronal coding that precludes memory interference in the hippocampus. Its existence is supported by numerous theoretical, computational, and experimental findings in differe...
详细信息
Pattern separation is a basic principle of neuronal coding that precludes memory interference in the hippocampus. Its existence is supported by numerous theoretical, computational, and experimental findings in different species. However, I argue that recent evidence from single-neuron recordings suggests that pattern separation may not be present in the human hippocampus and that memories are instead coded by the coactivation of invariant and context -independent engrams. This alternative model prompts a reassessment of the definition of episodic memory and its distinction from semantic memory. Furthermore, I propose that a lack of pattern separation in memory coding may have profound implications that could explain cognitive abilities that are uniquely developed in humans, such as our power of generalization and of creative and abstract thinking.
Spike encoding of sound consists in converting a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural network applications, where it is the first and a...
详细信息
Spike encoding of sound consists in converting a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural network applications, where it is the first and a crucial stage of processing. Many spike encoding techniques exist, but there is no systematic approach to quantitatively evaluate their performance. This work proposes the use of three efficiency metrics based on information theory to solve this problem. The first, coding efficiency, measures the fraction of information that the spikes encode on the amplitude of the input signal. The second, computational efficiency, measures the information encoded subject to abstract computational costs imposed on the algorithmic operations of the spike encoding technique. The third, energy efficiency, measures the actual energy expended in the implementation of a spike encoding task. These three efficiency metrics are used to evaluate the performance of four spike encoding techniques for sound on the encoding of a cochleagram representation of speech data. The spike encoding techniques are: Independent Spike coding, Send-on-Delta coding, Ben's Spiker Algorithm, and Leaky Integrate-and-Fire (LIF) coding. The results show that LIF coding has the overall best performance in terms of coding, computational, and energy efficiency.
作者：
Takahashi, HirokazuUniv Tokyo
Grad Sch Informat Sci & Technol Dept Mechanoinformat Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan Univ Tokyo
Res Ctr Adv Sci & Technol Meguro Ku 4-6-1 Komaba Tokyo 1538904 Japan
Place codes of frequency, or tonotopic maps, are commonly found in the auditory pathway, from the cochlea to the auditory cortex, and thus, are believed to play substantial roles in auditory computation. In contrast, ...
详细信息
Place codes of frequency, or tonotopic maps, are commonly found in the auditory pathway, from the cochlea to the auditory cortex, and thus, are believed to play substantial roles in auditory computation. In contrast, in the auditory cortex, tonotopic activation is clearly observed at the onset responses within 50-ms post-stimulus latency but rapidly decays to long-lasting suboptimal stimuli, suggesting that neural representation is made beyond the tonotopic map. We recently demonstrated in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area, suggesting that place coding is an effective strategy to generate diverse response properties within a neural population. We also demonstrated long-lasting sound-induced steady-state local synchrony within the auditory cortex, where neural representation might be made in a different manner from transient tonotopic activation at stimulus onset. These results support the idea of Darwinian computation, where the tonotopic map effectively creates a response variance, while the steady-state synchrony gradually selects the neural population beyond the tonotopic map.
neural coding is a key problem in neuroscience aimed to understand the information processing mechanism in brain. Among the classical theories of neural coding, population rate coding has been studied widely in many w...
详细信息
neural coding is a key problem in neuroscience aimed to understand the information processing mechanism in brain. Among the classical theories of neural coding, population rate coding has been studied widely in many works. In computational studies, neurons are usually classified into excitatory or inhibitory ones. Excitatory neurons have excitatory output synapses, and inhibitory neurons have inhibitory output synapses. However, according to physiological observations, neurons potentially have both types of output synapses. Thus, in this paper, neuronal networks consisting of neurons with mixed excitatory-inhibitory synapses are constructed to investigate the population rate coding fidelity of neuronal systems. It is revealed that, under intermediate values of recurrent probability, inhibitory-excitatory strength ratio, and noise intensity, the performance of population rate coding could be improved by both excitatory synaptic strength and synaptic time constant. It is indicated that external stimuli can be encoded in the form of population firing rate by the studied neuronal networks very well. What is more exciting is that we find the neuronal networks considered in our work have higher coding efficiency than the traditional ones. Therefore, neurons with mixed excitatory-inhibitory synapses may be much more rational.
Accumulating evidence across multiple sensory modalities suggests that the thalamus does not simply relay information from the periphery to the cortex. Here we review recent findings showing that vestibular neurons wi...
详细信息
In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate...
详细信息
In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain. To facilitate the cross-talk between neuromorphic engineering and neuroscience, in this review we first critically examine and summarize emerging recent findings about how population of neurons encode and transmit information. We examine the effects on encoding and readout of information for different features of neural population activity, namely the sparseness of neural representations, the heterogeneity of neural properties, the correlations among neurons, and the timescales (from short to long) at which neurons encode information and maintain it consistently over time. Finally, we critically elaborate on how these facts constrain the design of information coding in neuromorphic circuits. We focus primarily on the implications for designing neuromorphic circuits that communicate with the brain, as in this case it is essential that artificial and biological neurons use compatible neural codes. However, we also discuss implications for the design of neuromorphic systems for implementation or emulation of neural computation.
暂无评论