Neural Computation
Neural Computation - Related Videos
10:32
Enumeration of Neural Stem Cells Using Clonal Assays
0 Views •
Cited by 5 •
2016
Neural stem cells (NSCs) refer to cells which can self-renew and differentiate into the three neural lineages. Here, we describe a protocol to determine NSC frequency in a given cell population using neurosphere formation and differentiation under clonal conditions.
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
0 Views •
Cited by 9 •
2015
In this paper, an experimental framework to perform closed-loop experiments is presented, in which information processing (i.e., coding and decoding) and learning of neuronal assemblies are studied during the continuous interaction with a robotic body.
09:48
Neural Activity Propagation in an Unfolded Hippocampal Preparation with a Penetrating Micro-electrode Array
0 Views •
Cited by 2 •
2015
We have developed an in vitro unfolded hippocampus which preserves CA1-CA3 array of neurons. Combined with the penetrating micro-electrode array, neural activity can be monitored in both the longitudinal and transverse orientations. This method provides advantages over hippocampal slice preparations as the propagation in the entire hippocampus can be recorded simultaneously.
12:48
Isolation and Culture of Neural Crest Cells from Embryonic Murine Neural Tube
0 Views •
Cited by 26 •
2012
Isolation of embryonic neural crest from the neural tube facilitates the use of in vitro methods for studying migration, self-renewal, and multipotency of neural crest.
06:31
Neural-Colony Forming Cell Assay: An Assay To Discriminate Bona Fide Neural Stem Cells from Neural Progenitor Cells
0 Views •
Cited by 36 •
2011
This video protocol demonstrates how to discriminate and enumerate bona fide neural stem cells in a mixed population of neural precursor cells using the neural colony-forming cell assay.
FAQs
Neural outputs depend on how electrical and chemical signals move through connected neurons and how incoming activity is integrated. Synaptic strengths influence the impact of one neuron on another, while connectivity determines which signals can interact. Patterns of activity add a dynamic layer, allowing the same general input pathway to contribute to different perceptions, decisions, or behaviors.
Sensory coding examines how patterns of neural activity represent information from the senses. Researchers analyze which activity patterns correspond to particular inputs and how later neural processing transforms those patterns into information useful for perception. This approach connects signal representation with the biological processes that support interpreting sensory information and guiding subsequent behavior.
Synaptic strengths and connectivity provide structural and functional constraints on how neural activity is transformed. Changes in these features can alter which signals are emphasized, combined, or transmitted through a network. Studying their relationship with activity patterns helps researchers investigate how biological neural systems support learning and memory rather than treating those functions as isolated outputs.
Biological neural computation examines information processing in nervous systems, whereas computer modeling represents selected features of that processing in a mathematical or computational framework. Models can simplify connectivity, signal transformations, or activity patterns while preserving a mechanism of interest. Comparing model behavior with biological observations helps clarify which features may be important for explaining brain function.
Researchers combine neuroscience and biology with mathematics and computer modeling. Biological and neural observations provide information about signals, connectivity, and activity, while mathematical descriptions and models help formalize relationships among those components. This combined workflow supports analysis of sensory coding, learning, memory, and motor control, and helps researchers interpret neural data in a mechanistic context.
This research can link neural information processing with perception, decisions, and behavior, including motor control. It also provides frameworks for interpreting neural data and examining disease-related changes in nervous-system function. In addition, computational principles can guide artificial systems designed to emulate selected features of biological information processing, without requiring those systems to reproduce the entire nervous system.